协同换道避障模型和轨迹数据驱动的车辆协同避障策略

Vehicle cooperative obstacle avoidance strategy driven by CLAM model and trajectory data

考虑车辆类型、驾驶风格及不同阶段影响车辆换道的关键目标,将车辆避障过程中的“车-车交互”机理描述为力的关系,构建协同换道避障模型(CLAM),提取并建立适用于突发事件的车辆避障微观轨迹数据集,将车辆避障转化为多约束优化控制问题,以优化算法(OA)为纽带,设计车辆协同避障控制(CLAM-OA)策略。结果表明:相较于数据驱动的长短时记忆模型,CLAM-OA策略输出的误差均显著减小、车速与位移在不同时域的输出结果也更加稳定。

Considering the vehicle type,driving style,and the most important objects(MIO)affecting the vehicle lane change at different stages,cooperative lane-change obstacle avoidance model(CLAM)was constructed by describing the"vehicle-vehicle interaction"mechanism in the vehicle obstacle avoidance process as a force relationship;the vehicle lane change avoidance execution events under emergencies were extracted according to the lane change execution segment extraction criterion to establish a vehicle obstacle avoidance micro-trajectory dataset to unexpected events.The cooperative vehicle lane change obstacle avoidance was transformed into a multi-constraint optimal control problem.The cooperative lane-change obstacle avoidance model-optimistic algorithm strategy(CLAM-OA strategy)was designed with the optimization algorithm as a bridge.The results show that compared with the data-driven LSTM model,the outputs of the CLAM-OA strategy have significantly lower errors and more stable results in different time domains of vehicle speed and displacement.