Gao Feng, Zheng Xu, Hu Qiuxia, Liu Hongwei
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China.
Western Science City Intelligent and Connected Vehicle Innovation Center (Chongqing) Co., Ltd., Chongqing 400015, China.
Sensors (Basel). 2024 Dec 24;25(1):26. doi: 10.3390/s25010026.
With the rise in the intelligence levels of automated vehicles, increasing numbers of modules of automated driving systems are being combined to achieve better performance and adaptability by reducing information loss. In this study, an integrated decision and motion planning system is designed for multi-object highways. A two-layer structure is presented to decouple the influence of the traffic environment and the dynamic control of ego vehicles using the cognitive safety area, the size of which is determined by naturalistic driving behavior. The artificial potential field method is used to comprehensively describe the influence of all external objects on the cognitive safety area, the lateral motion dynamics of which are determined by the attention mechanism of the human driver during lane changes. Then, the interaction between the designed cognitive safety area and the ego vehicle can be simplified into a spring-damping system, and the desired dynamic states of the ego vehicle can be obtained analytically for better computational efficiency. The effectiveness of this on improving traffic efficiency, driving comfort, safety, and real-time performance was validated using several comparative tests utilizing complicated scenarios with multiple vehicles.
随着自动驾驶车辆智能水平的提高，越来越多的自动驾驶系统模块被组合起来，以通过减少信息损失来实现更好的性能和适应性。在本研究中，为多目标高速公路设计了一种集成决策与运动规划系统。提出了一种两层结构，利用认知安全区域来解耦交通环境的影响和自车的动态控制，认知安全区域的大小由自然驾驶行为确定。采用人工势场法综合描述所有外部物体对认知安全区域的影响，其横向运动动力学由驾驶员在变道时的注意力机制确定。然后，将设计的认知安全区域与自车之间的相互作用简化为一个弹簧阻尼系统，并通过解析得到自车的期望动态状态，以提高计算效率。通过使用多个车辆的复杂场景进行的几次对比测试，验证了这在提高交通效率、驾驶舒适性、安全性和实时性能方面的有效性。
