Dynamic lane changing trajectory planning for CAV: A multi-agent model with path preplanning
Dynamic lane changing trajectory planning for CAV: A multi-agent model with path preplanning
复制标题
CAV 动态变道轨迹规划：具有路径预规划的多智能体模型
DOI：
10.1080/21680566.2021.1989079
复制
发表时间：
2021-10-22
影响因子：
2.8
通讯作者：
Liu, Yixuan
中科院分区：
文献类型：
--
作者：
Zong, Fang;He, Zhengbing;Liu, Yixuan
提出了一种自动驾驶汽车多智能体动态变道轨迹规划方法。该方法通过势场构造决策模块来确定LC的起始点。然后在轨迹生成模块中生成一系列轨迹。构造了一个成本函数，用于搜索目标车辆和参与者的相应最优轨迹。仿真结果表明，该模型提高了LC成功率，缩短了耗时。与传统模型不同的是，我们考虑了CAV LC的合作特性，在满足主体车辆需求的同时，将其对其他参与者的影响最小化。此外，考虑了包含中尺度信息的驱动环境来提高LC的成功率，为优化LC决策提供了一种新的策略。此外，该方法还可用于模拟cav的LC行为。
This paper presents a multi-agent dynamic lane-changing (LC) trajectory planning method for CAV. In this method, a decision module is constructed by means of a potential field to determine the LC starting point. Then a series of trajectories is generated in the trajectory generation module. A cost function is constructed for searching for the corresponding optimal trajectory for both the subject vehicle and the participants. The simulation results indicate that the proposed model improves the LC success rate and reduces duration. Differing from the traditional model, we consider the cooperation feature of CAV's LC and satisfy the subject vehicle's demand as well as minimizing its impact on the other participants. Moreover, the driving environment including mesoscale information is considered to improve the LC success rate, which provides a new strategy for optimizing LC decision. Additionally, the method can also be applied to simulate CAVs' LC behaviour.