This study presents an adaptive motion planning strategy for automated vehicle collision avoidance systems to be associated with the variation of collision speed region based on the position of the obstacle. This is done by designing the motion planner using an artificial potential field (APF) with the incorporation of an adaptive multi-speed scheduler using fuzzy system in the motion planning structure. The knowledge database information is developed based on the risk perception of the driver that consists of APF parameters and was optimised by using particle swarm optimisation algorithm. This study contributes to the improvement of a feasible reference motion generated by the motion planner that can be converted into desired control signals. The reference motion resulted to provide the control command that managed to avoid collision successfully by evasive manoeuvre without lane departure when adapting to variation in the vehicle speeds with different obstacle positions. The results indicated the reduction of the lateral error with respect to the reference avoidance trajectory data of up to 87% compared to base-type APF with maximum reference lateral motion is reduced of up to 26%. Then, a hardware-in-loop test is conducted to verify the proposed strategy using a steering wheel system.

中文翻译：

---

自适应势速调度器的人工势场车辆防撞运动计划策略

这项研究提出了一种针对自动车辆防撞系统的自适应运动计划策略，该策略与基于障碍物位置的碰撞速度区域的变化相关联。这是通过使用人工势场（APF）设计运动计划器并在运动计划结构中结合使用模糊系统的自适应多速计划器来完成的。知识数据库信息是基于驾驶员的风险感知而开发的，该知识由APF参数组成，并使用粒子群优化算法进行了优化。这项研究有助于改进运动计划器生成的可行参考运动，该参考运动可以转换为所需的控制信号。参考运动的结果是提供了一种控制命令，当适应具有不同障碍物位置的车速变化时，可以通过规避机动成功地避免碰撞而无车道偏离。结果表明，与最大参考横向运动的基本型APF相比，相对于参考规避轨迹数据的横向误差减少了87％。然后，进行了硬件在环测试，以使用方向盘系统验证所提出的策略。结果表明，与最大参考横向运动的基本型APF相比，相对于参考规避轨迹数据的横向误差减少了87％。然后，进行了硬件在环测试，以使用方向盘系统验证所提出的策略。结果表明，与最大参考横向运动的基本型APF相比，相对于参考回避轨迹数据而言，横向误差的降低幅度高达87％。然后，进行了硬件在环测试，以使用方向盘系统验证所提出的策略。