混驾环境下基于主从博弈的多车协同决策规划  

Multi-vehicle Cooperative Decision-making and Trajectory Planning Based on Stackelberg Game Theory in Mixed Driving Environments

作　　者：严永俊 彭林 王金湘[1] 皮大伟[2] 刘亚辉[3] 殷国栋[1] YAN Yong-jun;PENG Lin;WANG Jin-xiang;PI Da-wei;LIU Ya-hui;YIN Guo-dong(School of Mechanical Engineering,Southeast University,Nanjing 211189,Jiangsu,China;School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,Jiangsu,China;School of Vehicle and Mobility,Tsinghua University,Beijing 100085,China)

机构地区：[1]东南大学机械工程学院,江苏南京211189 [2]南京理工大学机械工程学院,江苏南京210094 [3]清华大学车辆与运载学院,北京100085

出　　处：《中国公路学报》2024年第3期117-133,共17页China Journal of Highway and Transport

基　　金：国家自然科学基金项目(52072073);国家杰出青年科学基金项目(52025121)。

摘　　要：在自动驾驶车辆与人工驾驶车辆混行的复杂交通环境中,如何减小驾驶行为截然不同的2类车辆间的复杂相互作用对于车辆行驶安全性、乘坐舒适性和交通通行效率的影响,是当前自动驾驶决策与控制领域亟待解决的关键问题。提出了一个人机混驾环境下人工驾驶车辆与自动驾驶车辆之间的非合作博弈交互框架。首先,综合考虑车辆加速度线性递减的驾驶人纵向操纵特性、差异化配合程度和不同的延迟响应特性,建立人工驾驶车辆的纵向博弈策略。其次,考虑自动驾驶车辆与周围车辆的安全性约束,以及自动驾驶车辆在换道过程中的舒适性和通行效率目标,设计了自动驾驶车辆的纵向博弈策略。然后,基于主从博弈理论对不同混驾环境下人工驾驶车辆与自动驾驶车辆的博弈交互问题进行求解,得到最优的换道间隙和自动驾驶车辆的纵向速度轨迹,并采用模型预测控制方法规划出自动驾驶车辆的横向安全换道轨迹。最后,根据人工驾驶车辆不同配合度和延迟响应时间的差异,设计了多组人机混驾试验工况进行验证。试验结果表明:自动驾驶车辆能够快速准确识别人工驾驶车辆的配合度,选择出最优的目标换道间隙,并与间隙周围的自动驾驶车辆协作来汇入目标间隙。在换道过程中,自动驾驶车辆始终与周围车辆保持安全距离,并且在车速为20 m·s^(-1)的情况下,换道车辆的纵横向加速度均不超过1.25 m·s^(-2),安全性和舒适性都得到了保障,验证了该非合作博弈交互框架的有效性。In a complex traffic environment where autonomous and human-driven vehicles coexist,reducing the influence of complex interactions between two vehicle types,which have drastically different driving behaviors on vehicle driving safety,ride comfort,and traffic efficiency is a key issue that needs to be addressed in the field of autonomous driving decision-making and control.Accordingly,this study proposed a non-cooperative game interaction framework between human-driven vehicles(HV)and autonomous vehicles(AV)in a mixed driving environment.First,a longitudinal game strategy for human-driven vehicles was established,considering the driver's longitudinal control characteristics of linearly decreasing vehicle acceleration,differentiated coordination degree,and different characteristics of time delay.Second,a longitudinal game strategy for autonomous vehicles was designed,considering the safety constraints of autonomous vehicles and surrounding vehicles,as well as the comfort and traffic efficiency objectives constraining the autonomous vehicles during the lane-changing process.Then,the interactions between human-driven vehicles and autonomous vehicles in different mixed-driving environments were solved based on the Stackelberg game theory to obtain the optimal lane-changing gaps and longitudinal speed trajectories of autonomous vehicles.The model predictive control(MPC)method was used to generate safe lateral lane-changing trajectories for autonomous vehicles.Finally,multiple sets of mixed driving conditions were designed according to the differences in the coordination degree and response delay time of human-driven vehicles.The test results showed that autonomous vehicles could quickly and accurately identify the coordination degree of human-driven vehicles,select the optimal lane-changing gap,and cooperate with surrounding autonomous vehicles to merge into the target gap.During the lane-changing process,the autonomous vehicles always maintained a safe distance from the surrounding vehicles,and both the longitudinal a

关 键 词：汽车工程 混驾环境多车交互框架 主从博弈理论 自动驾驶车辆 决策规划 模型预测控制 

分 类 号：U461[机械工程—车辆工程]