TY - JOUR
T1 - A motion planner enabling cooperative lane changing
T2 - Reducing congestion under partially connected and automated environment
AU - Bai, Yu
AU - Zhang, Yu
AU - Hu, Jia
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Connected and automated vehicle
KW - cooperative lane change
KW - model predictive control
KW - traffic oscillation
UR - http://www.scopus.com/inward/record.url?scp=85091416884&partnerID=8YFLogxK
U2 - 10.1080/15472450.2020.1820332
DO - 10.1080/15472450.2020.1820332
M3 - Article
AN - SCOPUS:85091416884
SN - 1547-2450
VL - 25 (2021)
SP - 469
EP - 481
JO - Journal of Intelligent Transportation Systems: technology, planning, and operations
JF - Journal of Intelligent Transportation Systems: technology, planning, and operations
IS - 5
ER -