技术领域technical field
本发明涉及一种车联网环境下的车辆编队初始方案确定方法，是一种用于在车联网环境下，对在由普通人工驾驶车辆和自动驾驶网联车辆构成的混合交通流中，欲形成队列的若干自动驾驶车辆进行初始编队方案确定的方法。The invention relates to a method for determining an initial plan of vehicle formation in the environment of the Internet of Vehicles, which is a method for determining the initial scheme of vehicle formation in the environment of the Internet of Vehicles. A method for determining an initial formation scheme for several self-driving vehicles in a queue.
背景技术Background technique
自动驾驶网联车辆是能够执行自动驾驶功能的车辆，此外车辆之间、车辆和路侧设备之间可以相互通信，传输的信息包括车辆的位置、运动状态等。车辆的编队行驶是车联网的重要应用之一。编队之后，一系列车辆跟随队列中的头车行驶，整体风阻降低、后部的车辆的运动学控制可以过渡给首车。因此，编队行驶能够带来巨大的效益。Autonomous driving networked vehicles are vehicles that can perform automatic driving functions. In addition, vehicles, vehicles and roadside equipment can communicate with each other, and the information transmitted includes the location and motion status of the vehicle. The platooning of vehicles is one of the important applications of the Internet of Vehicles. After formation, a series of vehicles follow the leading vehicle in the formation, the overall wind resistance is reduced, and the kinematic control of the rear vehicles can be transferred to the leading vehicle. Therefore, driving in formation can bring huge benefits.
然而，道路系统中，不仅包含自动驾驶网联车，还包括普通的人工驾驶车辆。当自动驾驶网联车提出组队申请时，这些车辆的位置各异，能否全部加入编队以及它们在队列中的顺序受限于组队申请提出时的相互空间关系。However, the road system includes not only self-driving connected vehicles, but also ordinary human-driven vehicles. When the self-driving networked vehicles apply for formation, the positions of these vehicles are different, whether they can all join the formation and their order in the formation are limited by the mutual spatial relationship when the formation application is made.
发明内容Contents of the invention
为了将多车道道路上的多个提出组队申请的自动驾驶网联车编为队列，本发明提出一种初始编队方法。该方法适用于混合交通流环境，也即由人工驾驶车辆和自动驾驶网联车形成的交通流。In order to form a plurality of autonomous driving networked vehicles on a multi-lane road that have applied for forming a formation into a formation, the present invention proposes an initial formation method. The method is suitable for mixed traffic flow environments, that is, traffic flows formed by human-driven vehicles and autonomous connected vehicles.
本发明解决其技术问题所采用的技术方案是，The technical scheme that the present invention solves its technical problem adopts is,
一种车联网环境下的车辆编队初始方案确定方法，道路上运行的多个自动网联车辆提出组队申请，通过下述流程确定编队初始方案，并将编队方案信息发送至各个自动驾驶网联车辆：该方法根据车道数量信息、混合交通流中的车辆位置信息形成车辆可达图，计算获得各车道容许的队列长度，确定编队初始方案，具体包括下列步骤：A method for determining an initial vehicle formation plan in an Internet of Vehicles environment. Multiple automatic networked vehicles running on the road submit a teaming application, determine the initial formation plan through the following process, and send the formation plan information to each automatic driving networked vehicle. Vehicles: This method forms a vehicle reachable map based on the information on the number of lanes and the position information of vehicles in the mixed traffic flow, calculates the allowable queue length of each lane, and determines the initial formation plan, specifically including the following steps:
1)利用车辆的横向和纵向信息形成车辆位置的拓扑图；1) Using the lateral and longitudinal information of the vehicle to form a topological map of the vehicle position;
2)根据拓扑图，考虑人工驾驶车辆的安全条件，形成车辆可达图；2) According to the topology map, consider the safety conditions of the manually driven vehicle to form a vehicle reachable map;
3)计算每个车道容许的队列长度，取最大者作为编队的长度，并按照自动驾驶车辆的纵向位置进行队列编号，最终形成队列。3) Calculate the allowable queue length of each lane, take the largest one as the length of the formation, and number the queue according to the longitudinal position of the autonomous vehicle, and finally form a queue.
上述技术方案中，步骤1)中获取车道数量以及车辆类型、各车辆所在车道及纵向位置，所述的车辆类型分为自动驾驶网联车辆和普通人工驾驶车辆，并将未提出编队申请的自动驾驶网联车辆一律视为人工驾驶车辆；每个车辆作为一个节点，形成车辆位置拓扑图，拓扑图中一车辆对应一行，每个车道对应图中一列。In the above technical solution, in step 1), the number of lanes, the vehicle type, the lane and the longitudinal position of each vehicle are obtained, and the vehicle types are divided into automatic driving networked vehicles and ordinary manual driving vehicles, and the automatic driving vehicles that have not submitted a formation application All connected vehicles are regarded as human-driven vehicles; each vehicle acts as a node to form a topological map of the vehicle location, one vehicle corresponds to a row in the topological map, and each lane corresponds to a column in the map.
步骤2)中考虑每一辆人工驾驶车辆的安全要求，即车辆前面的净空和后面的净空要求，在拓扑图中位于各人工驾驶车辆前净空处及后净空处分别添加虚拟行，在所形成的区域中，将人工驾驶车辆对应的节点和与该节点直接相连的连线从拓扑图中去除，形成车辆可达图。In step 2), consider the safety requirements of each human-driven vehicle, that is, the clearance requirements in front of the vehicle and the clearance requirements at the rear, and add virtual lines respectively at the front clearance and rear clearance of each manual driving vehicle in the topology map, and form In the area of , the node corresponding to the human-driven vehicle and the line directly connected to the node are removed from the topological graph to form a vehicle reachable graph.
步骤3)中将提出编队申请的所有车辆中最前面的车辆定为首车，纵向位置设为零，其他车辆的纵向位置为相对于首车的位置的纵向距离，按照自动驾驶网联车辆要求的最小间距lmin，计算各车道容许的队列长度n＝L/lmin，L为车辆可达图中从首车对应行起算至该车道断裂处线段对应车道的长度，取各车道队列长度最大值作为编队的长度，按照自动驾驶网联车辆的纵向位置从近到远依次进行队列编号，最终形成队列。In step 3), the frontmost vehicle among all the vehicles applying for formation is set as the first vehicle, and the longitudinal position is set to zero. The longitudinal positions of other vehicles are the longitudinal distances relative to the position of the first vehicle, according to the requirements of the automatic driving networked vehicles. The minimum distance is lmin , calculate the allowable queue length of each lane n=L/lmin , L is the length from the line corresponding to the first vehicle in the vehicle reachable diagram to the lane corresponding to the line segment where the lane breaks, and take the maximum queue length of each lane As the length of the formation, the queues are numbered in sequence according to the longitudinal position of the self-driving networked vehicles from near to far, and finally form a queue.
步骤3)中如果编队的长度大于提出编队申请的自动驾驶网联车数，则所有申请车辆都可以加入队列，否则按照编队长度由前向后进入队列，其余车辆申请加入队列失败。In step 3), if the length of the formation is greater than the number of self-driving networked vehicles applying for the formation, all the vehicles applying for the formation can join the queue; otherwise, they enter the queue from front to back according to the length of the formation, and other vehicles fail to apply for joining the queue.
本发明的有益效果是：The beneficial effects of the present invention are:
采用本发明的方法可以为自动驾驶网联车环境下提供初始编队方案，并且队列的长度能够最大化。Using the method of the present invention can provide an initial formation scheme for the environment of autonomous driving networked vehicles, and the length of the formation can be maximized.
附图说明Description of drawings
图1为多车道交通流运行图；Figure 1 is a multi-lane traffic flow diagram;
图2为车辆位置拓扑图；Fig. 2 is a topological diagram of the vehicle position;
图3为车辆可达图；Figure 3 is a vehicle reachable map;
图4为队列最大车辆数和队列编号示意图；Fig. 4 is a schematic diagram of the maximum number of vehicles in the queue and the serial numbers of the queue;
图5为执行编队方案完毕之后的示意图；Fig. 5 is the schematic diagram after carrying out formation plan;
图6为本发明方法的技术流程示意图。Fig. 6 is a schematic diagram of the technical flow of the method of the present invention.
具体实施方式Detailed ways
下面结合附图对本发明做进一步的详细描述。The present invention will be described in further detail below in conjunction with the accompanying drawings.
图1所示为一多车道道路，其中运行的车辆包括自动驾驶网联车和普通的人工驾驶车辆。如果某辆自动驾驶网联车没有提出编队申请，或者其没有编入队列的要求，则将其视为普通的人工驾驶车辆。Figure 1 shows a multi-lane road, where the vehicles running include autonomous driving connected vehicles and ordinary human-driven vehicles. If a self-driving networked vehicle does not apply for formation, or if it is not required to be included in the formation, it will be regarded as an ordinary human-driven vehicle.
当多个自动网联车辆提出编队申请时，首先获得车道数量的信息和所有车辆的信息。车道数量也即所在路段的可用车道的数量，车辆的信息包括车辆所在车道、车辆的类型(自动驾驶网联车辆或者普通人工驾驶车辆)、车辆的纵向位置。其中，车辆的纵向位置确定如下：将提出编队申请的所有车辆中最前面的车辆定为首车，纵向位置设为零，后续的所有自动驾驶网联车辆的纵向位置为其和首车的纵向距离。获取所有车辆信息之后，形成车辆位置的拓扑图。每一辆车，不管其是自动驾驶网联车或者普通的人工驾驶车辆，在车辆位置格点图中，都有对应的节点；如图2所示。拓扑图为棋盘形，每辆车横向位置作为行，每条车道作为列。When multiple automatic networked vehicles apply for platoon formation, the information on the number of lanes and all vehicles is first obtained. The number of lanes is the number of available lanes on the road section. The information of the vehicle includes the lane where the vehicle is located, the type of vehicle (autonomous driving networked vehicle or ordinary human-driven vehicle), and the longitudinal position of the vehicle. Among them, the longitudinal position of the vehicle is determined as follows: the front vehicle among all vehicles applying for formation is set as the first vehicle, and the longitudinal position is set to zero, and the longitudinal position of all subsequent autonomous driving networked vehicles is the longitudinal distance from the first vehicle . After obtaining all vehicle information, a topological map of vehicle locations is formed. Every vehicle, no matter whether it is a self-driving networked vehicle or an ordinary human-driven vehicle, has a corresponding node in the vehicle location grid map; as shown in Figure 2. The topological map is a checkerboard, with the lateral position of each vehicle as a row and each lane as a column.
形成拓扑图之后，依据普通人工驾驶车辆的安全距离要求(包括人工驾驶车辆的前部净空距离lh和后部净空距离lr，参数根据需求设定)，在拓扑图中每一个人工驾驶车辆的前部净空距离处和后部净空距离处添加虚拟行，在所形成的区域中将人工驾驶车辆节点和与该节点直接相连的连线去除，形成车辆可达图，如图3所示。依据车辆可达图，可以得到某辆自动驾驶车辆在多车道道路上可以活动的空间范围。After the topological map is formed, according to the safety distance requirements of ordinary human-driven vehicles (including the front clearance distance lh and the rear clearance distance lr of the human-driven vehicle, the parameters are set according to the requirements), each human-driven vehicle in the topology map Add virtual lines at the front clearance distance and rear clearance distance of , and remove the human-driven vehicle node and the connection line directly connected to the node in the formed area to form a vehicle reachable graph, as shown in Figure 3. According to the vehicle reachability map, the spatial range of an autonomous vehicle on a multi-lane road can be obtained.
依据自动驾驶网联车辆的平均车头时距要求，计算每条车道上首车以及其所在行每一个节点后部可以容纳多少辆车。容纳车辆数按照可活动空间长度L(即可达图中各车道自首行起距断裂处线段对应的车道长度)除以最小车头间距(lmin，根据需求设定)来计算。以图 4中的最右侧车道为例，其长度为L，则容纳车辆数长度计算方法为队列最大容许车辆数选择所有队列容纳车辆数的最大值，以图4为例，也即n1～n4之间的最大者，可以看出n2＝n4。容纳车辆数确定之后，将自动驾驶车辆按照纵向位置先后顺序，进行编号，如图4所示。如果队列最大容纳车辆数n4多于自动驾驶网联车数，则所有申请车辆都可以加入队列，否则按照前n4辆车可以进入队列，其余车辆申请加入队列失败。According to the average headway requirements of self-driving networked vehicles, calculate how many vehicles can be accommodated by the first vehicle in each lane and the rear of each node in its row. The number of accommodated vehicles is calculated by dividing the length of the movable space L (that is, the length of each lane in the reachable map from the first row to the line segment at the break) divided by the minimum distance between vehicles (lmin , set according to requirements). Taking the rightmost lane in Figure 4 as an example, its length is L, and the calculation method for the length of the number of vehicles accommodated is The maximum allowable number of vehicles in a queue selects the maximum number of vehicles accommodated in all queues, taking Figure 4 as an example, that is, the largest one between n1 and n4 , it can be seen that n2 =n4 . After the number of accommodated vehicles is determined, the self-driving vehicles are numbered according to their longitudinal positions, as shown in Figure 4. If the maximum number of vehicles n4 that can be accommodated in the queue is more than the number of autonomous driving networked vehicles, all the application vehicles can join the queue, otherwise the first n4 vehicles can enter the queue, and the remaining vehicles fail to apply for joining the queue.
至此，自动驾驶网联车辆的初始队列编排就确定了，最终的队列形成如图5所示。So far, the initial platoon arrangement of autonomous driving networked vehicles has been determined, and the final platoon formation is shown in Figure 5.
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| CN201811488568.7ACN109859456B (en) | 2018-12-06 | 2018-12-06 | Determination method of initial scheme of vehicle formation under Internet of Vehicles environment |
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| CN201811488568.7ACN109859456B (en) | 2018-12-06 | 2018-12-06 | Determination method of initial scheme of vehicle formation under Internet of Vehicles environment |
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| CN201811488568.7AActiveCN109859456B (en) | 2018-12-06 | 2018-12-06 | Determination method of initial scheme of vehicle formation under Internet of Vehicles environment |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110261856B (en)* | 2019-07-31 | 2021-06-11 | 北京邮电大学 | Radar detection method and device based on multi-radar cooperative detection |
| CN110992676B (en)* | 2019-10-15 | 2021-06-04 | 同济大学 | A method for estimating road capacity and equivalence coefficient of connected autonomous vehicles |
| CN112750297B (en)* | 2019-10-31 | 2022-09-16 | 广州汽车集团股份有限公司 | A fleet control method, dispatch processing system, vehicle and control system |
| CN112835352B (en)* | 2019-11-25 | 2022-10-04 | 华为云计算技术有限公司 | Formation control method and device, vehicle, electronic device, storage medium |
| CN111161526B (en)* | 2020-01-02 | 2023-10-20 | 腾讯科技（深圳）有限公司 | Vehicle formation method, device, system and medium |
| CN111325967B (en)* | 2020-02-28 | 2020-12-25 | 清华大学 | Intelligent networking automobile formation control method and device based on cooperative assignment |
| CN111445690A (en)* | 2020-03-03 | 2020-07-24 | 北京汽车集团有限公司 | Vehicle team driving method, vehicle and system |
| CN113734169A (en)* | 2020-12-16 | 2021-12-03 | 江铃汽车股份有限公司 | Car following control method and system based on vehicle-road cooperation |
| CN113401234B (en)* | 2021-06-11 | 2023-05-02 | 上海振华重工(集团)股份有限公司 | Method and system for mixing automatic driving vehicle and manual driving vehicle |
| CN113823079B (en)* | 2021-10-27 | 2022-08-16 | 大连理工大学 | Formation control method for manned/unmanned vehicles based on vehicle-road cooperation |
| CN115578849B (en)* | 2022-09-28 | 2023-08-29 | 东南大学 | An optimization method for centralized formation of autonomous vehicles in a dedicated lane environment |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3906768B2 (en)* | 2002-09-03 | 2007-04-18 | 株式会社デンソー | Convoy travel system |
| CN106408930A (en)* | 2015-07-31 | 2017-02-15 | 中国联合网络通信集团有限公司 | Method and apparatus for creating driving fleet |
| CN105809950A (en)* | 2016-03-28 | 2016-07-27 | 重庆邮电大学 | Vehicle fleet forming method and system based on vehicle road collaboration technology |
| US9940840B1 (en)* | 2016-10-06 | 2018-04-10 | X Development Llc | Smart platooning of vehicles |
| US10372123B2 (en)* | 2016-12-30 | 2019-08-06 | Bendix Commercial Vehicle Systems Llc | “V” shaped and wide platoon formations |
| CN106600952A (en)* | 2017-01-24 | 2017-04-26 | 中国科学院自动化研究所 | Method for vehicle formation forming |
| CN107010064B (en)* | 2017-03-24 | 2019-06-14 | 奇瑞汽车股份有限公司 | A kind of fleet's formation drive manner and system |
| CN108388247B (en)* | 2018-01-30 | 2020-11-10 | 上海交通大学 | A formation driving method of unmanned vehicles based on directed communication network |
| Publication number | Publication date |
|---|---|
| CN109859456A (en) | 2019-06-07 |
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