首页|协同换道避障模型和轨迹数据驱动的车辆协同避障策略

协同换道避障模型和轨迹数据驱动的车辆协同避障策略

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

engineering of communication and transportation systemcollision avoidance control strategyhybrid drivevehicle controllane changing behaviormicro-trajectory data

秦雅琴、钱正富、谢济铭

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昆明理工大学 交通工程学院,昆明 650500

交通运输系统工程 避障策略 混合驱动 车辆控制 换道行为 微观轨迹数据

国家自然科学基金国家重点研发计划

718610162018YFB1600500

2024

10.13229/j.cnki.jdxbgxb.20220754

吉林大学学报(工学版)
吉林大学

吉林大学学报(工学版)

CSTPCD北大核心
影响因子：0.792
ISSN：1671-5497
年,卷(期)：2024.54(5)
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