CN115909768B - Intelligent networking hybrid traffic flow intersection signal collaborative optimization method and system - Google Patents

Abstract

The invention discloses a method and a system for collaborative optimization of signals at an intelligent network-connected mixed traffic flow intersection, wherein the method comprises the steps of collecting traffic state information with high precision; optimizing the intelligent vehicle track based on initial information control; and optimizing based on the optimized signal control of the intelligent vehicle track. Therefore, the defects that the permeability of the intelligent vehicle is low, a large number of intelligent vehicle following manual driving vehicles and only intelligent vehicle single vehicles are controlled, the multi-vehicle following state is not considered, and the optimization effect is limited are avoided under the mixed traffic mode. Meanwhile, by adopting a mode of collaborative optimization of the track and the signal, the operation efficiency of the intersection is further improved, the data source is ensured to be more accurate and microscopic, and the control strategy is more reasonable and effective.

Description

Translated fromChinese

一种智能网联混合交通流交叉口信号协同优化方法及系统A method and system for collaborative optimization of intersection signals for intelligent networked mixed traffic flows

技术领域Technical Field

本发明涉及智能交通控制技术领域，尤其涉及一种智能网联混合交通流交叉口信号协同优化方法及系统。The present invention relates to the field of intelligent traffic control technology, and in particular to a method and system for collaborative optimization of intersection signals of intelligent networked mixed traffic flows.

背景技术Background technique

近年来，我国提出了“双碳目标”，正加快形成绿色低碳交通运输方式，推广新能源、智能化、数字化交通装备，鼓励引导绿色出行，让交通更加环保、出行更加低碳。面对诸多挑战，智能网联汽车融合了智能化、网联化的优点，为实现交通出行的节能减排、提升交通效率提供了前所未有的契机，在智能交通自动驾驶技术领域取得了许多突破性成果，可显著缓解我国所面临的能源与环境危机，并在一定程度上有效缓解日益严重的交通拥堵和道路安全问题。技术的突破让越来越多的原型智能车能够走出实验室，在真实道路环境中进行测试，逐渐向实际应用迈进。城市交通拥堵和交通事故等问题日益严重，交叉口作为城市交通网络的节点，直接影响着路网交通运行与控制效果，解决交叉口的交通问题对解决整个城市交通问题具有重大意义。In recent years, my country has proposed the "dual carbon goal" and is accelerating the formation of green and low-carbon transportation modes, promoting new energy, intelligent and digital transportation equipment, encouraging and guiding green travel, making transportation more environmentally friendly and low-carbon. Faced with many challenges, intelligent networked vehicles integrate the advantages of intelligence and networking, providing an unprecedented opportunity to achieve energy conservation and emission reduction in transportation and improve transportation efficiency. Many breakthroughs have been made in the field of intelligent transportation and autonomous driving technology, which can significantly alleviate the energy and environmental crisis faced by my country, and to a certain extent effectively alleviate the increasingly serious traffic congestion and road safety problems. Technological breakthroughs have enabled more and more prototype smart cars to walk out of the laboratory, be tested in real road environments, and gradually move towards practical applications. Urban traffic congestion and traffic accidents are becoming increasingly serious. As nodes in the urban transportation network, intersections directly affect the operation and control of road network traffic. Solving the traffic problems at intersections is of great significance to solving the entire urban traffic problem.

随着通信技术、传感和计算机技术的发展，智能网联技术成为解决交通问题的关键技术。尽管到目前为止智能网联车已经取得了巨大的进步，但要达到完全自动化以及较高的智能网联车市场渗透率还需要相对较长的时间，在智能网联车完全取代人类驾驶车辆之前，道路将在很长一段时间内存在智能网联车和人类驾驶车辆混合交通流。在智能网联环境下，智能网联车和人类驾驶车辆具有更快的信息探知能力和更小的反应时间，路端感知也可将检测到的交叉口范围内的道路、车辆运行状况实时传输到车端。如何在交叉口控制问题中发挥智能网联系统的技术优势，实现安全、有效、科学的控制是目前交通控制领域研究的重要方向。With the development of communication technology, sensor and computer technology, intelligent network technology has become a key technology for solving traffic problems. Although intelligent network vehicles have made great progress so far, it will take a relatively long time to achieve full automation and a high market penetration rate of intelligent network vehicles. Before intelligent network vehicles completely replace human-driven vehicles, roads will have mixed traffic flows of intelligent network vehicles and human-driven vehicles for a long time. In an intelligent network environment, intelligent network vehicles and human-driven vehicles have faster information detection capabilities and shorter reaction times. Road-side perception can also transmit the detected road and vehicle operating conditions within the intersection range to the vehicle side in real time. How to give full play to the technical advantages of intelligent network systems in intersection control problems and achieve safe, effective and scientific control is an important research direction in the field of traffic control.

然而，现有的基于混合通行条件下智能车轨迹优化研究中，智能车的渗透率往往较低，在人机混驾的交通模式中检测器难以获得研究范围内所有车辆的动态运行状况，制约了智能车的轨迹规划；现有研究为单边优化，少有轨迹与信号协同优化。并且，较少使用多车跟驰模型进行速度控制，往往仅考虑单车。针对上述这种情况，本发明提出了一种智能网联混合交通流交叉口信号协同优化方法及系统，能够有效地对现有技术进行改进，克服其不足。However, in the existing research on smart car trajectory optimization under mixed traffic conditions, the penetration rate of smart cars is often low. In the traffic mode of mixed driving of humans and machines, it is difficult for detectors to obtain the dynamic operating conditions of all vehicles within the research range, which restricts the trajectory planning of smart cars. Existing research is unilateral optimization, and there is little coordinated optimization of trajectory and signal. In addition, the multi-vehicle following model is rarely used for speed control, and often only a single vehicle is considered. In view of the above situation, the present invention proposes a method and system for coordinated optimization of intersection signals of intelligent networked mixed traffic flow, which can effectively improve the existing technology and overcome its shortcomings.

发明内容Summary of the invention

针对现有技术的不足，本发明提供了一种智能网联混合交通流交叉口信号协同优化方法及系统，以解决现有技术存在的以上问题，其具体方案如下：In view of the shortcomings of the prior art, the present invention provides a method and system for collaborative optimization of intersection signals of intelligent networked mixed traffic flow to solve the above problems existing in the prior art. The specific scheme is as follows:

第一方面，本发明提供了一种智能网联混合交通流交叉口信号协同优化方法，所述方法包括：In a first aspect, the present invention provides a method for collaborative optimization of intersection signals of intelligent networked mixed traffic flows, the method comprising:

步骤1：对交通状态信息进行高精度采集，包括目标车辆运行状态数据与初始信控方案；Step 1: Collect traffic status information with high precision, including target vehicle operation status data and initial signal control plan;

步骤2：基于初始信息控制的智能车轨迹进行优化，包括交叉口分区界定、智能车队编组、速度引导区边界计算、跟驰模型建立、车速引导策略应用；Step 2: Optimize the trajectory of the intelligent vehicle based on the initial information control, including intersection partitioning, intelligent vehicle fleet formation, speed guidance zone boundary calculation, car-following model establishment, and vehicle speed guidance strategy application;

步骤3：基于优化后的所述智能车轨迹的信号控制进行优化，包括标准小汽车当量参数优化，道路通行能力占用系数确定，信控方案确定。Step 3: Optimize the signal control based on the optimized trajectory of the smart car, including optimizing the standard car equivalent parameters, determining the road capacity occupancy coefficient, and determining the signal control scheme.

优选地，所述步骤2包括：Preferably, the step 2 comprises:

S21.交叉口分区界定：对采集器检测范围L进行分区，主要由车队决策区L_D、速度引导区L_V所组成；车队编组区主要功能为将智能网联车辆与人工驾驶车辆进行编组；速度引导区主要功能为应用速度控制策略实现智能车队的实时控制，为保证交通安全，L_V长度需小于进口道实线区域L_limit；其组成与约束如下：S21. Intersection zoning: The detection range L of the collector is divided into two zones, which are mainly composed of the fleet decision zone_LD and the speed guidance zone_LV . The main function of the fleet marshaling zone is to marshall intelligent networked vehicles and manually driven vehicles. The main function of the speed guidance zone is to apply speed control strategies to achieve real-time control of the intelligent fleet. To ensure traffic safety, the length of_LV must be less than the solid line area of the entrance road L_limit . Its composition and constraints are as follows:

L＝L_D+L_VL＝_LD +_LV

L_V＜L_limit；_LV ＜_Llimit ;

S22.智能车队编组：由于人机混驾的环境，智能网联车辆按照一定渗透率存在于交叉口内；在车队决策区L_D内将智能网联车辆(CAV)作为所编组的车队的头车(其中m为头车所在车队编号，l为头车所在车道编号)；其后跟驰的若干人工驾驶车辆(其中m为所在车队编号，j为车队中人工驾驶车辆序号)与头车共同组成智能网联车队M_n；S22. Intelligent convoy formation: Due to the mixed driving environment of humans and machines, intelligent connected vehicles exist in the intersection at a certain penetration rate; in the convoy decision area_LD , the intelligent connected vehicle (CAV) is used as the head vehicle of the convoy. (where m is the fleet number of the lead vehicle and l is the lane number of the lead vehicle); several manually driven vehicles following behind (where m is the fleet number and j is the number of the manually driven vehicle in the fleet) and the lead vehicle Together they form an intelligent connected fleet M_n ;

S23.速度引导区边界计算：不同于检测器的探测边界，确定车辆被集中控制器开始纳入速度控制策略的影响范围；其判断条件为驾驶员能够接受的加速度极值a_c，并根据加速度极值a_c与道路限制速度V_L计算舒适制动距离S_c；其计算公式如下：S23. Speed guidance zone boundary calculation: Different from the detection boundary of the detector, it determines the vehicle being included in the influence range of the speed control strategy by the centralized controller; its judgment condition is the acceleration extreme value a_c that the driver can accept, and the comfortable braking distance_Sc is calculated based on the acceleration extreme value a_c and the road speed limit V_L ; its calculation formula is as follows:

根据计算得出的舒适制动距离S_c，以交叉口停止线为基准确定速度引导区L_V边界；若S_c＞S_limit，取边界距离为S_limit；According to the calculated comfortable braking distance_Sc , the boundary of the speed guidance area_LV is determined with the intersection stop line as the reference; if_Sc >_Slimit , the boundary distance is taken as_Slimit ;

S24.跟驰模型建立：由于车队中存在部分人工驾驶车辆，智能网联车队M_n头车的驾驶特征将诱导跟驰人工驾驶车辆以相同目标速度通过交叉口或停止；由于基于雷视一体机的交通采集系统能够得到包括人工驾驶车辆在内的所有车辆的运行状态信息，可应用基于车队的改进FVD(全速度差)跟驰模型，得到后一车队头车与前一车队最后车辆间的跟驰加速度，计算公式如下：S24. Establishment of the following model: Since there are some manually driven vehicles in the convoy, the driving characteristics of the leading vehicle in the intelligent networked convoy M_n will induce the following manually driven vehicles to pass through the intersection at the same target speed or stop; since the traffic collection system based on the integrated radar and vision device can obtain the operating status information of all vehicles including manually driven vehicles, the improved FVD (full velocity difference) following model based on the convoy can be applied to obtain the following convoy leading vehicle The last vehicle in the previous convoy The following acceleration is calculated as follows:

a_m+1＝α(V′_m-V_m(t))+β(V_m+1(t)-V_m(t))a_m+1 = α(V′_m - V_m (t)) + β(V_m+1 (t) -_{V m} (t))

式中：a_m+1——m+1车队中头车跟驰加速度；Where: a_m+1 —— the following acceleration of the leading vehicle in the m+1 convoy;

V′_m——m车队的头车目标车速；V′_m ——the target speed of the leading vehicle in the m-vehicle convoy;

V_m(t)——t时刻m车队尾车速度；V_m (t)——the speed of the last vehicle in the m-carriage at time t;

V_m+1(t)——t时刻m+1车队头车速度；V_m+1 (t)——the speed of the leading vehicle in the m+1 convoy at time t;

α——驾驶员敏感系数；β——驾驶员反应系数；α——driver sensitivity coefficient; β——driver response coefficient;

S25.车速引导策略应用：智能网联车队M_n到达速度引导区L_V边界时，进行相位判断，此行进方向信号灯为红黄灯相位P_r，其剩余时间设为T_r；或绿灯相位P_g，其剩余时间设为T_g；并针对不同的相位状态采用不同的车速引导策略，设置t_r为驾驶员反应时间；S25. Application of vehicle speed guidance strategy: When the intelligent networked vehicle fleet_Mn reaches the boundary of the speed guidance zone_LV , a phase judgment is performed. The travel direction signal light is the red-yellow light phase_Pr , and its remaining time is set to_Tr ; or the green light phase_Pg , and its remaining time is set to_Tg ; and different vehicle speed guidance strategies are adopted for different phase states, and_tr is set as the driver's reaction time;

其中，集中控制器仅在无下游排队车辆或排队车辆无干扰的情况下进行轨迹优化：The centralized controller performs trajectory optimization only when there are no downstream queued vehicles or when there is no interference from the queued vehicles:

1)当或时，取目标车速V′_m＝V_max；后车队的跟驰加速度为：a_m+1＝α(V_max-V_m(t))+β(V_m+1(t)-V_m(t))；1) When or When , the target vehicle speed V′_m = V_max ; the following acceleration of the rear vehicle group is: a_m+1 = α(V_max - V_m (t)) + β(V_m+1 (t) - V_m (t));

2)当时，应降低车速V_m至目标车速V′_m，使车队到达停止线时刻在绿灯启亮时刻，V′_m的求解方式为：2) When When the vehicle speed V m is reached, the vehicle speed V_m should be reduced to the target vehicle speed V ′_m so that the convoy reaches the stop line at the time when the green light turns on. The solution of V ′_m is:

式中：V_min——道路最低限速；V_max——道路最高限速。In the formula: V_min —— minimum speed limit of the road; V_max —— maximum speed limit of the road.

优选地，所述步骤3包括：Preferably, the step 3 comprises:

S31.标准小汽车当量参数优化：基于雷视一体机对于车辆运行状态获取的准确性，能够利用交叉口内运行车辆的车辆类型、车辆大小、占用空间、运行速度对小汽车当量换算系数进行改进：S31. Optimization of standard car equivalent parameters: Based on the accuracy of the vehicle running status acquired by the integrated radar vision device, the car equivalent conversion coefficient can be improved by using the vehicle type, vehicle size, occupied space, and running speed of the vehicles running in the intersection:

其中：为换算后k车道总交通量，G_s为所有小汽车，G_r为所有中型车，G_l为所有大型车，G_b为所有公交车，其计数均由雷视一体机采集系统直接给出；in: is the total traffic volume of lane k after conversion,_Gs is all cars,_Gr is all medium-sized cars,_Gl is all large cars, and_Gb is all buses, and their counts are directly given by the radar-visual integrated machine acquisition system;

λ_s、λ_r、λ_l、λ_b分别为小汽车、中型车、大型车、公交车的空间占用系数，其反应各类车辆对道路面积的占用度；并根据交通流率情况确定公交优先度，即λ_b取值；λ_s , λ_r , λ_l , λ_b are the space occupancy coefficients of cars, medium-sized cars, large cars, and buses, respectively, which reflect the occupancy of road area by various types of vehicles; and the priority of buses, i.e. the value of λ_b , is determined according to the traffic flow rate;

δ_s、δ_r、δ_l、δ_b分别为小汽车、中型车、大型车、公交车的道路通行能力占用系数；δ_s , δ_r , δ_l , δ_b are the road capacity occupancy coefficients of cars, medium-sized cars, large cars, and buses, respectively;

S32.道路通行能力占用系数确定：根据该车道平均速度与目标车辆平均车速归一化，由于越接近车道平均速度，车辆对通行能力影响越小；基于平均速度值，越接近限速值占用系数越低，越接近0km/h其占用系数越高；为避免此系数扰动过大，对其取值作约束；因此有以下系数计算公式：S32. Determination of road capacity occupancy coefficient: based on the average speed of the lane Normalized with the average speed of the target vehicle, the closer to the average speed of the lane, the smaller the impact of the vehicle on the traffic capacity; based on the average speed value, the closer to the speed limit, the lower the occupancy coefficient, and the closer to 0km/h, the higher the occupancy coefficient; to avoid excessive disturbance of this coefficient, its value is constrained; therefore, the following coefficient calculation formula is available:

S33.信控方案确定：根据步骤S32中的计算公式，可以得出精确的各关键车道的交通流量q_i，引用经典的英国TRRL方法进行信号配时计算，生成相位方案；S33. Determine the signal control scheme: According to the calculation formula in step S32, the accurate traffic flow q_i of each key lane can be obtained, and the classic British TRRL method is used to calculate the signal timing and generate a phase scheme;

其中，在一个周期中，集中控制器将实时读取轨迹优化后交叉口所有车辆的运行状态，利用基于改进标准小汽车当量的webster法进行交叉口信号控制。Among them, in one cycle, the centralized controller will read the operating status of all vehicles at the intersection after trajectory optimization in real time, and use the Webster method based on the improved standard car equivalent to control the intersection signal.

优选地，所述步骤S33包括：Preferably, the step S33 includes:

S41.关键车道流量比总和计算：根据步骤3.2中给出的改进下各车道交通量进行流量比总和计算，其计算方法如下：S41. Calculation of the total traffic ratio of key lanes: Calculate the total traffic ratio of each lane according to the improved traffic volume given in step 3.2. The calculation method is as follows:

其中i为相位的索引，需根据交叉口实际运行情况具体设定；为i相位中k车道的流量比，其计算方法如下：Where i is the index of the phase, which needs to be set according to the actual operation of the intersection; is the flow ratio of lane k in phase i, and its calculation method is as follows:

其中为i相位中k车道的饱和流率，其具体数据根据相关交通规范标准进行确定即可；in is the saturation flow rate of lane k in phase i, and its specific data can be determined according to relevant traffic regulations and standards;

S42.最优周期时长：TRRL方法信号控制的周期时长应桉如下公式计算：S42. Optimal cycle duration: The cycle duration of the TRRL method signal control should be calculated as follows:

40s≤C₀≤180s40s≤C₀ ≤180s

其中L定义为信号总损失时间，其计算公式如下：Where L is defined as the total signal loss time, which is calculated as follows:

其中L_s定义为启动损失时间；AR为i相位的全红交叉口清空时间；Where_Ls is defined as the start-up loss time; AR is the all-red intersection clearing time of phase i;

S43.有效绿灯时间计算：根据各相位准确的流量比分配最优周期时长C₀，其计算方法如下：S43. Calculation of effective green light time: Allocate the optimal cycle time C₀ according to the accurate flow ratio of each phase. The calculation method is as follows:

由有效绿灯时间生成相位组合G＝[g₁，g₂，g₃，...]，用于进行下一周期的交叉口信号控制；下周期开始时，回到步骤2继续进行基于步骤3给出信控方案下的智能车轨迹优化。The effective green light time generates a phase combination G = [g₁ , g₂ , g₃ , ...] for the next cycle of intersection signal control; when the next cycle starts, return to step 2 to continue the intelligent vehicle trajectory optimization based on the signal control solution given in step 3.

第二方面，本发明提供了一种智能网联混合交通流交叉口信号协同优化系统，所述系统包括：In a second aspect, the present invention provides an intelligent networked mixed traffic flow intersection signal collaborative optimization system, the system comprising:

获取模块，用于对交通状态信息进行高精度采集，包括目标车辆运行状态数据与初始信控方案；The acquisition module is used to collect traffic status information with high precision, including target vehicle operation status data and initial signal control scheme;

处理模块，用于基于初始信息控制的智能车轨迹进行优化，包括交叉口分区界定、智能车队编组、速度引导区边界计算、跟驰模型建立、车速引导策略应用；The processing module is used to optimize the trajectory of the intelligent vehicle based on the initial information control, including intersection partitioning, intelligent vehicle fleet formation, speed guidance zone boundary calculation, car-following model establishment, and vehicle speed guidance strategy application;

优化模块，用于基于优化后的所述智能车轨迹的信号控制进行优化，包括标准小汽车当量参数优化，道路通行能力占用系数确定，信控方案确定。The optimization module is used to optimize the signal control based on the optimized trajectory of the smart car, including the optimization of standard car equivalent parameters, the determination of road capacity occupancy coefficient, and the determination of signal control scheme.

第三方面，本发明提供了一种智能网联混合交通流交叉口信号协同优化设备，所述设备包括：In a third aspect, the present invention provides an intelligent networked mixed traffic flow intersection signal collaborative optimization device, the device comprising:

通信总线，用于实现处理器与存储器间的连接通信；A communication bus, used to realize the connection and communication between the processor and the memory;

存储器，用于存储计算机程序；Memory for storing computer programs;

处理器，用于执行所述计算机程序以实现如下步骤：A processor is used to execute the computer program to implement the following steps:

对交通状态信息进行高精度采集，包括目标车辆运行状态数据与初始信控方案；High-precision collection of traffic status information, including target vehicle operating status data and initial signal control plan;

基于初始信息控制的智能车轨迹进行优化，包括交叉口分区界定、智能车队编组、速度引导区边界计算、跟驰模型建立、车速引导策略应用；Optimize the trajectory of the intelligent vehicle based on the initial information control, including intersection zoning, intelligent vehicle fleet formation, speed guidance zone boundary calculation, car-following model establishment, and vehicle speed guidance strategy application;

基于优化后的所述智能车轨迹的信号控制进行优化，包括标准小汽车当量参数优化，道路通行能力占用系数确定，信控方案确定。The signal control based on the optimized trajectory of the smart car is optimized, including the optimization of standard car equivalent parameters, the determination of road capacity occupancy coefficient, and the determination of the signal control scheme.

第四方面，本发明提供了一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现如第一方面所述的方法。In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the first aspect.

有益效果：本发明的智能网联混合交通流交叉口信号协同优化方法及系统，通过对交通状态信息进行高精度采集，包括目标车辆运行状态数据与初始信控方案；基于初始信息控制的智能车轨迹进行优化，包括交叉口分区界定、智能车队编组、速度引导区边界计算、跟驰模型建立、车速引导策略应用；基于优化后的所述智能车轨迹的信号控制进行优化，包括标准小汽车当量参数优化，道路通行能力占用系数确定，信控方案确定。这样，通过采用车辆编组的方式，将智能车与人工车辆编组，使用车队进行跟驰；并利用了雷视一体机的采集特性，车队领头智能车能够获得前车队车尾人工驾驶车辆的速度，从而进行跟驰，解决了混合交通模式下，智能车渗透率较低，存在大量的人工智能驾驶跟驰智能车辆；且仅仅是控制智能车单车，未考虑多车跟驰状态，优化作用将有限的缺点。同时，通过采用了轨迹与信号协同优化的方式，避免了现有仅采用单边优化的混合交通情况导致的往往仅针对车辆运行状态进行信号优化，或者在固定配时方案下进行轨迹优化与速度控制的弊端，进一步提升交叉口的运行效率，保障了数据来源更精确、微观，并且控制策略更合理、有效。Beneficial effects: The intelligent networked mixed traffic flow intersection signal collaborative optimization method and system of the present invention collects traffic status information with high precision, including target vehicle operation status data and initial signal control scheme; optimizes the trajectory of the intelligent vehicle controlled by the initial information, including intersection partitioning, intelligent vehicle fleet formation, speed guidance zone boundary calculation, following model establishment, and vehicle speed guidance strategy application; optimizes the signal control based on the optimized trajectory of the intelligent vehicle, including standard car equivalent parameter optimization, road capacity occupancy coefficient determination, and signal control scheme determination. In this way, by adopting the vehicle formation method, the intelligent vehicle and the manual vehicle are formed, and the vehicle fleet is used for following; and by utilizing the acquisition characteristics of the integrated radar vision machine, the leading intelligent vehicle of the vehicle fleet can obtain the speed of the manual vehicle at the rear of the vehicle fleet, so as to follow, which solves the problem that the penetration rate of intelligent vehicles is low in the mixed traffic mode, and there are a large number of artificial intelligence driving and following intelligent vehicles; and it only controls the intelligent vehicle alone, without considering the following state of multiple vehicles, and the optimization effect will be limited. At the same time, by adopting the method of coordinated optimization of trajectory and signal, the disadvantages of the existing mixed traffic conditions that only use unilateral optimization, which often only optimizes signals according to the vehicle operation status, or optimizes trajectory and speed under fixed timing schemes, are avoided. This further improves the operation efficiency of intersections, ensures that the data source is more accurate and microscopic, and the control strategy is more reasonable and effective.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，附图中的实施例不构成对本发明的任何限制，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and the embodiments in the drawings do not constitute any limitation to the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

图1是本发明智能网联混合交通流交叉口信号协同优化方法一实施例流程示意图。FIG1 is a flow chart of an embodiment of a method for collaboratively optimizing intersection signals of intelligent networked mixed traffic flows according to the present invention.

图2是本发明智能网联混合交通流交叉口信号协同优化系统一实施例结构示意图。FIG2 is a schematic diagram of the structure of an embodiment of an intelligent networked mixed traffic flow intersection signal collaborative optimization system of the present invention.

图3是本发明智能网联混合交通流交叉口信号协同优化设备一实施例结构示意图。FIG3 is a schematic diagram of the structure of an embodiment of an intelligent networked mixed traffic flow intersection signal collaborative optimization device according to the present invention.

具体实施方式Detailed ways

下面结合附图与实施例对本发明技术方案作进一步详细的说明，这是本发明的较佳实施例。应当理解，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例；需要说明的是，在不冲突的情况下，本发明中的实施例及实施例中的特征可以相互组合。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments, which are preferred embodiments of the present invention. It should be understood that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments; it should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

本发明实施例技术方案的主要思想：对交通状态信息进行高精度采集，包括目标车辆运行状态数据与初始信控方案；基于初始信息控制的智能车轨迹进行优化，包括交叉口分区界定、智能车队编组、速度引导区边界计算、跟驰模型建立、车速引导策略应用；基于优化后的所述智能车轨迹的信号控制进行优化，包括标准小汽车当量参数优化，道路通行能力占用系数确定，信控方案确定。The main idea of the technical solution of the embodiment of the present invention is: to collect traffic status information with high precision, including target vehicle operating status data and initial signal control scheme; to optimize the smart car trajectory based on the initial information control, including intersection zoning, smart vehicle fleet formation, speed guidance area boundary calculation, following model establishment, and vehicle speed guidance strategy application; to optimize the signal control based on the optimized smart car trajectory, including standard car equivalent parameter optimization, road capacity occupancy coefficient determination, and signal control scheme determination.

为了更好的理解上述的技术方案，下面将结合说明书附图以及具体的实施方式对上述技术方案进行详细的说明。In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

实施例一Embodiment 1

本发明一实施例提供了一种智能网联混合交通流交叉口信号协同优化方法，如图1所示，该数据处理方法具体可以包括如下步骤：An embodiment of the present invention provides a method for coordinated optimization of intersection signals of intelligent networked mixed traffic flows. As shown in FIG1 , the data processing method may specifically include the following steps:

步骤S101，对交通状态信息进行高精度采集，包括目标车辆运行状态数据与初始信控方案；Step S101, high-precision collection of traffic status information, including target vehicle operation status data and initial signal control scheme;

在本发明申请实施例中，目标车辆运行状态数据具体可以包括：目标类型C_s、目标纵向速度V_p、目标纵向加速度V_ap、目标所在车道号码L_i、目标距停车线距离Y_i；初始信控方案采集包括各相位的绿灯时长、黄灯时长、全红时间、相序方案。数据的采集通过建立于交叉口的雷视一体机目标检测系统进行，实时采集得到的信息同时输入至轨迹优化与信控方案优化方法中。In the embodiment of the present invention, the target vehicle running state data may specifically include: target type_Cs , target longitudinal speed_Vp , target longitudinal acceleration_Vap , target lane number_Li , target distance from the stop line_Yi ; the initial signal control scheme collection includes the green light duration, yellow light duration, full red time, and phase sequence scheme of each phase. The data is collected through the radar-visual integrated target detection system established at the intersection, and the information collected in real time is simultaneously input into the trajectory optimization and signal control scheme optimization methods.

步骤S102，基于初始信息控制的智能车轨迹进行优化，包括交叉口分区界定、智能车队编组、速度引导区边界计算、跟驰模型建立、车速引导策略应用；Step S102, optimizing the trajectory of the smart car controlled based on the initial information, including intersection zoning, smart car team formation, speed guidance zone boundary calculation, car-following model establishment, and vehicle speed guidance strategy application;

具体地，是在周期开始时，采用集中控制器，读取交叉口全部车辆运行状态，同时对交叉口控制范围内所有的智能网联车辆进行速度控制。Specifically, at the beginning of the cycle, a centralized controller is used to read the operating status of all vehicles at the intersection, and at the same time, the speed of all intelligent connected vehicles within the control range of the intersection is controlled.

在本发明申请实施例中，交叉口分区界定具体可以包括：对采集器检测范围L进行分区，主要由车队决策区L_D、速度引导区L_V所组成；车队编组区主要功能为将智能网联车辆与人工驾驶车辆进行编组；速度引导区主要功能为应用速度控制策略实现智能车队的实时控制，为保证交通安全，L_V长度需小于进口道实线区域L_limit；其组成与约束如下：In the embodiment of the present invention, the intersection zoning definition may specifically include: zoning the collector detection range L, which is mainly composed of the fleet decision area_LD and the speed guidance area_LV ; the fleet marshaling area mainly functions to marshall intelligent networked vehicles and manually driven vehicles; the speed guidance area mainly functions to apply the speed control strategy to achieve real-time control of the intelligent fleet. To ensure traffic safety, the length of_LV must be less than the solid line area L_{limit of} the entrance road; its composition and constraints are as follows:

L＝L_D+L_VL＝_LD +_LV

L_V＜L_limit；_LV ＜_Llimit ;

在本发明申请实施例中，智能车队编组具体可以包括：由于人机混驾的环境，智能网联车辆按照一定渗透率存在于交叉口内；在车队决策区L_D内将智能网联车辆(CAV)作为所编组的车队的头车(其中m为头车所在车队编号，l为头车所在车道编号)；其后跟驰的若干人工驾驶车辆(其中m为所在车队编号，j为车队中人工驾驶车辆序号)与头车共同组成智能网联车队M_n；In the embodiment of the present invention, the intelligent vehicle fleet formation may specifically include: due to the mixed driving environment of humans and machines, intelligent networked vehicles exist in the intersection at a certain penetration rate; in the fleet decision area_LD , the intelligent networked vehicle (CAV) is used as the head vehicle of the fleet. (where m is the fleet number of the lead vehicle and l is the lane number of the lead vehicle); several manually driven vehicles following behind (where m is the fleet number and j is the number of the manually driven vehicle in the fleet) and the lead vehicle Together they form an intelligent connected fleet M_n ;

在本发明申请实施例中，速度引导区边界计算具体可以包括：不同于检测器的探测边界，确定车辆被集中控制器开始纳入速度控制策略的影响范围；其判断条件为驾驶员能够接受的加速度极值a_c，并根据加速度极值a_c与道路限制速度V_L计算舒适制动距离S_c；其计算公式如下：In the embodiment of the present invention, the speed guidance zone boundary calculation may specifically include: different from the detection boundary of the detector, determining the influence range of the vehicle being included in the speed control strategy by the centralized controller; its judgment condition is the acceleration extreme value a_c that the driver can accept, and calculating the comfortable braking distance_Sc according to the acceleration extreme value a_c and the road speed limit V_L ; its calculation formula is as follows:

根据计算得出的舒适制动距离S_c，以交叉口停止线为基准确定速度引导区L_V边界；若S_c＞S_limit，取边界距离为S_limit；According to the calculated comfortable braking distance_Sc , the boundary of the speed guidance area_LV is determined with the intersection stop line as the reference; if_Sc >_Slimit , the boundary distance is taken as_Slimit ;

在本发明申请实施例中，跟驰模型建立具体可以包括：由于车队中存在部分人工驾驶车辆，智能网联车队M_n头车的驾驶特征将诱导跟驰人工驾驶车辆以相同目标速度通过交叉口或停止；由于基于雷视一体机的交通采集系统能够得到包括人工驾驶车辆在内的所有车辆的运行状态信息，可应用基于车队的改进FVD(全速度差)跟驰模型，得到后一车队头车与前一车队最后车辆间的跟驰加速度，计算公式如下：In the embodiment of the present invention, the establishment of the following model may specifically include: since there are some manually driven vehicles in the convoy, the driving characteristics of the leading vehicle of the intelligent networked convoy M_n will induce the following manually driven vehicle to pass through the intersection or stop at the same target speed; since the traffic collection system based on the radar-visual integrated device can obtain the operating status information of all vehicles including manually driven vehicles, the improved FVD (full speed difference) following model based on the convoy can be applied to obtain the following convoy leading vehicle The last vehicle in the previous convoy The following acceleration is calculated as follows:

a_m+1＝α(V′_m-V_m(t))+β(V_m+1(t)-V_m(t))a_m+1 = α(V′_m - V_m (t)) + β(V_m+1 (t) -_{V m} (t))

式中：a_m+1－－m+1车队中头车跟驰加速度；Where: a_m+1 - the following acceleration of the leading vehicle in the m+1 convoy;

V′_m——m车队的头车目标车速；V′_m ——the target speed of the leading vehicle in the m-vehicle convoy;

V_m(t)——t时刻m车队尾车速度；V_m (t)——the speed of the last vehicle in the m-carriage at time t;

V_m+1(t)——t时刻m+1车队头车速度；V_m+1 (t)——the speed of the leading vehicle in the m+1 convoy at time t;

α——驾驶员敏感系数；β——驾驶员反应系数；α——driver sensitivity coefficient; β——driver response coefficient;

在本发明申请实施例中，车速引导策略应用具体可以包括：智能网联车队M_n到达速度引导区L_V边界时，进行相位判断，此行进方向信号灯为红黄灯相位P_r，其剩余时间设为T_r；或绿灯相位P_g，其剩余时间设为T_g；并针对不同的相位状态采用不同的车速引导策略，设置t_r为驾驶员反应时间；In the embodiment of the present invention, the application of the vehicle speed guidance strategy may specifically include: when the intelligent networked vehicle fleet_Mn reaches the boundary of the speed guidance area_LV , a phase judgment is performed, and the travel direction signal light is the red-yellow light phase_Pr , and its remaining time is set to_Tr ; or the green light phase_Pg , and its remaining time is set to_Tg ; and different vehicle speed guidance strategies are adopted for different phase states, and_tr is set as the driver's reaction time;

其中，集中控制器仅在无下游排队车辆或排队车辆无干扰的情况下进行轨迹优化：The centralized controller performs trajectory optimization only when there are no downstream queued vehicles or when there is no interference from the queued vehicles:

1)当或时，取目标车速V′_m＝V_max；后车队的跟驰加速度为：a_m+1＝α(V_max-V_m(t))+β(V_m+1(t)-V_m(t))；1) When or When , the target vehicle speed V′_m = V_max ; the following acceleration of the rear vehicle group is: a_m+1 = α(V_max - V_m (t)) + β(V_m+1 (t) - V_m (t));

2)当时，应降低车速V_m至目标车速V′_m，使车队到达停止线时刻在绿灯启亮时刻，V′_m的求解方式为：2) When When the vehicle speed V m is reached, the vehicle speed V_m should be reduced to the target vehicle speed V ′_m so that the convoy reaches the stop line at the time when the green light turns on. The solution of V ′_m is:

式中：V_min——道路最低限速；V_max——道路最高限速。In the formula: V_min —— minimum speed limit of the road; V_max —— maximum speed limit of the road.

步骤S103，基于优化后的所述智能车轨迹的信号控制进行优化，包括标准小汽车当量参数优化，道路通行能力占用系数确定，信控方案确定。Step S103, optimizing the signal control based on the optimized smart car trajectory, including optimizing the standard car equivalent parameters, determining the road capacity occupancy coefficient, and determining the signal control scheme.

具体地，在一个周期中，集中控制器将实时读取轨迹优化后交叉口所有车辆的运行状态，利用基于改进标准小汽车当量的webster法进行交叉口信号控制。Specifically, in one cycle, the centralized controller will read the operating status of all vehicles at the intersection after trajectory optimization in real time, and use the Webster method based on the improved standard car equivalent to control the intersection signal.

在本发明申请实施例中，标准小汽车当量参数优化具体可以包括：基于雷视一体机对于车辆运行状态获取的准确性，能够利用交叉口内运行车辆的车辆类型、车辆大小、占用空间、运行速度对小汽车当量换算系数进行改进：In the embodiment of the present invention, the optimization of the standard car equivalent parameters may specifically include: based on the accuracy of the radar-visual integrated device in obtaining the vehicle running status, the car equivalent conversion coefficient can be improved by using the vehicle type, vehicle size, occupied space, and running speed of the vehicles running in the intersection:

其中：为换算后k车道总交通量，G_s为所有小汽车，G_r为所有中型车，G_l为所有大型车，G_b为所有公交车，其计数均由雷视一体机采集系统直接给出；in: is the total traffic volume of lane k after conversion,_Gs is all cars,_Gr is all medium-sized cars,_Gl is all large cars, and_Gb is all buses, and their counts are directly given by the radar-visual integrated machine acquisition system;

λ_s、λ_r、λ_l、λ_b分别为小汽车、中型车、大型车、公交车的空间占用系数，其反应各类车辆对道路面积的占用度；并根据交通流率情况确定公交优先度，即λ_b取值；λ_s , λ_r , λ_l , λ_b are the space occupancy coefficients of cars, medium-sized cars, large cars, and buses, respectively, which reflect the occupancy of road area by various types of vehicles; and the priority of buses, i.e. the value of λ_b , is determined according to the traffic flow rate;

δ_s、δ_r、δ_l、δ_b分别为小汽车、中型车、大型车、公交车的道路通行能力占用系数；δ_s , δ_r , δ_l , δ_b are the road capacity occupancy coefficients of cars, medium-sized cars, large cars, and buses, respectively;

在本发明申请实施例中，道路通行能力占用系数确定具体可以包括：根据该车道平均速度与目标车辆平均车速归一化，由于越接近车道平均速度，车辆对通行能力影响越小；基于平均速度值，越接近限速值占用系数越低，越接近0km/h其占用系数越高；为避免此系数扰动过大，对其取值作约束；因此有以下系数计算公式：In the embodiment of the present invention, the road capacity occupancy coefficient can be determined by: Normalized with the average speed of the target vehicle, the closer to the average speed of the lane, the smaller the impact of the vehicle on the traffic capacity; based on the average speed value, the closer to the speed limit, the lower the occupancy coefficient, and the closer to 0km/h, the higher the occupancy coefficient; to avoid excessive disturbance of this coefficient, its value is constrained; therefore, the following coefficient calculation formula is available:

在本发明申请实施例中，具体地，信控方案确定具体可以包括：根据步骤S32中的计算公式，可以得出精确的各关键车道的交通流量q_i，引用经典的英国TRRL方法进行信号配时计算，生成相位方案。In the embodiment of the present invention, the signal control scheme determination may specifically include: according to the calculation formula in step S32, the accurate traffic flow q_i of each key lane may be obtained, and the classic British TRRL method is used to calculate the signal timing to generate a phase scheme.

较佳地，以上步骤具体可以包括：Preferably, the above steps may specifically include:

首先，关键车道流量比总和计算是根据步骤3.2中给出的改进下各车道交通量进行流量比总和计算，其计算方法如下：First, the total flow ratio of the key lanes is calculated based on the total flow ratio of the traffic volume of each lane under the improvement given in step 3.2. The calculation method is as follows:

其中i为相位的索引，需根据交叉口实际运行情况具体设定；为i相位中k车道的流量比，其计算方法如下：Where i is the index of the phase, which needs to be set according to the actual operation of the intersection; is the flow ratio of lane k in phase i, and its calculation method is as follows:

其中为i相位中k车道的饱和流率，其具体数据根据相关交通规范标准进行确定即可；然后，最优周期时长未TRRL方法信号控制的周期时长应按如下公式计算：in is the saturation flow rate of lane k in phase i, and its specific data can be determined according to relevant traffic regulations and standards; then, the optimal cycle duration of the cycle duration of the TRRL method signal control should be calculated according to the following formula:

40s≤C₀≤180s40s≤C₀ ≤180s

其中L定义为信号总损失时间，其计算公式如下：Where L is defined as the total signal loss time, which is calculated as follows:

其中L_s定义为启动损失时间；AR为i相位的全红交叉口清空时间；Where_Ls is defined as the start-up loss time; AR is the all-red intersection clearing time of phase i;

最后，有效绿灯时间计算是根据各相位准确的流量比分配最优周期时长C₀，其计算方法如下：Finally, the effective green time is calculated by allocating the optimal cycle time C₀ according to the accurate flow ratio of each phase. The calculation method is as follows:

在本发明申请实施例中，由有效绿灯时间生成相位组合G＝[g₁，g₂，g₃，...]，用于进行下一周期的交叉口信号控制；下周期开始时，回到步骤2继续进行基于步骤3给出信控方案下的智能车轨迹优化。In the embodiment of the present invention, the effective green light time generates a phase combination G=[g₁ , g₂ , g₃ , ...] for the next cycle of intersection signal control; when the next cycle starts, the process returns to step 2 to continue the intelligent vehicle trajectory optimization based on the signal control solution given in step 3.

实施例二Embodiment 2

本发明一实施例提供了一种智能网联混合交通流交叉口信号协同优化系统，如图2所示，该协同优化系统具体可以包括如下模块：An embodiment of the present invention provides an intelligent networked mixed traffic flow intersection signal collaborative optimization system, as shown in FIG2 , the collaborative optimization system may specifically include the following modules:

获取模块，用于对交通状态信息进行高精度采集，包括目标车辆运行状态数据与初始信控方案；The acquisition module is used to collect traffic status information with high precision, including target vehicle operation status data and initial signal control scheme;

在本发明申请实施例中，目标车辆运行状态数据具体可以包括：目标类型C_s、目标纵向速度V_p、目标纵向加速度V_ap、目标所在车道号码L_i、目标距停车线距离Y_i；初始信控方案采集包括各相位的绿灯时长、黄灯时长、全红时间、相序方案。数据的采集通过建立于交叉口的雷视一体机目标检测系统进行，实时采集得到的信息同时输入至轨迹优化与信控方案优化方法中。In the embodiment of the present invention, the target vehicle running state data may specifically include: target type_Cs , target longitudinal speed_Vp , target longitudinal acceleration_Vap , target lane number_Li , target distance from the stop line_Yi ; the initial signal control scheme collection includes the green light duration, yellow light duration, full red time, and phase sequence scheme of each phase. The data is collected through the radar-visual integrated machine target detection system established at the intersection, and the information collected in real time is simultaneously input into the trajectory optimization and signal control scheme optimization methods.

处理模块，用于基于初始信息控制的智能车轨迹进行优化，包括交叉口分区界定、智能车队编组、速度引导区边界计算、跟驰模型建立、车速引导策略应用；The processing module is used to optimize the trajectory of the intelligent vehicle based on the initial information control, including intersection partitioning, intelligent vehicle fleet formation, speed guidance zone boundary calculation, car-following model establishment, and vehicle speed guidance strategy application;

具体地，是在周期开始时，采用集中控制器，读取交叉口全部车辆运行状态，同时对交叉口控制范围内所有的智能网联车辆进行速度控制。Specifically, at the beginning of the cycle, a centralized controller is used to read the operating status of all vehicles at the intersection, and at the same time, the speed of all intelligent connected vehicles within the control range of the intersection is controlled.

在本发明申请实施例中，交叉口分区界定具体可以包括：对采集器检测范围L进行分区，主要由车队决策区L_D、速度引导区L_V所组成；车队编组区主要功能为将智能网联车辆与人工驾驶车辆进行编组；速度引导区主要功能为应用速度控制策略实现智能车队的实时控制，为保证交通安全，L_V长度需小于进口道实线区域L_limit；其组成与约束如下：In the embodiment of the present invention, the intersection zoning definition may specifically include: zoning the collector detection range L, which is mainly composed of the fleet decision area_LD and the speed guidance area_LV ; the fleet marshaling area mainly functions to marshall intelligent networked vehicles and manually driven vehicles; the speed guidance area mainly functions to apply the speed control strategy to achieve real-time control of the intelligent fleet. To ensure traffic safety, the length of_LV must be less than the solid line area L_{limit of} the entrance road; its composition and constraints are as follows:

L＝L_D+L_VL＝_LD +_LV

L_V＜L_limit；_LV ＜_Llimit ;

在本发明申请实施例中，智能车队编组具体可以包括：由于人机混驾的环境，智能网联车辆按照一定渗透率存在于交叉口内；在车队决策区L_D内将智能网联车辆(CAV)作为所编组的车队的头车(其中m为头车所在车队编号，l为头车所在车道编号)；其后跟驰的若干人工驾驶车辆(其中m为所在车队编号，j为车队中人工驾驶车辆序号)与头车共同组成智能网联车队M_n；In the embodiment of the present invention, the intelligent vehicle fleet formation may specifically include: due to the mixed driving environment of humans and machines, intelligent networked vehicles exist in the intersection at a certain penetration rate; in the fleet decision area_LD , the intelligent networked vehicle (CAV) is used as the head vehicle of the fleet. (where m is the fleet number of the lead vehicle and l is the lane number of the lead vehicle); several manually driven vehicles following behind (where m is the fleet number and j is the number of the manually driven vehicle in the fleet) and the lead vehicle Together they form an intelligent connected fleet M_n ;

在本发明申请实施例中，速度引导区边界计算具体可以包括：不同于检测器的探测边界，确定车辆被集中控制器开始纳入速度控制策略的影响范围；其判断条件为驾驶员能够接受的加速度极值a_c，并根据加速度极值a_c与道路限制速度V_L计算舒适制动距离S_c；其计算公式如下：In the embodiment of the present invention, the speed guidance zone boundary calculation may specifically include: different from the detection boundary of the detector, determining the influence range of the vehicle being included in the speed control strategy by the centralized controller; its judgment condition is the acceleration extreme value a_c that the driver can accept, and calculating the comfortable braking distance_Sc according to the acceleration extreme value a_c and the road speed limit V_L ; its calculation formula is as follows:

根据计算得出的舒适制动距离S_c，以交叉口停止线为基准确定速度引导区L_V边界；若S_c＞S_limit，取边界距离为S_limit；According to the calculated comfortable braking distance_Sc , the boundary of the speed guidance area_LV is determined with the intersection stop line as the reference; if_Sc >_Slimit , the boundary distance is taken as_Slimit ;

在本发明申请实施例中，跟驰模型建立具体可以包括：由于车队中存在部分人工驾驶车辆，智能网联车队M_n头车的驾驶特征将诱导跟驰人工驾驶车辆以相同目标速度通过交叉口或停止；由于基于雷视一体机的交通采集系统能够得到包括人工驾驶车辆在内的所有车辆的运行状态信息，可应用基于车队的改进FVD(全速度差)跟驰模型，得到后一车队头车与前一车队最后车辆间的跟驰加速度，计算公式如下：In the embodiment of the present invention, the establishment of the following model may specifically include: since there are some manually driven vehicles in the convoy, the driving characteristics of the leading vehicle of the intelligent networked convoy M_n will induce the following manually driven vehicle to pass through the intersection or stop at the same target speed; since the traffic collection system based on the radar-visual integrated device can obtain the operating status information of all vehicles including manually driven vehicles, the improved FVD (full speed difference) following model based on the convoy can be applied to obtain the following convoy leading vehicle The last vehicle in the previous convoy The following acceleration is calculated as follows:

a_m+1＝α(V′_m-V_m(t))+β(V_m+1(t)-V_m(t))a_m+1 = α(V′_m - V_m (t)) + β(V_m+1 (t) -_{V m} (t))

式中：a_m+1－－m+1车队中头车跟驰加速度；Where: a_m+1 - the following acceleration of the leading vehicle in the m+1 convoy;

V′_m－－m车队的头车目标车速；V′_m - target speed of the leading vehicle in the m-vehicle convoy;

V_m(t)——t时刻m车队尾车速度；V_m (t)——the speed of the last vehicle in the m-carriage at time t;

V_m+1(t)－－t时刻m+1车队头车速度；V_m+1 (t)－－the speed of the leading vehicle in the m+1 convoy at time t;

α－－驾驶员敏感系数；β——驾驶员反应系数；α - driver sensitivity coefficient; β - driver reaction coefficient;

在本发明申请实施例中，车速引导策略应用具体可以包括：智能网联车队M_n到达速度引导区L_V边界时，进行相位判断，此行进方向信号灯为红黄灯相位P_r，其剩余时间设为T_r；或绿灯相位P_g，其剩余时间设为T_g；并针对不同的相位状态采用不同的车速引导策略，设置t_r为驾驶员反应时间；In the embodiment of the present invention, the application of the vehicle speed guidance strategy may specifically include: when the intelligent networked vehicle fleet_Mn reaches the boundary of the speed guidance area_LV , a phase judgment is performed, and the travel direction signal light is the red-yellow light phase_Pr , and its remaining time is set to_Tr ; or the green light phase_Pg , and its remaining time is set to_Tg ; and different vehicle speed guidance strategies are adopted for different phase states, and_tr is set as the driver's reaction time;

其中，集中控制器仅在无下游排队车辆或排队车辆无干扰的情况下进行轨迹优化：The centralized controller performs trajectory optimization only when there are no downstream queued vehicles or when there is no interference from the queued vehicles:

1)当或时，取目标车速V′_m＝V_max；后车队的跟驰加速度为：a_m+1＝α(V_max-V_m(t))+β(V_m+1(t)-V_m(t))；1) When or When , the target vehicle speed V′_m = V_max ; the following acceleration of the rear vehicle group is: a_m+1 = α(V_max - V_m (t)) + β(V_m+1 (t) - V_m (t));

2)当时，应降低车速V_m至目标车速V′_m，使车队到达停止线时刻在绿灯启亮时刻，V′_m的求解方式为：2) When When the vehicle speed V m is reached, the vehicle speed V_m should be reduced to the target vehicle speed V ′_m so that the convoy reaches the stop line at the time when the green light turns on. The solution of V ′_m is:

式中：V_min——道路最低限速；V_max——道路最高限速。In the formula: V_min —— minimum speed limit of the road; V_max —— maximum speed limit of the road.

优化模块，用于基于优化后的所述智能车轨迹的信号控制进行优化，包括标准小汽车当量参数优化，道路通行能力占用系数确定，信控方案确定。The optimization module is used to optimize the signal control based on the optimized trajectory of the smart car, including the optimization of standard car equivalent parameters, the determination of road capacity occupancy coefficient, and the determination of signal control scheme.

具体地，在一个周期中，集中控制器将实时读取轨迹优化后交叉口所有车辆的运行状态，利用基于改进标准小汽车当量的webster法进行交叉口信号控制。Specifically, in one cycle, the centralized controller will read the operating status of all vehicles at the intersection after trajectory optimization in real time, and use the Webster method based on the improved standard car equivalent to control the intersection signal.

在本发明申请实施例中，标准小汽车当量参数优化具体可以包括：基于雷视一体机对于车辆运行状态获取的准确性，能够利用交叉口内运行车辆的车辆类型、车辆大小、占用空间、运行速度对小汽车当量换算系数进行改进：In the embodiment of the present invention, the optimization of the standard car equivalent parameters may specifically include: based on the accuracy of the radar-visual integrated device in obtaining the vehicle running status, the car equivalent conversion coefficient can be improved by using the vehicle type, vehicle size, occupied space, and running speed of the vehicles running in the intersection:

其中：为换算后k车道总交通量，G_s为所有小汽车，G_r为所有中型车，G_l为所有大型车，G_b为所有公交车，其计数均由雷视一体机采集系统直接给出；in: is the total traffic volume of lane k after conversion,_Gs is all cars,_Gr is all medium-sized cars,_Gl is all large cars, and_Gb is all buses, and their counts are directly given by the radar-visual integrated machine acquisition system;

λ_s、λ_r、λ_l、λ_b分别为小汽车、中型车、大型车、公交车的空间占用系数，其反应各类车辆对道路面积的占用度；并根据交通流率情况确定公交优先度，即λ_b取值；λ_s , λ_r , λ_l , λ_b are the space occupancy coefficients of cars, medium-sized cars, large cars, and buses, respectively, which reflect the occupancy of road area by various types of vehicles; and the priority of buses, i.e. the value of λ_b , is determined according to the traffic flow rate;

δ_s、δ_r、δ_l、δ_b分别为小汽车、中型车、大型车、公交车的道路通行能力占用系数；δ_s , δ_r , δ_l , δ_b are the road capacity occupancy coefficients of cars, medium-sized cars, large cars, and buses, respectively;

在本发明申请实施例中，道路通行能力占用系数确定具体可以包括：根据该车道平均速度与目标车辆平均车速归一化，由于越接近车道平均速度，车辆对通行能力影响越小；基于平均速度值，越接近限速值占用系数越低，越接近0km/h其占用系数越高；为避免此系数扰动过大，对其取值作约束；因此有以下系数计算公式：In the embodiment of the present invention, the road capacity occupancy coefficient can be determined by: Normalized with the average speed of the target vehicle, the closer to the average speed of the lane, the smaller the impact of the vehicle on the traffic capacity; based on the average speed value, the closer to the speed limit, the lower the occupancy coefficient, and the closer to 0km/h, the higher the occupancy coefficient; to avoid excessive disturbance of this coefficient, its value is constrained; therefore, the following coefficient calculation formula is available:

在本发明申请实施例中，具体地，信控方案确定具体可以包括：根据步骤S32中的计算公式，可以得出精确的各关键车道的交通流量q_i，引用经典的英国TRRL方法进行信号配时计算，生成相位方案。In the embodiment of the present invention, the signal control scheme determination may specifically include: according to the calculation formula in step S32, the accurate traffic flow q_i of each key lane may be obtained, and the classic British TRRL method is used to calculate the signal timing to generate a phase scheme.

较佳地，以上步骤具体可以包括：Preferably, the above steps may specifically include:

首先，关键车道流量比总和计算是根据步骤3.2中给出的改进下各车道交通量进行流量比总和计算，其计算方法如下：First, the total flow ratio of the key lanes is calculated based on the total flow ratio of the traffic volume of each lane under the improvement given in step 3.2. The calculation method is as follows:

其中i为相位的索引，需根据交叉口实际运行情况具体设定；为i相位中k车道的流量比，其计算方法如下：Where i is the index of the phase, which needs to be set according to the actual operation of the intersection; is the flow ratio of lane k in phase i, and its calculation method is as follows:

其中为i相位中k车道的饱和流率，其具体数据根据相关交通规范标准进行确定即可；in is the saturation flow rate of lane k in phase i, and its specific data can be determined according to relevant traffic regulations and standards;

然后，最优周期时长未TRRL方法信号控制的周期时长应按如下公式计算：Then, the optimal cycle duration and the cycle duration of the TRRL method signal control should be calculated according to the following formula:

40s≤C₀≤180s40s≤C₀ ≤180s

其中L定义为信号总损失时间，其计算公式如下：Where L is defined as the total signal loss time, which is calculated as follows:

其中L_s定义为启动损失时间；AR为i相位的全红交叉口清空时间；Where_Ls is defined as the start-up loss time; AR is the all-red intersection clearing time of phase i;

最后，有效绿灯时间计算是根据各相位准确的流量比分配最优周期时长C₀，其计算方法如下：Finally, the effective green time is calculated by allocating the optimal cycle time C₀ according to the accurate flow ratio of each phase. The calculation method is as follows:

在本发明申请实施例中，由有效绿灯时间生成相位组合G＝[g₁，g₂，g₃，...]，用于进行下一周期的交叉口信号控制；下周期开始时，回到步骤2继续进行基于步骤3给出信控方案下的智能车轨迹优化。In the embodiment of the present invention, the effective green light time generates a phase combination G=[g₁ , g₂ , g₃ , ...] for the next cycle of intersection signal control; when the next cycle starts, the process returns to step 2 to continue the intelligent vehicle trajectory optimization based on the signal control solution given in step 3.

实施例三Embodiment 3

本发明一实施例提供了一种智能网联混合交通流交叉口信号协同优化系统，如图3所示，该协同优化系统具体可以包括如下模块：An embodiment of the present invention provides a smart networked mixed traffic flow intersection signal collaborative optimization system, as shown in FIG3 , the collaborative optimization system may specifically include the following modules:

通信总线，用于实现处理器与存储器间的连接通信；A communication bus, used to realize the connection and communication between the processor and the memory;

存储器，用于存储计算机程序；存储器可能包含高速RAM存储器，也可能还包含非不稳定的存储器(non-volatile memory)，例如至少一个磁盘存储器。存储器可选的可以包含至少一个存储装置。The memory is used to store computer programs; the memory may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory. The memory may optionally include at least one storage device.

处理器，用于执行上述计算机程序以实现如下步骤：A processor is used to execute the above computer program to implement the following steps:

首先，对交通状态信息进行高精度采集，包括目标车辆运行状态数据与初始信控方案；First, traffic status information is collected with high precision, including target vehicle operation status data and initial signal control scheme;

在本发明申请实施例中，目标车辆运行状态数据具体可以包括：目标类型C_s、目标纵向速度V_p、目标纵向加速度V_ap、目标所在车道号码L_i、目标距停车线距离Y_i；初始信控方案采集包括各相位的绿灯时长、黄灯时长、全红时间、相序方案。数据的采集通过建立于交叉口的雷视一体机目标检测系统进行，实时采集得到的信息同时输入至轨迹优化与信控方案优化方法中。In the embodiment of the present invention, the target vehicle running state data may specifically include: target type_Cs , target longitudinal speed_Vp , target longitudinal acceleration_Vap , target lane number_Li , target distance from the stop line_Yi ; the initial signal control scheme collection includes the green light duration, yellow light duration, full red time, and phase sequence scheme of each phase. The data is collected through the radar-visual integrated machine target detection system established at the intersection, and the information collected in real time is simultaneously input into the trajectory optimization and signal control scheme optimization methods.

然后，基于初始信息控制的智能车轨迹进行优化，包括交叉口分区界定、智能车队编组、速度引导区边界计算、跟驰模型建立、车速引导策略应用；Then, the trajectory of the intelligent vehicle controlled by the initial information is optimized, including intersection partitioning, intelligent vehicle fleet formation, speed guidance zone boundary calculation, car-following model establishment, and vehicle speed guidance strategy application;

具体地，是在周期开始时，采用集中控制器，读取交叉口全部车辆运行状态，同时对交叉口控制范围内所有的智能网联车辆进行速度控制。Specifically, at the beginning of the cycle, a centralized controller is used to read the operating status of all vehicles at the intersection, and at the same time, the speed of all intelligent connected vehicles within the control range of the intersection is controlled.

在本发明申请实施例中，交叉口分区界定具体可以包括：对采集器检测范围L进行分区，主要由车队决策区L_D、速度引导区L_V所组成；车队编组区主要功能为将智能网联车辆与人工驾驶车辆进行编组；速度引导区主要功能为应用速度控制策略实现智能车队的实时控制，为保证交通安全，L_V长度需小于进口道实线区域L_limit；其组成与约束如下：In the embodiment of the present invention, the intersection zoning definition may specifically include: zoning the collector detection range L, which is mainly composed of the fleet decision area_LD and the speed guidance area_LV ; the fleet marshaling area mainly functions to marshall intelligent networked vehicles and manually driven vehicles; the speed guidance area mainly functions to apply the speed control strategy to achieve real-time control of the intelligent fleet. To ensure traffic safety, the length of_LV must be less than the solid line area L_{limit of} the entrance road; its composition and constraints are as follows:

L＝L_D+L_VL＝_LD +_LV

L_V＜L_limit；_LV ＜_Llimit ;

在本发明申请实施例中，智能车队编组具体可以包括：由于人机混驾的环境，智能网联车辆按照一定渗透率存在于交叉口内；在车队决策区L_D内将智能网联车辆(CAV)作为所编组的车队的头车(其中m为头车所在车队编号，l为头车所在车道编号)；其后跟驰的若干人工驾驶车辆(其中m为所在车队编号，j为车队中人工驾驶车辆序号)与头车共同组成智能网联车队M_n；In the embodiment of the present invention, the intelligent vehicle fleet formation may specifically include: due to the mixed driving environment of humans and machines, intelligent networked vehicles exist in the intersection at a certain penetration rate; in the fleet decision area_LD , the intelligent networked vehicle (CAV) is used as the head vehicle of the fleet. (where m is the fleet number of the lead vehicle and l is the lane number of the lead vehicle); several manually driven vehicles following behind (where m is the fleet number and j is the number of the manually driven vehicle in the fleet) and the lead vehicle Together they form an intelligent connected fleet M_n ;

在本发明申请实施例中，速度引导区边界计算具体可以包括：不同于检测器的探测边界，确定车辆被集中控制器开始纳入速度控制策略的影响范围；其判断条件为驾驶员能够接受的加速度极值a_c，并根据加速度极值a_c与道路限制速度V_L计算舒适制动距离S_c；其计算公式如下：In the embodiment of the present invention, the speed guidance zone boundary calculation may specifically include: different from the detection boundary of the detector, determining the influence range of the vehicle being included in the speed control strategy by the centralized controller; its judgment condition is the acceleration extreme value a_c that the driver can accept, and calculating the comfortable braking distance_Sc according to the acceleration extreme value a_c and the road speed limit V_L ; its calculation formula is as follows:

根据计算得出的舒适制动距离S_c，以交叉口停止线为基准确定速度引导区L_V边界；若S_c＞S_limit，取边界距离为S_limit；According to the calculated comfortable braking distance_Sc , the boundary of the speed guidance area_LV is determined with the intersection stop line as the reference; if_Sc >_Slimit , the boundary distance is taken as_Slimit ;

在本发明申请实施例中，跟驰模型建立具体可以包括：由于车队中存在部分人工驾驶车辆，智能网联车队M_n头车的驾驶特征将诱导跟驰人工驾驶车辆以相同目标速度通过交叉口或停止；由于基于雷视一体机的交通采集系统能够得到包括人工驾驶车辆在内的所有车辆的运行状态信息，可应用基于车队的改进FVD(全速度差)跟驰模型，得到后一车队头车与前一车队最后车辆间的跟驰加速度，计算公式如下：In the embodiment of the present invention, the establishment of the following model may specifically include: since there are some manually driven vehicles in the convoy, the driving characteristics of the leading vehicle of the intelligent networked convoy M_n will induce the following manually driven vehicle to pass through the intersection or stop at the same target speed; since the traffic collection system based on the radar-visual integrated device can obtain the operating status information of all vehicles including manually driven vehicles, the improved FVD (full speed difference) following model based on the convoy can be applied to obtain the following convoy leading vehicle The last vehicle in the previous convoy The following acceleration is calculated as follows:

a_m+1＝α(V′_m-V_m(t))+β(V_m+1(t)-V_m(t))a_m+1 = α(V′_m - V_m (t)) + β(V_m+1 (t) -_{V m} (t))

式中：a_m+1－－m+1车队中头车跟驰加速度；Where: a_m+1 - the following acceleration of the leading vehicle in the m+1 convoy;

V′_m——m车队的头车目标车速；V′_m ——the target speed of the leading vehicle in the m-vehicle convoy;

V_m(t)——t时刻m车队尾车速度；V_m (t)——the speed of the last vehicle in the m-carriage at time t;

V_m+1(t)——t时刻m+1车队头车速度；V_m+1 (t)——the speed of the leading vehicle in the m+1 convoy at time t;

α——驾驶员敏感系数；β——驾驶员反应系数；α——driver sensitivity coefficient; β——driver response coefficient;

在本发明申请实施例中，车速引导策略应用具体可以包括：智能网联车队M_n到达速度引导区L_V边界时，进行相位判断，此行进方向信号灯为红黄灯相位P_r，其剩余时间设为T_r；或绿灯相位P_g，其剩余时间设为T_g；并针对不同的相位状态采用不同的车速引导策略，设置t_r为驾驶员反应时间；In the embodiment of the present invention, the application of the vehicle speed guidance strategy may specifically include: when the intelligent networked vehicle fleet_Mn reaches the boundary of the speed guidance area_LV , a phase judgment is performed, and the travel direction signal light is the red-yellow light phase_Pr , and its remaining time is set to_Tr ; or the green light phase_Pg , and its remaining time is set to_Tg ; and different vehicle speed guidance strategies are adopted for different phase states, and_tr is set as the driver's reaction time;

其中，集中控制器仅在无下游排队车辆或排队车辆无干扰的情况下进行轨迹优化：The centralized controller performs trajectory optimization only when there are no downstream queued vehicles or when there is no interference from the queued vehicles:

1)当或时，取目标车速V′_m＝V_max；后车队的跟驰加速度为：a_m+1＝α(V_max-V_m(t))+β(V_m+1(t)-V_m(t))；1) When or When , the target vehicle speed V′_m = V_max ; the following acceleration of the rear vehicle group is: a_m+1 = α(V_max - V_m (t)) + β(V_m+1 (t) - V_m (t));

2)当时，应降低车速V_m至目标车速V′_m，使车队到达停止线时刻在绿灯启亮时刻，V′_m的求解方式为：2) When When the vehicle speed V m is reached, the vehicle speed V_m should be reduced to the target vehicle speed V ′_m so that the convoy reaches the stop line at the time when the green light turns on. The solution of V ′_m is:

式中：V_min——道路最低限速；V_max——道路最高限速。In the formula: V_min —— minimum speed limit of the road; V_max —— maximum speed limit of the road.

最后，基于优化后的所述智能车轨迹的信号控制进行优化，包括标准小汽车当量参数优化，道路通行能力占用系数确定，信控方案确定。Finally, the signal control based on the optimized trajectory of the smart car is optimized, including the optimization of standard car equivalent parameters, the determination of road capacity occupancy coefficient, and the determination of the signal control scheme.

具体地，在一个周期中，集中控制器将实时读取轨迹优化后交叉口所有车辆的运行状态，利用基于改进标准小汽车当量的webster法进行交叉口信号控制。Specifically, in one cycle, the centralized controller will read the operating status of all vehicles at the intersection after trajectory optimization in real time, and use the Webster method based on the improved standard car equivalent to control the intersection signal.

在本发明申请实施例中，标准小汽车当量参数优化具体可以包括：基于雷视一体机对于车辆运行状态获取的准确性，能够利用交叉口内运行车辆的车辆类型、车辆大小、占用空间、运行速度对小汽车当量换算系数进行改进：In the embodiment of the present invention, the optimization of the standard car equivalent parameters may specifically include: based on the accuracy of the vehicle running status acquired by the integrated radar and vision device, the car equivalent conversion coefficient can be improved by using the vehicle type, vehicle size, occupied space, and running speed of the vehicles running in the intersection:

其中：为换算后k车道总交通量，G_s为所有小汽车，G_r为所有中型车，G_l为所有大型车，G_b为所有公交车，其计数均由雷视一体机采集系统直接给出；in: is the total traffic volume of lane k after conversion,_Gs is all cars,_Gr is all medium-sized cars,_Gl is all large cars, and_Gb is all buses, and their counts are directly given by the radar-visual integrated machine acquisition system;

λ_s、λ_r、λ_l、λ_b分别为小汽车、中型车、大型车、公交车的空间占用系数，其反应各类车辆对道路面积的占用度；并根据交通流率情况确定公交优先度，即λ_b取值；λ_s , λ_r , λ_l , λ_b are the space occupancy coefficients of cars, medium-sized cars, large cars, and buses, respectively, which reflect the occupancy of road area by various types of vehicles; and the priority of buses, i.e. the value of λ_b , is determined according to the traffic flow rate;

δ_s、δ_r、δ_l、δ_b分别为小汽车、中型车、大型车、公交车的道路通行能力占用系数；δ_s , δ_r , δ_l , δ_b are the road capacity occupancy coefficients of cars, medium-sized cars, large cars, and buses, respectively;

在本发明申请实施例中，道路通行能力占用系数确定具体可以包括：根据该车道平均速度与目标车辆平均车速归一化，由于越接近车道平均速度，车辆对通行能力影响越小；基于平均速度值，越接近限速值占用系数越低，越接近0km/h其占用系数越高；为避免此系数扰动过大，对其取值作约束；因此有以下系数计算公式：In the embodiment of the present invention, the road capacity occupancy coefficient can be determined by: Normalized with the average speed of the target vehicle, the closer to the average speed of the lane, the smaller the impact of the vehicle on the traffic capacity; based on the average speed value, the closer to the speed limit, the lower the occupancy coefficient, and the closer to 0km/h, the higher the occupancy coefficient; to avoid excessive disturbance of this coefficient, its value is constrained; therefore, the following coefficient calculation formula is available:

在本发明申请实施例中，具体地，信控方案确定具体可以包括：根据步骤S32中的计算公式，可以得出精确的各关键车道的交通流量q_i，引用经典的英国TRRL方法进行信号配时计算，生成相位方案。In the embodiment of the present invention, the signal control scheme determination may specifically include: according to the calculation formula in step S32, the accurate traffic flow q_i of each key lane may be obtained, and the classic British TRRL method is used to calculate the signal timing to generate a phase scheme.

较佳地，以上步骤具体可以包括：Preferably, the above steps may specifically include:

首先，关键车道流量比总和计算是根据步骤3.2中给出的改进下各车道交通量进行流量比总和计算，其计算方法如下：First, the total flow ratio of the key lanes is calculated based on the total flow ratio of the traffic volume of each lane under the improvement given in step 3.2. The calculation method is as follows:

其中i为相位的索引，需根据交叉口实际运行情况具体设定；为i相位中k车道的流量比，其计算方法如下：Where i is the index of the phase, which needs to be set according to the actual operation of the intersection; is the flow ratio of lane k in phase i, and its calculation method is as follows:

其中为i相位中k车道的饱和流率，其具体数据根据相关交通规范标准进行确定即可；然后，最优周期时长未TRRL方法信号控制的周期时长应按如下公式计算：in is the saturation flow rate of lane k in phase i, and its specific data can be determined according to relevant traffic regulations and standards; then, the optimal cycle duration of the cycle duration of the TRRL method signal control should be calculated according to the following formula:

40s≤C₀≤180s40s≤C₀ ≤180s

其中L定义为信号总损失时间，其计算公式如下：Where L is defined as the total signal loss time, which is calculated as follows:

其中L_s定义为启动损失时间；AR为i相位的全红交叉口清空时间；Where_Ls is defined as the start-up loss time; AR is the all-red intersection clearing time of phase i;

最后，有效绿灯时间计算是根据各相位准确的流量比分配最优周期时长C₀，其计算方法如下：Finally, the effective green time is calculated by allocating the optimal cycle time C₀ according to the accurate flow ratio of each phase. The calculation method is as follows:

在本发明申请实施例中，由有效绿灯时间生成相位组合G＝[g₁，g₂，g₃，...]，用于进行下一周期的交叉口信号控制；下周期开始时，回到步骤2继续进行基于步骤3给出信控方案下的智能车轨迹优化。In the embodiment of the present invention, the effective green light time generates a phase combination G=[g₁ , g₂ , g₃ , ...] for the next cycle of intersection signal control; when the next cycle starts, the process returns to step 2 to continue the intelligent vehicle trajectory optimization based on the signal control solution given in step 3.

本实施例中的处理器可能是一种集成电路芯片，具有信号处理能力。在实现过程中，上述方法的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。上述的处理器可以是通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本发明实施例中的公开的各方法、步骤及逻辑框图。上述处理器可以是微处理器或者上述处理器也可以是任何常规的处理器等。结合本发明实施例所公开的方法的步骤可以直接体现为硬件处理器执行完成，或者用处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上述方法的步骤。The processor in this embodiment may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The above processor may be a microprocessor or the above processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiment of the present invention can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware.

实施例四Embodiment 4

本发明一实施例提供了一种计算机可读存储介质，其上存储有计算机程序，上述计算机程序被处理器执行时实现上述的协同优化方法。An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the collaborative optimization method described above is implemented.

综上所述，本发明实施例提供的一种智能网联混合交通流交叉口信号协同优化方法及系统，通过对交通状态信息进行高精度采集，包括目标车辆运行状态数据与初始信控方案；基于初始信息控制的智能车轨迹进行优化，包括交叉口分区界定、智能车队编组、速度引导区边界计算、跟驰模型建立、车速引导策略应用；基于优化后的所述智能车轨迹的信号控制进行优化，包括标准小汽车当量参数优化，道路通行能力占用系数确定，信控方案确定。这样，通过采用车辆编组的方式，将智能车与人工车辆编组，使用车队进行跟驰；并利用了雷视一体机的采集特性，车队领头智能车能够获得前车队车尾人工驾驶车辆的速度，从而进行跟驰，解决了混合交通模式下，智能车渗透率较低，存在大量的人工智能驾驶跟驰智能车辆；且仅仅是控制智能车单车，未考虑多车跟驰状态，优化作用将有限的缺点。同时，通过采用了轨迹与信号协同优化的方式，避免了现有仅采用单边优化的混合交通情况导致的往往仅针对车辆运行状态进行信号优化，或者在固定配时方案下进行轨迹优化与速度控制的弊端，进一步提升交叉口的运行效率，保障了数据来源更精确、微观，并且控制策略更合理、有效。In summary, the embodiment of the present invention provides a method and system for coordinated optimization of intersection signals of intelligent networked mixed traffic flow, which collects traffic status information with high precision, including target vehicle operation status data and initial signal control scheme; optimizes the trajectory of the intelligent vehicle controlled by the initial information, including intersection partitioning, intelligent vehicle fleet formation, speed guidance zone boundary calculation, following model establishment, and vehicle speed guidance strategy application; optimizes the signal control based on the optimized trajectory of the intelligent vehicle, including standard car equivalent parameter optimization, road capacity occupancy coefficient determination, and signal control scheme determination. In this way, by adopting the vehicle formation method, the intelligent vehicle and the manual vehicle are formed, and the vehicle fleet is used for following; and by utilizing the acquisition characteristics of the integrated radar vision device, the leading intelligent vehicle of the vehicle fleet can obtain the speed of the manual vehicle at the rear of the vehicle fleet, so as to follow, which solves the problem that the penetration rate of intelligent vehicles is low in the mixed traffic mode, and there are a large number of artificial intelligence driving and following intelligent vehicles; and only controls a single intelligent vehicle, without considering the following state of multiple vehicles, and the optimization effect will be limited. At the same time, by adopting the method of coordinated optimization of trajectory and signal, the disadvantages of the existing mixed traffic conditions that only use unilateral optimization, which often only optimizes signals according to the vehicle operation status, or optimizes trajectory and speed under fixed timing schemes, are avoided. This further improves the operation efficiency of intersections, ensures that the data source is more accurate and microscopic, and the control strategy is more reasonable and effective.

需要说明的是，对于前述的各方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本发明并不受所描述的动作顺序的限制，因为依据本发明，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于可选实施例，所涉及的动作和模块并不一定是本发明所必须的。It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present invention.

上述实施例，可以全部或部分地通过软件、硬件、固件或其他任意组合来实现。当使用软件实现时，上述实施例可以全部或部分地以计算机程序产品的形式实现。该计算机程序产品包括一个或多个计算机指令。在计算机上加载或执行该计算机程序指令时，全部或部分地产生按照本申请实施例该的流程或功能。该计算机可以为通用计算机、专用计算机、计算机网络、或者其他可编程装置。该计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，该计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。该计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集合的服务器、数据中心等数据存储设备。该可用介质可以是磁性介质(例如，软盘、硬盘、磁带)、光介质(例如，DVD)、或者半导体介质。半导体介质可以是固态硬盘。The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by a wired (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or a data center containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

在本发明所提供的几个实施例中，应该理解到，所揭露的系统，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(read-only memory，ROM)、随机存取存储器(random access memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

本发明是参照本发明实施例的方法、装置(设备)和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。The present invention is described with reference to the flowcharts and/or block diagrams of the methods, devices (equipment) and computer program products of the embodiments of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the processes and/or boxes in the flowchart and/or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims (4)

1. An intelligent network-connected hybrid traffic flow intersection signal collaborative optimization method is characterized by comprising the following steps:

Step 1: the traffic state information is collected with high precision, wherein the traffic state information comprises target vehicle running state data and an initial information control scheme;

Step 2: optimizing the intelligent vehicle track based on initial information control, wherein the intelligent vehicle track comprises intersection partition definition, intelligent vehicle team grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application;

Step 3: optimizing based on the optimized signal control of the intelligent vehicle track, including optimizing standard car equivalent parameters, determining road traffic capacity occupancy coefficients and determining a signal control scheme;

The step 2 comprises the following steps:

s21, intersection partition definition: partitioning the detection range L of the collector, wherein the detection range L mainly comprises a motorcade decision area L_D and a speed guide area L_V; the main function of the motorcade marshalling area is to marshal intelligent network vehicles and manual driving vehicles; the main function of the speed guiding area is to apply a speed control strategy to realize real-time control of an intelligent motorcade, and the length L_V is required to be smaller than the solid line area L_limit of the entrance way in order to ensure traffic safety; the composition and constraints are as follows:

L＝L_D+L_V

L_V<L_limit；

S22, intelligent motorcade grouping: because of the man-machine driving environment, the intelligent network-connected vehicle exists in the intersection according to a certain permeability; intelligent networked vehicle CAV as the head car of the marshalled fleet in the fleet decision area L_DWherein m is the number of the fleet where the head car is located, and l is the number of the lane where the head car is located; several manually driven vehicles with heelWith the head carForming an intelligent network connected fleet M_n together, wherein M is the number of the fleet, and j is the number of the manual driving vehicles in the fleet;

S23, calculating a speed guide area boundary: determining an influence range of the vehicle which is started to be incorporated into the speed control strategy by the centralized controller, different from the detection boundary of the detector; the judgment condition is an acceleration extremum a_c which can be accepted by a driver, and a comfortable braking distance S_c is calculated according to the acceleration extremum a_c and a road limiting speed V_L; the calculation formula is as follows:

Determining a boundary of the speed guide area L_V by taking an intersection stop line as a reference according to the calculated comfortable braking distance S_c; if S_c>S_limit, taking the boundary distance as S_limit;

S24, building a following model: because part of manual driving vehicles exist in the motorcade, the driving characteristics of the M_n head vehicles of the intelligent network motorcade can induce the manual driving vehicles to pass through the intersection or stop at the same target speed; because the traffic collection system based on the thunder integrated machine can obtain the running state information of all vehicles including the manual driving vehicles, an improved FVD full speed difference following model based on a fleet can be applied to obtain the following acceleration between the head car CAV_l^m+1 of the fleet and the last vehicle HV_j^m of the fleet, and the calculation formula is as follows:

a_m+1＝α(V′_m-V_m(t))+β(V_m+1(t)-V_m(t))

Wherein: a_m+1 -the first vehicle heel-off acceleration in the m+1 fleet;

V'_m -the target vehicle speed of the head vehicle of the m vehicle fleets;

v_m (t) -the speed of the tail car of the m motorcade at the moment t;

V_m+1 (t) -t time m+1 fleet head speed;

alpha-the driver sensitivity coefficient; beta-driver reaction coefficient;

S25, vehicle speed guiding strategy application: when the intelligent network train M_n reaches the boundary of the speed guiding area L_V, judging the phase, wherein the running direction signal lamp is a red-yellow lamp phase P_r, and the rest time is set as T_r; or green light phase P_g, the remaining time of which is set to T_g; different vehicle speed guiding strategies are adopted aiming at different phase states, and t_r is set as the response time of a driver;

Wherein the centralized controller performs trajectory optimization only in the absence of downstream queuing vehicles or in the absence of disturbances of queuing vehicles:

1) When (when)Or (b)When the vehicle is started, taking a target vehicle speed V'_m＝V_max; the following acceleration of the rear fleet is: a_m+1＝α(V_max-V_m(t))+β(V_m+1(t)-V_m (t));

2) When (when)When the vehicle speed V_m is reduced to the target vehicle speed V '_m, the time when the motorcade reaches the stop line is at the green light starting time, and the solving mode of V'_m is as follows:

Wherein: v_min —road minimum speed limit; v_max -road highest speed limit;

the step 3 comprises the following steps:

s31, optimizing equivalent parameters of a standard car: based on the accuracy that the thunder all-in-one obtained to the vehicle running state, can utilize vehicle type, vehicle size, occupation space, the running speed of the interior operation vehicle of crossing to improve car equivalent conversion coefficient:

wherein: For the converted total traffic of k lanes, G_s is all cars, G_r is all medium-sized cars, G_l is all large-sized cars, G_b is all buses, and the counting is directly given by a thunder all-in-one machine acquisition system;

Lambda_s、λ_r、λ_l、λ_b is the space occupation coefficient of a car, a medium-sized car, a large-sized car and a bus respectively, and reflects the occupation degree of various vehicles on road areas; determining bus priority, namely lambda_b value, according to traffic flow rate condition;

delta_s、δ_r、δ_l、δ_b is the road traffic capacity occupancy coefficient of a car, a medium-sized car, a large-sized car and a bus respectively;

S32, determining a road traffic capacity occupancy coefficient: according to the average speed of the laneNormalizing the average speed of the target vehicle, wherein the influence of the vehicle on the traffic capacity is smaller as the average speed of the lane is closer; based on the average speed value, the occupancy coefficient is lower as the speed limit value is approached, and the occupancy coefficient is higher as the speed limit value is approached to 0 km/h; in order to avoid the overlarge disturbance of the coefficient, the value of the coefficient is restrained; the following coefficient calculation formula is therefore available:

s33, determining a signal control scheme: according to the calculation formula in the step S32, the accurate traffic flow q_i of each key lane can be obtained, and the classical British TRRL method is referenced for signal timing calculation to generate a phase scheme;

In one period, the central controller optimizes the real-time reading track and then controls the running states of all vehicles at the intersection by using webster method based on improved standard car equivalent;

The step S33 includes:

s41, calculating the total flow ratio of the key lanes: the total flow ratio is calculated according to the traffic volume of each lane under the improvement given in the step 3.2, and the calculation method is as follows:

I is an index of the phase, and is specifically set according to the actual running condition of the intersection; The flow ratio of k lanes in the i phase is calculated as follows:

Wherein the method comprises the steps ofThe specific data of the saturated flow rate of k lanes in the i phase is determined according to the related traffic specification standard;

s42, optimal period duration: the period duration of TRRL method signal control should be calculated according to the following formula:

40s≤C₀≤180s

Where L is defined as the total lost time of the signal, and its calculation formula is as follows:

wherein L_s is defined as start-up lost time; AR is the clearing time of the full red intersection of the i phase;

S43, calculating effective green light time: the optimal period duration C₀ is distributed according to the accurate flow ratio of each phase, and the calculation method is as follows:

Generating a phase combination G= [ G₁,g₂,g₃, … ] by the effective green light time for intersection signal control of the next period; and when the next period starts, returning to the step 2 to continue the intelligent vehicle track optimization under the signal control scheme given in the step 3.

2. An intelligent network-connected hybrid traffic flow intersection signal collaborative optimization system, the system comprising:

the acquisition module is used for carrying out high-precision acquisition on traffic state information, and comprises target vehicle running state data and an initial information control scheme;

The processing module is used for optimizing the intelligent vehicle track based on initial information control, and comprises intersection partition definition, intelligent vehicle team grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application;

the optimizing module is used for optimizing based on the optimized signal control of the intelligent vehicle track, and comprises optimizing standard car equivalent parameters, determining road traffic capacity occupation coefficients and determining a signal control scheme;

the processing module comprises:

s21, intersection partition definition: partitioning the detection range L of the collector, wherein the detection range L mainly comprises a motorcade decision area L_D and a speed guide area L_V; the main function of the motorcade marshalling area is to marshal intelligent network vehicles and manual driving vehicles; the main function of the speed guiding area is to apply a speed control strategy to realize real-time control of an intelligent motorcade, and the length L_V is required to be smaller than the solid line area L_limit of the entrance way in order to ensure traffic safety; the composition and constraints are as follows:

L＝L_D+L_V

L_V<L_limit；

S22, intelligent motorcade grouping: because of the man-machine driving environment, the intelligent network-connected vehicle exists in the intersection according to a certain permeability; taking the intelligent network-connected vehicle CAV as a head vehicle CAV_l^m of a grouped fleet in a fleet decision area L_D, wherein m is a fleet number of the head vehicle, and L is a lane number of the head vehicle; a plurality of manual driving vehicles HV_j^m which follow the intelligent network train consist of an intelligent network train M_n together with a head car CAV_l^m, wherein M is the serial number of the train, and j is the serial number of the manual driving vehicle in the train;

S23, calculating a speed guide area boundary: determining an influence range of the vehicle which is started to be incorporated into the speed control strategy by the centralized controller, different from the detection boundary of the detector; the judgment condition is an acceleration extremum a_c which can be accepted by a driver, and a comfortable braking distance S_c is calculated according to the acceleration extremum a_c and a road limiting speed V_L; the calculation formula is as follows:

Determining a boundary of the speed guide area L_V by taking an intersection stop line as a reference according to the calculated comfortable braking distance S_c; if S_c>S_limit, taking the boundary distance as S_limit;

S24, building a following model: because part of manual driving vehicles exist in the motorcade, the driving characteristics of the M_n head vehicles of the intelligent network motorcade can induce the manual driving vehicles to pass through the intersection or stop at the same target speed; because the traffic collection system based on the thunder integrated machine can obtain the running state information of all vehicles including the manual driving vehicles, an improved FVD full speed difference following model based on a fleet can be applied to obtain the following acceleration between the head car CAV_l^m+1 of the fleet and the last vehicle HV_j^m of the fleet, and the calculation formula is as follows:

a_m+1＝α(V′_m-V_m(t))+β(V_m+1(t)-V_m(t))

Wherein: a_m+1 -the first vehicle heel-off acceleration in the m+1 fleet;

V'_m -the target vehicle speed of the head vehicle of the m vehicle fleets;

v_m (t) -the speed of the tail car of the m motorcade at the moment t;

V_m+1 (t) -t time m+1 fleet head speed;

alpha-the driver sensitivity coefficient; beta-driver reaction coefficient;

S25, vehicle speed guiding strategy application: when the intelligent network train M_n reaches the boundary of the speed guiding area L_V, judging the phase, wherein the running direction signal lamp is a red-yellow lamp phase P_r, and the rest time is set as T_r; or green light phase P_g, the remaining time of which is set to T_g; different vehicle speed guiding strategies are adopted aiming at different phase states, and t_r is set as the response time of a driver;

Wherein the centralized controller performs trajectory optimization only in the absence of downstream queuing vehicles or in the absence of disturbances of queuing vehicles:

1) When (when)Or (b)When the vehicle is started, taking a target vehicle speed V'_m＝V_max; the following acceleration of the rear fleet is: a_m+1＝α(V_max-V_m(t))+β(V_m+1(t)-V_m (t));

2) When (when)When the vehicle speed V_m is reduced to the target vehicle speed V '_m, the time when the motorcade reaches the stop line is at the green light starting time, and the solving mode of V'_m is as follows:

Wherein: v_min —road minimum speed limit; v_max -road highest speed limit;

The optimization module comprises:

s31, optimizing equivalent parameters of a standard car: based on the accuracy that the thunder all-in-one obtained to the vehicle running state, can utilize vehicle type, vehicle size, occupation space, the running speed of the interior operation vehicle of crossing to improve car equivalent conversion coefficient:

wherein: For the converted total traffic of k lanes, G_s is all cars, G_r is all medium-sized cars, G_l is all large-sized cars, G_b is all buses, and the counting is directly given by a thunder all-in-one machine acquisition system;

Lambda_s、λ_r、λ_l、λ_b is the space occupation coefficient of a car, a medium-sized car, a large-sized car and a bus respectively, and reflects the occupation degree of various vehicles on road areas; determining bus priority, namely lambda_b value, according to traffic flow rate condition;

delta_s、δ_r、δ_l、δ_b is the road traffic capacity occupancy coefficient of a car, a medium-sized car, a large-sized car and a bus respectively;

S32, determining a road traffic capacity occupancy coefficient: according to the average speed of the laneNormalizing the average speed of the target vehicle, wherein the influence of the vehicle on the traffic capacity is smaller as the average speed of the lane is closer; based on the average speed value, the occupancy coefficient is lower as the speed limit value is approached, and the occupancy coefficient is higher as the speed limit value is approached to 0 km/h; in order to avoid the overlarge disturbance of the coefficient, the value of the coefficient is restrained; the following coefficient calculation formula is therefore available:

s33, determining a signal control scheme: according to the calculation formula in the step S32, the accurate traffic flow q_i of each key lane can be obtained, and the classical British TRRL method is referenced for signal timing calculation to generate a phase scheme;

In one period, the central controller optimizes the real-time reading track and then controls the running states of all vehicles at the intersection by using webster method based on improved standard car equivalent;

The step S33 includes:

s41, calculating the total flow ratio of the key lanes: the total flow ratio is calculated according to the traffic volume of each lane under the improvement given in the step 3.2, and the calculation method is as follows:

I is an index of the phase, and is specifically set according to the actual running condition of the intersection; The flow ratio of k lanes in the i phase is calculated as follows:

Wherein the method comprises the steps ofThe specific data of the saturated flow rate of k lanes in the i phase is determined according to the related traffic specification standard;

s42, optimal period duration: the period duration of TRRL method signal control should be calculated according to the following formula:

40s≤C₀≤180s

Where L is defined as the total lost time of the signal, and its calculation formula is as follows:

wherein L_s is defined as start-up lost time; AR is the clearing time of the full red intersection of the i phase;

S43, calculating effective green light time: the optimal period duration C₀ is distributed according to the accurate flow ratio of each phase, and the calculation method is as follows:

Generating a phase combination G= [ G₁,g₂,g₃, … ] by the effective green light time for intersection signal control of the next period; and when the next period starts, returning to the processing module to continue the intelligent vehicle track optimization under the information control scheme based on the optimization module.

3. An intelligent network-connected hybrid traffic flow intersection signal collaborative optimization device, the device comprising:

A communication bus for realizing connection communication between the processor and the memory;

a memory for storing a computer program;

a processor for executing the computer program to perform the steps of:

The traffic state information is collected with high precision, wherein the traffic state information comprises target vehicle running state data and an initial information control scheme;

optimizing the intelligent vehicle track based on initial information control, wherein the intelligent vehicle track comprises intersection partition definition, intelligent vehicle team grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application;

Optimizing based on the optimized signal control of the intelligent vehicle track, including optimizing standard car equivalent parameters, determining road traffic capacity occupancy coefficients and determining a signal control scheme;

the intelligent vehicle track based on initial information control is optimized, and comprises intersection partition definition, intelligent vehicle team grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application, wherein the vehicle speed guide strategy application comprises the following steps:

s21, intersection partition definition: partitioning the detection range L of the collector, wherein the detection range L mainly comprises a motorcade decision area L_D and a speed guide area L_V; the main function of the motorcade marshalling area is to marshal intelligent network vehicles and manual driving vehicles; the main function of the speed guiding area is to apply a speed control strategy to realize real-time control of an intelligent motorcade, and the length L_V is required to be smaller than the solid line area L_limit of the entrance way in order to ensure traffic safety; the composition and constraints are as follows:

L＝L_D+L_V

L_V<L_limit；

S22, intelligent motorcade grouping: because of the man-machine driving environment, the intelligent network-connected vehicle exists in the intersection according to a certain permeability; taking the intelligent network-connected vehicle CAV as a head vehicle CAV_l^m of a grouped fleet in a fleet decision area L_D, wherein m is a fleet number of the head vehicle, and L is a lane number of the head vehicle; a plurality of manual driving vehicles HV_j^m which follow the intelligent network train consist of an intelligent network train M_n together with a head car CAV_l^m, wherein M is the serial number of the train, and j is the serial number of the manual driving vehicle in the train;

S23, calculating a speed guide area boundary: determining an influence range of the vehicle which is started to be incorporated into the speed control strategy by the centralized controller, different from the detection boundary of the detector; the judgment condition is an acceleration extremum a_c which can be accepted by a driver, and a comfortable braking distance S_c is calculated according to the acceleration extremum a_c and a road limiting speed V_L; the calculation formula is as follows:

Determining a boundary of the speed guide area L_V by taking an intersection stop line as a reference according to the calculated comfortable braking distance S_c; if S_c>S_limit, taking the boundary distance as S_limit;

S24, building a following model: because part of manual driving vehicles exist in the motorcade, the driving characteristics of Mn head vehicles of the intelligent network motorcade can induce the manual driving vehicles to pass through the intersection or stop at the same target speed; because the traffic collection system based on the thunder integrated machine can obtain the running state information of all vehicles including the manual driving vehicles, an improved FVD full speed difference following model based on a fleet can be applied to obtain the following acceleration between the head car CAV_l^m+1 of the fleet and the last vehicle HV_j^m of the fleet, and the calculation formula is as follows:

a_m+1＝α(V′_m-V_m(t))+β(V_m+1(t)-V_m(t))

Wherein: a_m+1 -the first vehicle heel-off acceleration in the m+1 fleet;

V'_m -the target vehicle speed of the head vehicle of the m vehicle fleets;

v_m (t) -the speed of the tail car of the m motorcade at the moment t;

V_m+1 (t) -t time m+1 fleet head speed;

alpha-the driver sensitivity coefficient; beta-driver reaction coefficient;

S25, vehicle speed guiding strategy application: when the intelligent network train M_n reaches the boundary of the speed guiding area L_V, judging the phase, wherein the running direction signal lamp is a red-yellow lamp phase P_r, and the rest time is set as T_r; or green light phase P_g, the remaining time of which is set to T_g; different vehicle speed guiding strategies are adopted aiming at different phase states, and t_r is set as the response time of a driver;

Wherein the centralized controller performs trajectory optimization only in the absence of downstream queuing vehicles or in the absence of disturbances of queuing vehicles:

1) When (when)Or (b)When the vehicle is started, taking a target vehicle speed V'_m＝V_max; the following acceleration of the rear fleet is: a_m+1＝α(V_max-V_m(t))+β(V_m+1(t)-V_m (t));

2) When (when)When the vehicle speed V_m is reduced to the target vehicle speed V '_m, the time when the motorcade reaches the stop line is at the green light starting time, and the solving mode of V'_m is as follows:

Wherein: v_min —road minimum speed limit; v_max -road highest speed limit;

The optimized signal control based on the intelligent vehicle track comprises standard car equivalent parameter optimization, road traffic capacity occupation coefficient determination, and signal control scheme determination comprises the following steps:

s31, optimizing equivalent parameters of a standard car: based on the accuracy that the thunder all-in-one obtained to the vehicle running state, can utilize vehicle type, vehicle size, occupation space, the running speed of the interior operation vehicle of crossing to improve car equivalent conversion coefficient:

wherein: For the converted total traffic of k lanes, G_s is all cars, G_r is all medium-sized cars, G_l is all large-sized cars, G_b is all buses, and the counting is directly given by a thunder all-in-one machine acquisition system;

Lambda_s、λ_r、λ_l、λ_b is the space occupation coefficient of a car, a medium-sized car, a large-sized car and a bus respectively, and reflects the occupation degree of various vehicles on road areas; determining bus priority, namely lambda_b value, according to traffic flow rate condition;

delta_s、δ_r、δ_l、δ_b is the road traffic capacity occupancy coefficient of a car, a medium-sized car, a large-sized car and a bus respectively;

S32, determining a road traffic capacity occupancy coefficient: according to the average speed of the laneNormalizing the average speed of the target vehicle, wherein the influence of the vehicle on the traffic capacity is smaller as the average speed of the lane is closer; based on the average speed value, the occupancy coefficient is lower as the speed limit value is approached, and the occupancy coefficient is higher as the speed limit value is approached to 0 km/h; in order to avoid the overlarge disturbance of the coefficient, the value of the coefficient is restrained; the following coefficient calculation formula is therefore available:

s33, determining a signal control scheme: according to the calculation formula in the step S32, the accurate traffic flow q_i of each key lane can be obtained, and the classical British TRRL method is referenced for signal timing calculation to generate a phase scheme;

In one period, the central controller optimizes the real-time reading track and then controls the running states of all vehicles at the intersection by using webster method based on improved standard car equivalent;

The step S33 includes:

s41, calculating the total flow ratio of the key lanes: the total flow ratio is calculated according to the traffic volume of each lane under the improvement given in the step 3.2, and the calculation method is as follows:

I is an index of the phase, and is specifically set according to the actual running condition of the intersection; The flow ratio of k lanes in the i phase is calculated as follows:

Wherein the method comprises the steps ofThe specific data of the saturated flow rate of k lanes in the i phase is determined according to the related traffic specification standard;

s42, optimal period duration: the period duration of TRRL method signal control should be calculated according to the following formula:

40s≤C₀≤180s

Where L is defined as the total lost time of the signal, and its calculation formula is as follows:

wherein L_s is defined as start-up lost time; AR is the clearing time of the full red intersection of the i phase;

S43, calculating effective green light time: the optimal period duration C₀ is distributed according to the accurate flow ratio of each phase, and the calculation method is as follows:

Generating a phase combination G= [ G₁,g₂,g₃, … ] by the effective green light time for intersection signal control of the next period; and when the next period starts, the intelligent vehicle track controlled based on the initial information is returned to be optimized, the steps of intersection partition definition, intelligent vehicle team grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application continue to be optimized based on the optimized signal control of the intelligent vehicle track, the optimization comprises standard car equivalent parameter optimization, road traffic capacity occupation coefficient determination, and the intelligent vehicle track optimization under the signal control scheme is given by the signal control scheme determination step.

4. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to claim 1.