Abstract

A cooperative lane-changing (CLC) motion planning algorithm for partially connected and automated environment is proposed in this study. Unlike conventional motion planner whose goal is merely enabling driving maneuver, this proposed algorithm takes one step further in terms of reducing oscillation and shockwave caused by lane change, hence improves transport mobility. The proposed motion planner is designed as a model predictive control which is solved by a dynamic programming-based numerical solution method. Since longitudinal automation is much more accessible than lateral automation, the motion planner requires only longitudinal automation in order to keep the design practical. The proposed motion planner is evaluated against the human driver. Sensitivity analysis is conducted in terms of the initial headway of the receiving gap. The results demonstrate that the motion planner reduces oscillation by 0.1%−9.4%. The variation is due to the changes in initial headway of receiving gap. The computation time is around 17-21 milliseconds showing great potential to be applied in real time.

摘要

本研究提出了一种用于部分连接和自动化环境的合作换道（CLC）运动规划算法。与仅以实现驾驶机动为目标的传统运动规划器不同，该算法在减少换道引起的振荡和冲击波方面更进了一步，从而提高了运输机动性。所提出的运动规划器被设计为模型预测控制，它通过基于动态规划的数值求解方法来求解。由于纵向自动化比横向自动化更容易获得，因此运动规划器只需要纵向自动化以保持设计的实用性。建议的运动规划器根据人类驾驶员进行评估。敏感性分析是根据接收差距的初始进展进行的。结果表明，运动规划器将振荡减少了 0.1%-9.4%。变化是由于接收间隙的初始进展的变化。计算时间约为 17-21 毫秒，显示出实时应用的巨大潜力。