技术领域technical field
本发明涉及一种辅助驾驶技术领域，尤其涉及一种激进型辅助驾驶弯道避障换道路径规划系统及方法。The invention relates to the technical field of driving assistance, in particular to a path planning system and method for aggressive driving assistance in curves, obstacle avoidance and lane change.
背景技术Background technique
随着人工智能时代的来临，智能车辆已成为当今的研究热点。智能车辆上路最大的问题是安全问题，汽车主动安全技术也越来越受到人们的关注。With the advent of the era of artificial intelligence, intelligent vehicles have become a research hotspot today. The biggest problem for smart vehicles on the road is safety, and the active safety technology of automobiles has attracted more and more attention.
根据驾驶人的驾驶行为的不同，可以分为谨慎型、正常型和激进型三种。据研究表明:驾驶风格越激进,驾驶人对周围环境关注越少,对车辆的横向控制稳定性越差,换道发生的频次越高，激进型驾驶人往往不满足跟随前方慢车行驶，而是通过频繁换道来获得速度优势。According to the driver's driving behavior, it can be divided into three types: cautious type, normal type and aggressive type. According to research, the more aggressive the driving style, the less the driver pays attention to the surrounding environment, the worse the stability of the lateral control of the vehicle, and the higher the frequency of lane changes. The aggressive driver is often not satisfied with following the slow car ahead, but Gain a speed advantage by changing lanes frequently.
有效的辅助驾驶避障系统需具备合理的传感器布置、特定工况下的安全距离模型以及能兼顾稳定性与实时性的避障策略。目前，多数关于紧急避障的研究所使用的方法都是根据与前车不同的距离与安全距离作比较选择制动、制动与转向组合或转向的避障方法。在紧急避障方法选择中，当检测到相邻车道无车辆时可通行时，若进行紧急制动停车避障，则可能对后方车辆产生惊吓，甚至造成交通事故，同时也降低了车辆的通行效率。An effective assisted driving obstacle avoidance system requires a reasonable sensor arrangement, a safe distance model under specific working conditions, and an obstacle avoidance strategy that can balance stability and real-time performance. At present, the methods used in most researches on emergency obstacle avoidance are to select braking, braking and steering combination or steering obstacle avoidance methods according to the distance from the vehicle in front and the safety distance. In the selection of emergency obstacle avoidance methods, when it is detected that there is no vehicle in the adjacent lane, if emergency braking is performed to avoid obstacles, it may frighten the vehicles behind, or even cause traffic accidents, and also reduce the traffic speed of vehicles. efficiency.
所以为满足激进型驾驶员对于速度的追求，当检测到自车车道前方异常时，在保证相邻车道无障碍物的情况下，可以实时规划出一条合适的路径，选择直接进行转向的方法来进行避障，提高交通效率，保证安全性。Therefore, in order to meet the aggressive driver's pursuit of speed, when an abnormality is detected in the front of the vehicle lane, a suitable path can be planned in real time and a direct steering method can be selected to ensure that the adjacent lane is free of obstacles. Avoid obstacles, improve traffic efficiency, and ensure safety.
发明内容Contents of the invention
发明目的：本发明所要解决的技术问题是针对背景技术中所涉及的缺陷，提出一种激进型辅助驾驶弯道避障换道路径规划系统及。Purpose of the invention: The technical problem to be solved by the present invention is to propose a path planning system for radical assisted driving, curve obstacle avoidance and lane change in view of the defects involved in the background technology.
技术方案：Technical solutions:
一种激进型辅助驾驶弯道避障换道路径规划系统，包括环境感知单元、自车传感器单元、CAN总线、电子控制单元即VCU、线控转向单元、线控制动单元、速度控制单元；所述环境感知单元包括摄像头、激光雷达以及毫米波雷达；所述自车传感器单元包括车速传感器、加速度传感器、前轮转角传感器；所述摄像头安装在车辆挡风玻璃正上方，用于识别车道线信息、障碍物信息、车道限速，并将图像信息传入VCU；所述激光雷达，数量至少2个(保证前端检测的安全性，防止单一雷达失效造成危险)，分别安装在前端舱盖上和车顶上，用于检测前方障碍物与自车之间的相对距离以及前方车辆的速度、加速度，并将信息存储于CAN总线上，供VCU实时调取和处理；所述毫米波雷达，数量至少1个，安装在车辆前部的进气隔栅，用于检测远距离车辆与自车的相对距离，并将信息存储于CAN总线上，供VCU实时调取和处理；所述车速传感器、加速度传感器与前轮转角传感器，分别用于收集车辆的速度、纵向加速度、前轮转角，并将信息存储于CAN总线上，供VCU实时调取和处理；所述线控转向单元包含转向助力电机以及转向控制器，用于接收VCU的转向信号，进行转向；所述线控制动单元包含制动轮缸，用于接收VCU的制动信号，进行制动；所述轮速控制单元包含车轮电机，用于接收VCU的轮速信号，对车速进行控制；所述电子控制单元VCU实现计算、判断、发出控制信号功能，用于根据接收到的自车的车速、纵向加速度以及前方车辆的速度、加速度计算自车和前方车辆之间的安全距离后，将计算得到的安全距离作为判断是否进行转向的依据；同时，当前车距离达到安全距离后，VCU计算模块计算得到换道路径以及换道所需的前轮转角、自车速度、纵向加速度后，分别与自车实时的前轮转角、自车车速、加速度进行比较，依据比较得到的差值来调节转向助力电机的输入电流、制动轮缸的压力以及车轮速度，使得线控转向单元、线控制动单元、车轮速度单元工作。A path planning system for aggressive assisted driving with corner avoidance, obstacle avoidance, and lane change, including an environment perception unit, a self-vehicle sensor unit, a CAN bus, an electronic control unit (VCU), a steer-by-wire unit, a brake-by-wire unit, and a speed control unit; The environment sensing unit includes a camera, laser radar and millimeter wave radar; the vehicle sensor unit includes a vehicle speed sensor, an acceleration sensor, and a front wheel angle sensor; the camera is installed directly above the vehicle windshield for identifying lane line information , obstacle information, lane speed limit, and the image information is transmitted to the VCU; the number of the laser radars is at least 2 (to ensure the safety of the front-end detection and prevent the danger caused by a single radar failure), which are respectively installed on the front hatch and On the roof of the vehicle, it is used to detect the relative distance between the obstacle in front and the own vehicle, as well as the speed and acceleration of the vehicle in front, and store the information on the CAN bus for real-time retrieval and processing by the VCU; the millimeter-wave radar, the quantity At least one, installed on the air intake grille at the front of the vehicle, used to detect the relative distance between the long-distance vehicle and the own vehicle, and store the information on the CAN bus for real-time retrieval and processing by the VCU; the vehicle speed sensor, The acceleration sensor and the front wheel angle sensor are respectively used to collect the speed, longitudinal acceleration, and front wheel angle of the vehicle, and store the information on the CAN bus for real-time retrieval and processing by the VCU; the steering-by-wire unit includes a power steering motor and a steering controller for receiving a steering signal from the VCU to perform steering; the brake-by-wire unit includes a brake wheel cylinder for receiving a braking signal from the VCU to perform braking; the wheel speed control unit includes a wheel motor , used to receive the wheel speed signal of the VCU to control the vehicle speed; the electronic control unit VCU realizes the functions of calculating, judging, and sending control signals, and is used to receive the vehicle speed, longitudinal acceleration of the own vehicle and the speed of the vehicle ahead, After the acceleration calculates the safe distance between the self-vehicle and the vehicle in front, the calculated safe distance is used as the basis for judging whether to turn; at the same time, after the current vehicle distance reaches the safe distance, the VCU calculation module calculates the lane-changing path and the lane-changing location. After the required front wheel angle, vehicle speed, and longitudinal acceleration are compared with the real-time front wheel angle, vehicle speed, and acceleration of the vehicle, the input current of the power steering motor and the brake wheel are adjusted according to the difference obtained from the comparison. The pressure of the cylinder and the speed of the wheels make the steering-by-wire unit, brake-by-wire unit and wheel speed unit work.
一种激进型辅助驾驶弯道避障换道路径规划方法，包括如下步骤：A path planning method for aggressive assisted driving in curves, obstacle avoidance, and lane change, comprising the following steps:
步骤1)，摄像头采集车道线信息、障碍物信息以及车道限速，将识别的自车所在车道的两车道线曲率、障碍物位置信息、车道的限速传到CAN总线上，分别供VCU调取用于计算和判断；激光雷达采集前方障碍物的相关信息，将采集的前方障碍物和自车之间的相对距离以及前方车辆的速度传到CAN总线上，分别供VCU调取用于判断和计算；Step 1), the camera collects lane line information, obstacle information, and lane speed limit, and transmits the recognized two-lane line curvature, obstacle position information, and lane speed limit of the lane where the self-vehicle is located to the CAN bus for VCU adjustment. It is used for calculation and judgment; the laser radar collects the relevant information of the obstacles in front, and transmits the relative distance between the obstacles in front and the vehicle in front and the speed of the vehicle in front to the CAN bus, which are respectively called by the VCU for judgment and calculate;
步骤2)，车速传感器、加速度传感器、前轮转角传感器分别收集汽车的车速、加速度、前轮转角，并将收集到的数据输入到CAN总线上和供电子VCU进行计算；Step 2), the vehicle speed sensor, the acceleration sensor, and the front wheel angle sensor respectively collect the vehicle speed, acceleration, and front wheel angle of the car, and input the collected data to the CAN bus and supply the electronic VCU for calculation;
步骤3)，VCU根据接收到的前方车辆的车速、自车车速、加速度建立制动安全距离模型，根据Mazda制动安全距离模型计算汽车和前方车辆之间的安全距离：Step 3), the VCU establishes a braking safety distance model according to the received vehicle speed, vehicle speed, and acceleration of the vehicle in front, and calculates the safety distance between the vehicle and the vehicle in front according to the Mazda braking safety distance model:
其中，Vh为自车速度，vrel为前车与自车的相对速度，μ为路面辅助系数，g为重力加速度，t1为驾驶员反应延迟时间,t2为制动器延迟时间，d0为最小停车距离，并将计算的到的安全距离传到CAN总线上，供VCU进行调取和判断；Among them, Vh is the speed of the vehicle, vrel is the relative speed of the vehicle in front and the vehicle in front, μ is the road surface assistance coefficient, g is the acceleration of gravity, t1 is the driver’s reaction delay time, t2 is the brake delay time, d0 It is the minimum parking distance, and the calculated safe distance is transmitted to the CAN bus for VCU to call and judge;
步骤4)，VCU通过判断自车道前方障碍物和自车之间的距离Sreal与安全距离Ssafe的大小，计算出差值a，a＝Sreal-Ssafe；Step 4), the VCU calculates the difference a by judging the distance Sreal and the safe distance Ssafe between the obstacle in front of the own lane and the vehicle, a=Sreal −Ssafe ;
步骤5)，摄像头检测道路信息，通过图像识别，得到车道限速为vlim,VCU通过判断得到Sreal≤S的结果后，S为需要测量操作的距离(Ssafe＜S＜传感器有效检测距离，由法规GBT20608-2006规定为75m)，判断前车速度vq与vlim*j1的大小(j1∈(0,1))，计算出差值b，b＝vq-vlim*j1；Step 5), the camera detects the road information, and through image recognition, the speed limit of the lane is vlim , after the VCU obtains the result of Sreal ≤ S through judgment, S is the distance that needs to be measured (Ssafe < S < the effective detection distance of the sensor , which is 75m according to the regulations GBT20608-2006), judge the size of the front vehicle speed vq and vlim *j1 (j1∈(0,1)), and calculate the difference b, b=vq -vlim *j1;
步骤6)，VCU通过判断得到Sreal≤S的结果后，通过判断前车是否紧急制动(紧急制动时，汽车的最大减速度一般为7.5－8m/s，普通制动时，汽车的平均减速度应为acom为3－4m/s)，计算前车减速度aq与max(acom)大小，即计算出差值c＝max(acom)-aq，max(acom)为普通制动时汽车平均减速度的最大值；Step 6), after the VCU obtains the result of Sreal ≤ S through judgment, it judges whether the vehicle in front brakes urgently (during emergency braking, the maximum deceleration of the vehicle is generally 7.5-8m/s, and during normal braking, the maximum deceleration of the vehicle The average deceleration should be acom is 3-4m/s), calculate the deceleration aq and max(acom ) of the vehicle in front, that is, calculate the difference c=max(acom )-aq , max(acom ) is the maximum value of the average deceleration of the vehicle during normal braking;
步骤7)，摄像头实时检测，前方障碍物左侧车道是否有障碍物，当差值a、b、c三者有一个小于等于0时，且当左侧车道无障碍物时，VCU计算出换道路径，为进行直接换道的决策做准备，当判断为假时，计算给出制动轮缸压力，进行制动；Step 7), the camera detects in real time whether there is an obstacle in the left lane of the front obstacle, when one of the differences a, b, and c is less than or equal to 0, and when there is no obstacle in the left lane, the VCU calculates The lane path is prepared for the decision to change lanes directly. When the judgment is false, the brake wheel cylinder pressure is calculated and braked;
步骤8)，VCU通过路径规划控制器，对方向盘转角进行控制，并计算转向角δ、速度v、制动踏板力F，传入线控转向单元、线控制动单元、速度控制单元进行控制。Step 8), the VCU controls the steering wheel angle through the path planning controller, and calculates the steering angle δ, speed v, and brake pedal force F, and transmits them to the steering-by-wire unit, brake-by-wire unit, and speed control unit for control.
进一步地，步骤5)中j1的确定，j1为自定义速度系数，由于激进型驾驶风格的驾驶员存在对外车道上慢速行驶车辆不耐烦而从内车道超车的行为，可对激进型驾驶风格的驾驶员进行驾驶行为分析，分析超车时前车速度与道路限制速度的比值，这n个数据为k1,k2,···kn,为保证数据的准确性，采用最小二乘法，使最小，为平均值，即为使D最小时的x，Further, in the determination of j1 in step 5), j1 is a self-defined speed coefficient. Since the aggressive driving style driver is impatient with slow-moving vehicles on the outer lane and overtakes from the inner lane, the aggressive driving style can Analyze the driving behavior of the driver, analyze the ratio of the speed of the vehicle in front and the speed limit of the road when overtaking, the n data are k1 , k2 ,... kn , in order to ensure the accuracy of the data, the least square method is used, Make minimum, is the average value, that is, x when D is minimized,
进一步地，步骤7)中，换道路径的确定方法为：Further, in step 7), the determination method of the lane-changing path is:
步骤7.1)，根据CAN总线中的路径曲率，以车辆后轮中心所在位置与右侧车道垂线的交点为原点(0,0)拟合出自车车道实时圆弧曲线，为Step 7.1), according to the path curvature in the CAN bus, the intersection point (0,0) of the center of the rear wheel of the vehicle and the vertical line of the right lane is used as the origin (0,0) to fit the real-time arc curve of the vehicle lane, which is
其中，R为道路曲率半径，x为换道纵向距离；Among them, R is the radius of curvature of the road, and x is the longitudinal distance of changing lanes;
设定车道宽度为dw，则自车所在路径公式为：Set the width of the lane as dw , then the path formula of the ego vehicle is:
步骤7.2)，通过道路宽度的关系，计算得到相邻车道道路中心的实时圆弧曲线，即目标换道路径公式为：Step 7.2), through the relationship of road width, calculate the real-time arc curve of the road center of the adjacent lane, that is, the formula of the target lane change path is:
步骤7.3)，通过五次多项曲线拟合出两车道中心圆弧曲线之间的换道路径，规划出换道路径；In step 7.3), the lane-changing path between the center arc curves of the two lanes is fitted by quintic polynomial curves, and the lane-changing path is planned;
车辆的换道结束目标点为(xs,ys),设换道路径方程为：The vehicle’s lane-changing end target point is (xs , ys ), and the lane-changing path equation is:
y(x)＝a0+a1x+a2x2+a3x3+a4x4+a5x5y(x)＝a0 +a1 x+a2 x2 +a3 x3 +a4 x4 +a5 x5
其中，a0，a1，a2，a3，a4，a5分别为五次多项式的系数；Among them, a0 , a1 , a2 , a3 , a4 , a5 are the coefficients of the quintic polynomial;
车辆在换道初始位置x＝0和结束位置x＝xs满足方程为：The vehicle satisfies the equation at the initial position x=0 and the end position x=xs of the lane change as follows:
其中，Rz为车道线中心线的曲率半径；Among them, Rz is the radius of curvature of the centerline of the lane line;
求解可得，a0，a1，a2，a3，a4，a5的值，假设纵向速度V不变，即xs＝V*t，则换道轨迹为：The values of a0 , a1 , a2 , a3 , a4 , and a5 can be obtained from the solution. Assuming that the longitudinal velocity V remains unchanged, that is, xs =V*t, the lane change trajectory is:
y(x)＝b0+b1t+b2t2+b3t3+b4t4+b5t5y(x)＝b0 +b1 t+b2 t2 +b3 t3 +b4 t4 +b5 t5
则：but:
C＝[V3 V4 V5] C＝[V3 V4 V5 ]
步骤7.4)，通过改变xs,即换道完成纵向距离，可得到不同的换道轨迹；Step 7.4), by changing xs , that is, the longitudinal distance to complete the lane change, different lane change trajectories can be obtained;
步骤7.5)，假设汽车的运动简单地服从于阿克曼几何关系，其中前轮转角由基础驾驶员模型得到，汽车轨迹曲率与方向盘转角成正比：Step 7.5), assuming that the motion of the car simply obeys the Ackermann geometric relation, where the front wheel angle is obtained from the basic driver model, and the curvature of the car trajectory is proportional to the steering wheel angle:
式中，L：轴距，V：纵向速度，为侧向加速度，δf为前轮转角。In the formula, L: wheelbase, V: longitudinal speed, is the lateral acceleration, and δf is the front wheel rotation angle.
步骤7.6)在不同车速下，根据基础驾驶员模型和轨迹二次求导得到的侧向加速度，得到期望前轮转角，在不同的轨迹条件下，根据运动学二自由度模型：Step 7.6) At different vehicle speeds, according to the basic driver model and the lateral acceleration obtained by the second derivation of the trajectory, the desired front wheel angle is obtained. Under different trajectory conditions, according to the kinematic two-degree-of-freedom model:
式中，K1为前轮总侧偏刚度，K2为后轮总侧偏刚度，Iz为绕z轴的转动惯量，m为汽车总质量，a为质心至前轴距离，b为质心至后轴距离vy为侧向速度，In the formula, K1 is the total cornering stiffness of the front wheels, K2 is the total cornering stiffness of the rear wheels, Iz is the moment of inertia around thez -axis, m is the total mass of the vehicle, a is the distance from the center of mass to the front axle, and b is the center of mass The distance vy to the rear axle is the lateral velocity,
计算得到横摆角速度和质心侧偏角；Calculate the yaw rate and the sideslip angle of the center of mass;
步骤7.7)设定目标函数为：Step 7.7) Set the objective function as:
式中：ld为换道轨迹的纵向长度ld＝xd，ay(t)、β(t)、ω(t)为换道过程中的侧向加速度、横摆角速度与质心侧偏角，aymax、βmax、ωmax为所有换道轨迹中最大侧向加速度、最大横摆角速度与最大质心侧偏角，w1、w2、w3、w4为权值系数，w1+w2+w3+w4＝1，第一项反应换道效率，第二、三、四项反应换道平顺性与安全性，满足目标函数的y(t)即为不同车速下的最优换道轨迹，In the formula: ld is the longitudinal length of the lane changing trajectory ld = xd , ay (t), β(t), ω(t) are the lateral acceleration, yaw rate and lateral deviation of the center of mass during the lane changing process angle, aymax , βmax , and ωmax are the maximum lateral acceleration, maximum yaw rate, and maximum center-of-mass sideslip angle in all lane-changing trajectories, w1 , w2 , w3 , and w4 are weight coefficients, and w1 +w2 +w3 +w4 ＝1, the first item reflects the efficiency of lane changing, the second, third, and fourth items reflect the smoothness and safety of lane changing, and y(t) satisfying the objective function is the optimal track change trajectory,
有益效果：Beneficial effect:
1)以转向换道为主、制动为辅的激进型避障驾驶方法，解决了不必要的紧急制动，造成对后方驾驶员的惊吓，提高了交通的整体安全性，同时解决了不必要的制动造成的交通的拥堵，提高交通通行效率；1) The radical obstacle-avoiding driving method, which mainly focuses on steering and changing lanes and supplemented by braking, solves unnecessary emergency braking, which causes fright to the rear drivers, improves the overall safety of traffic, and solves the problem of unnecessary emergency braking. Traffic congestion caused by necessary braking, improving traffic efficiency;
2)以前方车辆的制动减速度与正常情况下最大制动减速度的差值、前方车辆的速度与道路限速倍数的差值以及实时车距与制动安全距离三者作为避障触发条件，提高避障系统的可靠性和驾驶平顺性，也满足了激进式驾驶风格驾驶员的对于节省时间的追求；2) The difference between the braking deceleration of the vehicle in front and the maximum braking deceleration under normal conditions, the difference between the speed of the vehicle in front and the road speed limit multiple, and the real-time vehicle distance and braking safety distance are used as the obstacle avoidance trigger Conditions, improve the reliability and driving comfort of the obstacle avoidance system, and also meet the time-saving pursuit of aggressive driving style drivers;
3)以稳定性、舒适性评价指标(侧向加速度、横摆角速度、质心侧偏角)以及纵向换道距离为前提规划换道路径，保证换道稳定性和安全性。3) The lane change path is planned on the premise of stability, comfort evaluation indicators (lateral acceleration, yaw rate, side slip angle) and longitudinal lane change distance to ensure lane change stability and safety.
附图说明Description of drawings
图1为激进型辅助驾驶弯道避障系统的逻辑框图；Figure 1 is a logical block diagram of the aggressive assisted driving curve obstacle avoidance system;
图2为模拟场景的路径与车辆位置简图；Figure 2 is a schematic diagram of the path and vehicle position of the simulated scene;
图3为五次多项式换道路径图，对应的xs为40；Figure 3 is a quintic polynomial lane change path diagram, and the corresponding xs is 40;
图4为弯道半径为650m时的最优换道路径图。Figure 4 is a diagram of the optimal lane change path when the radius of the curve is 650m.
具体实施方式Detailed ways
下面结合附图对本发明做更进一步的解释。The present invention will be further explained below in conjunction with the accompanying drawings.
如图1所示，为该系统的逻辑框图，分为7个部分，环境感知单元、自车传感器单元、CAN总线、VCU、线控转向单元、线控制动单元、速度控制单元。下面为对框图流程的具体解释：As shown in Figure 1, it is a logical block diagram of the system, which is divided into seven parts, environment perception unit, vehicle sensor unit, CAN bus, VCU, steering-by-wire unit, brake-by-wire unit, and speed control unit. The following is a specific explanation of the block diagram process:
所述环境感知单元包括摄像头、激光雷达以及毫米波雷达；所述自车传感器单元包括车速传感器、加速度传感器、前轮转角传感器；所述摄像头安装在车辆挡风玻璃正上方，用于识别车道线信息、障碍物信息、车道限速，并将图像信息传入VCU；所述激光雷达，数量至少2个(保证前端检测的安全性，防止单一雷达失效造成危险)，分别安装在前端舱盖上和车顶上，用于检测前方障碍物与自车之间的相对距离以及前方车辆的速度、加速度，并将信息存储于CAN总线上，供VCU实时调取和处理；所述毫米波雷达，数量至少1个，安装在车辆前部的进气隔栅，用于检测远距离车辆与自车的相对距离，并将信息存储于CAN总线上，供VCU实时调取和处理；所述车速传感器、加速度传感器与前轮转角传感器，分别用于收集车辆的速度、纵向加速度、前轮转角，并将信息存储于CAN总线上，供VCU实时调取和处理；所述线控转向单元包含转向助力电机以及转向控制器，用于接收VCU的转向信号，进行转向；所述线控制动单元包含制动轮缸，用于接收VCU的制动信号，进行制动；所述轮速控制单元包含车轮电机，用于接收VCU的轮速信号，对车速进行控制；所述电子控制单元VCU实现计算、判断、发出控制信号功能，用于根据接收到的自车的车速、纵向加速度以及前方车辆的速度、加速度计算自车和前方车辆之间的安全距离后，将计算得到的安全距离作为判断是否进行转向的依据；同时，当前车距离达到安全距离后，VCU计算模块计算得到换道路径以及换道所需的前轮转角、自车速度、纵向加速度后，分别与自车实时的前轮转角、自车车速、加速度进行比较，依据比较得到的差值来调节转向助力电机的输入电流、制动轮缸的压力以及车轮速度，使得线控转向单元、线控制动单元、车轮速度单元工作。The environment perception unit includes a camera, laser radar and millimeter wave radar; the vehicle sensor unit includes a vehicle speed sensor, an acceleration sensor, and a front wheel angle sensor; the camera is installed directly above the vehicle windshield for identifying lane lines Information, obstacle information, lane speed limit, and the image information is transmitted to the VCU; the number of the laser radars is at least 2 (to ensure the safety of front-end detection and prevent danger caused by a single radar failure), which are respectively installed on the front hatch. And on the roof of the vehicle, it is used to detect the relative distance between the obstacle in front and the vehicle as well as the speed and acceleration of the vehicle in front, and store the information on the CAN bus for real-time retrieval and processing by the VCU; the millimeter-wave radar, The number is at least one, installed on the air intake grille at the front of the vehicle, used to detect the relative distance between the long-distance vehicle and the own vehicle, and store the information on the CAN bus for real-time retrieval and processing by the VCU; the vehicle speed sensor , an acceleration sensor and a front wheel angle sensor, which are respectively used to collect the speed, longitudinal acceleration, and front wheel angle of the vehicle, and store the information on the CAN bus for real-time retrieval and processing by the VCU; the steering-by-wire unit includes power steering The motor and the steering controller are used to receive the steering signal from the VCU and perform steering; the brake-by-wire unit includes a brake wheel cylinder, which is used to receive the braking signal from the VCU to perform braking; the wheel speed control unit includes a wheel The motor is used to receive the wheel speed signal from the VCU to control the vehicle speed; the electronic control unit VCU realizes the functions of calculation, judgment and sending control signals, and is used to control the vehicle speed according to the received vehicle speed, longitudinal acceleration and the speed of the vehicle ahead. , Acceleration After calculating the safe distance between the self-vehicle and the vehicle in front, the calculated safe distance is used as the basis for judging whether to turn; at the same time, after the current vehicle distance reaches the safe distance, the VCU calculation module calculates the lane-changing path and lane-changing After the required front wheel angle, vehicle speed, and longitudinal acceleration are compared with the real-time front wheel angle, vehicle speed, and acceleration of the vehicle, the input current of the power steering motor and the braking system are adjusted according to the difference obtained from the comparison. The pressure of the wheel cylinder and the speed of the wheels make the steering-by-wire unit, the brake-by-wire unit and the wheel speed unit work.
本发明还公开了一种基于该避障系统的避障换道路径规划方法，其特征在于，包括以下几个步骤：The present invention also discloses a path planning method for obstacle avoidance and lane change based on the obstacle avoidance system, which is characterized in that it includes the following steps:
步骤1)，摄像头采集车道线信息、障碍物信息以及车道限速，将识别的自车所在车道的两车道线曲率、障碍物位置信息、车道的限速传到CAN总线上，分别供VCU调取用于计算和判断；激光雷达采集前方障碍物的相关信息，将采集的前方障碍物和自车之间的相对距离以及前方车辆的速度传到CAN总线上，分别供VCU调取用于判断和计算；Step 1), the camera collects lane line information, obstacle information, and lane speed limit, and transmits the recognized two-lane line curvature, obstacle position information, and lane speed limit of the lane where the self-vehicle is located to the CAN bus for VCU adjustment. It is used for calculation and judgment; the laser radar collects the relevant information of the obstacles in front, and transmits the relative distance between the obstacles in front and the vehicle in front and the speed of the vehicle in front to the CAN bus, which are respectively called by the VCU for judgment and calculate;
步骤2)，车速传感器、加速度传感器、前轮转角传感器分别收集汽车的车速、加速度、前轮转角，并将收集到的数据输入到CAN总线上和供电子VCU进行计算；Step 2), the vehicle speed sensor, the acceleration sensor, and the front wheel angle sensor respectively collect the vehicle speed, acceleration, and front wheel angle of the car, and input the collected data to the CAN bus and supply the electronic VCU for calculation;
步骤3)，VCU根据接收到的前方车辆的车速、自车车速、加速度建立制动安全距离模型，根据Mazda制动安全距离模型计算汽车和前方车辆之间的安全距离：Step 3), the VCU establishes a braking safety distance model according to the received vehicle speed, vehicle speed, and acceleration of the vehicle in front, and calculates the safety distance between the vehicle and the vehicle in front according to the Mazda braking safety distance model:
其中，Vh为自车速度，vrel为前车与自车的相对速度，μ为路面辅助系数，g为重力加速度，t1为驾驶员反应延迟时间,t2为制动器延迟时间，d0为最小停车距离，并将计算的到的安全距离传到CAN总线上，供VCU进行调取和判断；Among them, Vh is the speed of the vehicle, vrel is the relative speed of the vehicle in front and the vehicle in front, μ is the road surface assistance coefficient, g is the acceleration of gravity, t1 is the driver’s reaction delay time, t2 is the brake delay time, d0 It is the minimum parking distance, and the calculated safe distance is transmitted to the CAN bus for VCU to call and judge;
步骤4)，VCU通过判断自车道前方障碍物和自车之间的距离Sreal与安全距离Ssafe的大小，计算出差值a，a＝Sreal-Ssafe；Step 4), the VCU calculates the difference a by judging the distance Sreal and the safe distance Ssafe between the obstacle in front of the own lane and the vehicle, a=Sreal −Ssafe ;
步骤5)，摄像头检测道路信息，通过图像识别，得到车道限速为vlim,VCU通过判断得到Sreal≤S的结果后，S为需要测量操作的距离(Ssafe＜S＜传感器有效检测距离，由法规GBT20608-2006规定为75m)，判断前车速度vq与vlim*j1的大小(j1∈(0,1))，计算出差值b，b＝vq-vlim*j1；Step 5), the camera detects the road information, and through image recognition, the speed limit of the lane is vlim , after the VCU obtains the result of Sreal ≤ S through judgment, S is the distance that needs to be measured (Ssafe < S < the effective detection distance of the sensor , which is 75m according to the regulations GBT20608-2006), judge the size of the front vehicle speed vq and vlim *j1 (j1∈(0,1)), and calculate the difference b, b=vq -vlim *j1;
步骤6)，VCU通过判断得到Sreal≤S的结果后，通过判断前车是否紧急制动(由资料查阅知，紧急制动时，汽车的最大减速度一般为7.5－8m/s，普通制动时，汽车的平均减速度应为acom为3－4m/s)，计算前车减速度aq与max(acom)大小，即计算出差值c＝max(acom)-aq，max(acom)为普通制动时汽车平均减速度的最大值；Step 6), after the VCU obtains the result of Sreal ≤ S through the judgment, it judges whether the vehicle in front brakes urgently (according to information, the maximum deceleration of the vehicle is generally 7.5-8m/s during emergency braking. When moving, the average deceleration of the car should be acom is 3-4m/s), calculate the deceleration aq and max(acom ) of the front car, that is, calculate the difference c=max(acom )-aq , max(acom ) is the maximum value of the average deceleration of the vehicle during normal braking;
步骤7)，摄像头实时检测，前方障碍物左侧车道是否有障碍物，当差值a、b、c三者有一个小于等于0时，且当左侧车道无障碍物时，VCU计算出换道路径，为进行直接换道的决策做准备，当判断为假时，计算给出制动轮缸压力，进行制动；Step 7), the camera detects in real time whether there is an obstacle in the left lane of the front obstacle, when one of the differences a, b, and c is less than or equal to 0, and when there is no obstacle in the left lane, the VCU calculates The lane path is prepared for the decision to change lanes directly. When the judgment is false, the brake wheel cylinder pressure is calculated and braked;
步骤8)，VCU通过路径规划控制器，对方向盘转角进行控制，并计算转向角δ、速度v、制动踏板力F，传入线控转向单元、线控制动单元、速度控制单元进行控制。Step 8), the VCU controls the steering wheel angle through the path planning controller, and calculates the steering angle δ, speed v, and brake pedal force F, and transmits them to the steering-by-wire unit, brake-by-wire unit, and speed control unit for control.
步骤5)中j1的确定，j1为自定义速度系数，由于激进型驾驶风格的驾驶员存在对外车道上慢速行驶车辆不耐烦而从内车道超车的行为，可对激进型驾驶风格的驾驶员进行驾驶行为分析，分析超车时前车速度与道路限制速度的比值，这n个数据为k1,k2,···kn,为保证数据的准确性，采用最小二乘法，使最小，为平均值，即为使D最小时的x，In the determination of j1 in step 5), j1 is the self-defined speed coefficient. Since the driver with the aggressive driving style is impatient with the slow-moving vehicle on the outer lane and overtakes from the inner lane, it can be used for the driver with the aggressive driving style. Carry out driving behavior analysis, analyze the ratio of the speed of the vehicle in front and the speed limit of the road when overtaking, the n data are k1 , k2 ,...kn , in order to ensure the accuracy of the data, the least square method is used to make minimum, is the average value, that is, x when D is minimized,
所述步骤7)中的换道路径的确定方法为：The determination method of the lane-changing path in the step 7) is:
步骤7.1)，如图2所示，为模拟场景的路径与车辆位置简图，Step 7.1), as shown in Figure 2, is a schematic diagram of the path and vehicle position of the simulated scene,
根据CAN总线中的路径曲率，以车辆后轮中心所在位置与右侧车道垂线的交点为原点(0,0)拟合出自车车道实时圆弧曲线，为According to the path curvature in the CAN bus, the intersection point (0,0) of the center of the rear wheel of the vehicle and the vertical line of the right lane is used as the origin (0,0) to fit the real-time arc curve of the vehicle lane, which is
其中，R为道路曲率半径，x为换道纵向距离；Among them, R is the radius of curvature of the road, and x is the longitudinal distance of changing lanes;
设定车道宽度为dw，则自车所在路径公式为：Set the width of the lane as dw , then the path formula of the ego vehicle is:
步骤7.2)，通过道路宽度的关系，计算得到相邻车道道路中心的实时圆弧曲线，即目标换道路径公式为：Step 7.2), through the relationship of road width, calculate the real-time arc curve of the road center of the adjacent lane, that is, the formula of the target lane change path is:
y1为外车道中心曲线，y2为内车道中心线曲线；y1 is the center curve of the outer lane, and y2 is the center line curve of the inner lane;
步骤7.3)，如图3所示，为当将换道距离设为40时的车辆换道路径图，通过五次多项曲线拟合出两条车道中心圆弧曲线之间的换道路径，规划出换道路径；Step 7.3), as shown in Figure 3, is the vehicle lane change path diagram when the lane change distance is set to 40, and the lane change path between the two lane center arc curves is fitted by a quintic polynomial curve, Plan out lane-changing paths;
车辆的换道结束目标点为(xs,ys),设换道路径方程为：The vehicle’s lane-changing end target point is (xs , ys ), and the lane-changing path equation is:
y(x)＝a0+a1x+a2x2+a3x3+a4x4+a5x5y(x)＝a0 +a1 x+a2 x2 +a3 x3 +a4 x4 +a5 x5
其中，a0，a1，a2，a3，a4，a5分别为五次多项式的系数；Among them, a0 , a1 , a2 , a3 , a4 , a5 are the coefficients of the quintic polynomial;
车辆在换道初始位置x＝0和结束位置x＝xs满足方程为：The vehicle satisfies the equation at the initial position x=0 and the end position x=xs of the lane change as follows:
其中，Rz为车道线中心线的曲率半径；Among them, Rz is the radius of curvature of the centerline of the lane line;
求解可得，a0，a1，a2，a3，a4，a5的值，假设纵向速度V不变，即xs＝V*t，则换道轨迹为：The values of a0 , a1 , a2 , a3 , a4 , and a5 can be obtained from the solution. Assuming that the longitudinal velocity V remains unchanged, that is, xs =V*t, the lane change trajectory is:
y(x)＝b0+b1t+b2t2+b3t3+b4t4+b5t5y(x)＝b0 +b1 t+b2 t2 +b3 t3 +b4 t4 +b5 t5
则：but:
C＝[V3 V4 V5] C＝[V3 V4 V5 ]
步骤7.4)，通过改变xs,即换道完成纵向距离，可得到不同的换道轨迹；Step 7.4), by changing xs , that is, the longitudinal distance to complete the lane change, different lane change trajectories can be obtained;
步骤7.5)，假设汽车的运动简单地服从于阿克曼几何关系，其中前轮转角由基础驾驶员模型得到，汽车轨迹曲率与方向盘转角成正比：Step 7.5), assuming that the motion of the car simply obeys the Ackermann geometric relation, where the front wheel angle is obtained from the basic driver model, and the curvature of the car trajectory is proportional to the steering wheel angle:
式中，L：轴距，V：纵向速度，为侧向加速度，δf为前轮转角。In the formula, L: wheelbase, V: longitudinal speed, is the lateral acceleration, and δf is the front wheel rotation angle.
步骤7.6)在不同车速下，根据基础驾驶员模型和轨迹二次求导得到的侧向加速度，得到期望前轮转角，在不同的轨迹条件下，根据运动学二自由度模型：Step 7.6) At different vehicle speeds, according to the basic driver model and the lateral acceleration obtained by the second derivation of the trajectory, the desired front wheel angle is obtained. Under different trajectory conditions, according to the kinematic two-degree-of-freedom model:
式中，K1为前轮总侧偏刚度，K2为后轮总侧偏刚度，Iz为绕z轴的转动惯量，m为汽车总质量，a为质心至前轴距离，b为质心至后轴距离vy为侧向速度，In the formula, K1 is the total cornering stiffness of the front wheels, K2 is the total cornering stiffness of the rear wheels, Iz is the moment of inertia around thez -axis, m is the total mass of the vehicle, a is the distance from the center of mass to the front axle, and b is the center of mass The distance vy to the rear axle is the lateral velocity,
计算得到横摆角速度和质心侧偏角；Calculate the yaw rate and the sideslip angle of the center of mass;
步骤7.7)设定目标函数为：Step 7.7) Set the objective function as:
式中：ld为换道轨迹的纵向长度ld＝xd，ay(t)、β(t)、ω(t)为换道过程中的侧向加速度、横摆角速度与质心侧偏角，aymax、βmax、ωmax为所有换道轨迹中最大侧向加速度、最大横摆角速度与最大质心侧偏角，w1、w2、w3、w4为权值系数，w1+w2+w3+w4＝1，第一项反应换道效率，第二、三、四项反应换道平顺性与安全性；In the formula: ld is the longitudinal length of the lane changing trajectory ld = xd , ay (t), β(t), ω(t) are the lateral acceleration, yaw rate and lateral deviation of the center of mass during the lane changing process angle, aymax , βmax , and ωmax are the maximum lateral acceleration, maximum yaw rate, and maximum center-of-mass sideslip angle in all lane-changing trajectories, w1 , w2 , w3 , and w4 are weight coefficients, and w1 +w2 +w3 +w4 ＝1, the first item reflects the efficiency of lane change, and the second, third, and fourth items reflect the smoothness and safety of lane change;
如图4所示，为车速为15m/s、弯道半径为650m的车辆换道轨迹与最优换道轨迹图，满足目标函数的y(t),即为不同车速下的最优换道轨迹。As shown in Figure 4, it is the vehicle lane change trajectory and optimal lane change trajectory diagram with a vehicle speed of 15m/s and a curve radius of 650m. The y(t) that satisfies the objective function is the optimal lane change at different vehicle speeds track.
此处w1取值为0.4，w2取值为0.2，w3取值为0.2,w4取值为0.2。Here w1 takes a value of 0.4, w2 takes a value of 0.2, w3 takes a value of 0.2, and w4 takes a value of 0.2.
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。The above is only a preferred embodiment of the present invention, it should be pointed out that, for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications can also be made. It should be regarded as the protection scope of the present invention.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111081069A (en)* | 2019-12-31 | 2020-04-28 | 交通运输部公路科学研究所 | Vehicle track control method for bottleneck area of expressway |
| CN111216718A (en)* | 2020-01-10 | 2020-06-02 | 浙江吉利汽车研究院有限公司 | A kind of collision avoidance method, device and equipment |
| CN111413990A (en)* | 2020-05-07 | 2020-07-14 | 吉林大学 | Lane change track planning system |
| CN111443709A (en)* | 2020-03-09 | 2020-07-24 | 北京百度网讯科技有限公司 | Vehicle lane change route planning method, device, terminal and storage medium |
| CN111488674A (en)* | 2020-03-12 | 2020-08-04 | 上海理工大学 | Plane intersection vehicle running track simulation method |
| CN111649955A (en)* | 2020-04-28 | 2020-09-11 | 东南大学 | A performance evaluation method for vehicle-road cooperative automatic emergency braking system |
| CN111731282A (en)* | 2020-06-04 | 2020-10-02 | 南京航空航天大学 | An emergency collision avoidance system considering vehicle stability and its control method |
| CN111873990A (en)* | 2020-07-15 | 2020-11-03 | 哈尔滨理工大学 | A lane-changing collision avoidance device suitable for high-speed emergency conditions and method thereof |
| CN112068574A (en)* | 2020-10-19 | 2020-12-11 | 中国科学技术大学 | A control method and system for an unmanned vehicle in a dynamic complex environment |
| CN112100565A (en)* | 2020-08-31 | 2020-12-18 | 中国第一汽车股份有限公司 | Road curvature determination method, device, equipment and storage medium |
| CN112238856A (en)* | 2020-10-13 | 2021-01-19 | 南京航空航天大学 | An intelligent vehicle overtaking trajectory optimization method based on hybrid particle swarm algorithm |
| CN112265548A (en)* | 2020-09-15 | 2021-01-26 | 北京工业大学 | An automatic driving control strategy adjustment method considering road conditions |
| CN112455444A (en)* | 2020-11-26 | 2021-03-09 | 东风汽车集团有限公司 | Lane changing device and method for autonomously learning lane changing style of driver |
| CN112677963A (en)* | 2021-01-07 | 2021-04-20 | 吉林大学 | Intelligent networking four-wheel independent steering and independent driving electric automobile emergency obstacle avoidance system |
| CN112918443A (en)* | 2021-03-09 | 2021-06-08 | 一汽解放汽车有限公司 | Brake control method, device, equipment and storage medium |
| CN113060118A (en)* | 2019-12-30 | 2021-07-02 | 百度（美国）有限责任公司 | Speed planning guideline for soft deceleration |
| CN113525365A (en)* | 2021-07-21 | 2021-10-22 | 上汽通用五菱汽车股份有限公司 | Road planning method, device and computer readable storage medium |
| CN113954834A (en)* | 2020-07-15 | 2022-01-21 | 荷兰移动驱动器公司 | Lane changing planning method and vehicle-mounted device |
| CN114030474A (en)* | 2021-08-19 | 2022-02-11 | 东南大学 | Driving safety field construction method based on driver subjective risk experience |
| CN114371691A (en)* | 2021-10-26 | 2022-04-19 | 南京航空航天大学 | A kind of assisted driving curve trajectory tracking control method |
| CN114407880A (en)* | 2022-02-18 | 2022-04-29 | 岚图汽车科技有限公司 | Unmanned emergency obstacle avoidance path tracking method |
| CN116834729B (en)* | 2023-08-28 | 2023-11-17 | 常州星宇车灯股份有限公司 | Transverse and longitudinal combined control system and control method for vehicle auxiliary driving |
| CN120335459A (en)* | 2025-06-17 | 2025-07-18 | 吉林大学 | A multi-objective collaborative optimization method for obstacle avoidance trajectory decision making |
| US20250304071A1 (en)* | 2024-04-01 | 2025-10-02 | Jilin University | Anthropomorphic lane-changing control method and system based on driving risk quantification, and vehicle |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080059037A1 (en)* | 2006-09-04 | 2008-03-06 | Denso Corporation | Vehicle control system |
| CN105109486A (en)* | 2015-09-18 | 2015-12-02 | 智车优行科技（北京）有限公司 | Safety monitoring and controlling device and method for vehicle |
| CN106965808A (en)* | 2017-04-17 | 2017-07-21 | 南京航空航天大学 | Automobile transverse and longitudinal active negotiation anti-collision system and its coordination approach |
| CN206781743U (en)* | 2017-04-24 | 2017-12-22 | 南京航空航天大学 | Automobile differential steering system with a variety of collision avoidance patterns |
| JP2018090123A (en)* | 2016-12-05 | 2018-06-14 | マツダ株式会社 | Vehicle control device |
| CN108569288A (en)* | 2018-04-12 | 2018-09-25 | 南京航空航天大学 | A method for defining and avoiding collisions in dangerous working conditions of automobiles |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080059037A1 (en)* | 2006-09-04 | 2008-03-06 | Denso Corporation | Vehicle control system |
| CN105109486A (en)* | 2015-09-18 | 2015-12-02 | 智车优行科技（北京）有限公司 | Safety monitoring and controlling device and method for vehicle |
| JP2018090123A (en)* | 2016-12-05 | 2018-06-14 | マツダ株式会社 | Vehicle control device |
| CN106965808A (en)* | 2017-04-17 | 2017-07-21 | 南京航空航天大学 | Automobile transverse and longitudinal active negotiation anti-collision system and its coordination approach |
| CN206781743U (en)* | 2017-04-24 | 2017-12-22 | 南京航空航天大学 | Automobile differential steering system with a variety of collision avoidance patterns |
| CN108569288A (en)* | 2018-04-12 | 2018-09-25 | 南京航空航天大学 | A method for defining and avoiding collisions in dangerous working conditions of automobiles |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113060118A (en)* | 2019-12-30 | 2021-07-02 | 百度（美国）有限责任公司 | Speed planning guideline for soft deceleration |
| CN111081069A (en)* | 2019-12-31 | 2020-04-28 | 交通运输部公路科学研究所 | Vehicle track control method for bottleneck area of expressway |
| CN111081069B (en)* | 2019-12-31 | 2021-08-06 | 交通运输部公路科学研究所 | A Vehicle Trajectory Control Method in the Bottleneck Area of Expressway |
| CN111216718A (en)* | 2020-01-10 | 2020-06-02 | 浙江吉利汽车研究院有限公司 | A kind of collision avoidance method, device and equipment |
| CN111443709A (en)* | 2020-03-09 | 2020-07-24 | 北京百度网讯科技有限公司 | Vehicle lane change route planning method, device, terminal and storage medium |
| CN111443709B (en)* | 2020-03-09 | 2023-08-29 | 北京百度网讯科技有限公司 | Vehicle lane change route planning method, device, terminal and storage medium |
| CN111488674A (en)* | 2020-03-12 | 2020-08-04 | 上海理工大学 | Plane intersection vehicle running track simulation method |
| CN111488674B (en)* | 2020-03-12 | 2024-01-16 | 上海理工大学 | Plane intersection vehicle running track simulation method |
| CN111649955A (en)* | 2020-04-28 | 2020-09-11 | 东南大学 | A performance evaluation method for vehicle-road cooperative automatic emergency braking system |
| CN111413990A (en)* | 2020-05-07 | 2020-07-14 | 吉林大学 | Lane change track planning system |
| CN111731282A (en)* | 2020-06-04 | 2020-10-02 | 南京航空航天大学 | An emergency collision avoidance system considering vehicle stability and its control method |
| CN111873990A (en)* | 2020-07-15 | 2020-11-03 | 哈尔滨理工大学 | A lane-changing collision avoidance device suitable for high-speed emergency conditions and method thereof |
| CN113954834A (en)* | 2020-07-15 | 2022-01-21 | 荷兰移动驱动器公司 | Lane changing planning method and vehicle-mounted device |
| CN112100565A (en)* | 2020-08-31 | 2020-12-18 | 中国第一汽车股份有限公司 | Road curvature determination method, device, equipment and storage medium |
| CN112265548A (en)* | 2020-09-15 | 2021-01-26 | 北京工业大学 | An automatic driving control strategy adjustment method considering road conditions |
| CN112238856A (en)* | 2020-10-13 | 2021-01-19 | 南京航空航天大学 | An intelligent vehicle overtaking trajectory optimization method based on hybrid particle swarm algorithm |
| CN112068574A (en)* | 2020-10-19 | 2020-12-11 | 中国科学技术大学 | A control method and system for an unmanned vehicle in a dynamic complex environment |
| CN112455444B (en)* | 2020-11-26 | 2021-12-07 | 东风汽车集团有限公司 | Lane changing device and method for autonomously learning lane changing style of driver |
| CN112455444A (en)* | 2020-11-26 | 2021-03-09 | 东风汽车集团有限公司 | Lane changing device and method for autonomously learning lane changing style of driver |
| CN112677963A (en)* | 2021-01-07 | 2021-04-20 | 吉林大学 | Intelligent networking four-wheel independent steering and independent driving electric automobile emergency obstacle avoidance system |
| CN112677963B (en)* | 2021-01-07 | 2022-02-15 | 吉林大学 | Intelligent network-connected four-wheel independent steering and independent drive electric vehicle emergency obstacle avoidance system |
| CN112918443A (en)* | 2021-03-09 | 2021-06-08 | 一汽解放汽车有限公司 | Brake control method, device, equipment and storage medium |
| CN113525365A (en)* | 2021-07-21 | 2021-10-22 | 上汽通用五菱汽车股份有限公司 | Road planning method, device and computer readable storage medium |
| CN114030474A (en)* | 2021-08-19 | 2022-02-11 | 东南大学 | Driving safety field construction method based on driver subjective risk experience |
| CN114030474B (en)* | 2021-08-19 | 2022-08-12 | 东南大学 | Construction method of driving safety field based on driver's subjective risk perception |
| CN114371691A (en)* | 2021-10-26 | 2022-04-19 | 南京航空航天大学 | A kind of assisted driving curve trajectory tracking control method |
| CN114371691B (en)* | 2021-10-26 | 2024-04-16 | 南京航空航天大学 | A method for tracking and controlling curved trajectory of assisted driving |
| CN114407880B (en)* | 2022-02-18 | 2023-06-27 | 岚图汽车科技有限公司 | Unmanned emergency obstacle avoidance path tracking method |
| CN114407880A (en)* | 2022-02-18 | 2022-04-29 | 岚图汽车科技有限公司 | Unmanned emergency obstacle avoidance path tracking method |
| CN116834729B (en)* | 2023-08-28 | 2023-11-17 | 常州星宇车灯股份有限公司 | Transverse and longitudinal combined control system and control method for vehicle auxiliary driving |
| US20250304071A1 (en)* | 2024-04-01 | 2025-10-02 | Jilin University | Anthropomorphic lane-changing control method and system based on driving risk quantification, and vehicle |
| CN120335459A (en)* | 2025-06-17 | 2025-07-18 | 吉林大学 | A multi-objective collaborative optimization method for obstacle avoidance trajectory decision making |
| Publication number | Publication date |
|---|---|
| CN110614998B (en) | 2021-01-08 |
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