A motion planner enabling cooperative lane changing: Reducing congestion under partially connected and automated environment

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.

Keywords:

• Connected and automated vehicle
• cooperative lane change
• model predictive control
• traffic oscillation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study is partially supported by National Natural Science Foundation of China (Grant No. 71871163 and No. 61803284), Fundamental Research Funds for the Central Universities (No. 1600219316), Shanghai Oriental Scholar (2018), Shanghai Yangfan Program (No. 18YF1424200), and National Key R&D Program of China (No. 2018YFB1600600).