考虑HV-AV交互的动态换道轨迹规划方法  

Dynamic Lane-change Trajectory-planning Method Considering Human-driven Vehicle and Autonomous Vehicle Interaction

作　　者：高雪溢 慈玉生[1] 李珊珊 徐浩城 GAO Xue-yi;CI Yu-sheng;LI Shan-shan;XU Hao-cheng(School of Transportation Science and Engineering,Harbin Institute of Technology,Harbin 150090,Heilongjiang,China)

机构地区：[1]哈尔滨工业大学交通科学与工程学院,黑龙江哈尔滨150090

出　　处：《中国公路学报》2024年第12期340-356,共17页China Journal of Highway and Transport

基　　金：国家重点研发计划项目(2023YFB2603505)。

摘　　要：精确预测邻近车辆控制行为的潜在变化对于自动驾驶车辆(AV)理解驾驶环境、预防安全隐患、提升换道安全至关重要。现有换道轨迹规划方法未能充分考虑邻近车辆控制行为的不确定性,这种不确定性在包含人类驾驶车辆(HV)的混合交通环境中尤为显著。针对此问题,提出了一种考虑HV-AV交互的动态换道轨迹规划方法,以提高包含HV在内的混合交通环境中AV换道行为的适应性和安全性。该方法首先基于LightGBM算法建立驾驶风格识别模型,精确识别邻近车辆驾驶人的不同驾驶风格。随后,通过全速度差(FVD)模型对不同驾驶风格下邻近车辆在AV换道过程中的动态控制反馈进行模拟和分析。最后,综合考虑HV-AV交互反应的影响,动态生成换道轨迹和加速度曲线,以确保换道操作的安全性和流畅性。研究结果表明,与传统的换道轨迹规划方法相比,所提出的方法在实际换道场景中表现出较高的安全性和适应性。该方法能够根据实时交通状况有效调整换道策略,显著降低碰撞风险。Accurately predicting the potential changes in the control behavior of nearby vehicles is crucial for autonomous vehicles(AVs)to understand their driving environments,prevent safety hazards,and enhance lane-changing safety.Existing lane-changing trajectory-planning methods do not sufficiently consider the uncertainty of nearby vehicle control behaviors,which is particularly prominent in mixed traffic environments that include human-driven vehicles(HVs).Hence,a dynamic lane-changing trajectory-planning method that considers the interaction between HVs and AVs was proposed to improve the adaptability and safety of AV lane-changing behaviors.This method comprises three key models.First,a driving-style-identification model based on the LightGBM algorithm was established to accurately identify the driving styles of nearby vehicle drivers.Subsequently,the full velocity-difference model was used to simulate and analyze the dynamic control feedback of nearby vehicles under different driving styles during the lane-changing process of the AV.Finally,considering the interaction effects between an HV and AV,lane-changing trajectories and acceleration curves were dynamically generated to ensure the safety and smoothness of the operations.The results show that this method can effectively adjust lane-changing strategies based on real-time traffic conditions,thereby mitigating collision risks.Compared with conventional lane-changing trajectory planning methods,the proposed method demonstrates higher safety and adaptability in actual lane-changing scenarios.

关 键 词：汽车工程 换道轨迹规划 HV-AV交互 自动驾驶车辆 周围车辆控制反馈 动态交通环境 

分 类 号：U463.6[机械工程—车辆工程]