Receding horizon cooperative platoon trajectory planning on corridors with dynamic traffic signal

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Meiqi Liu - (Author)
  • Serge Hoogendoorn - (Author)
  • Meng Wang - , Delft University of Technology (Author)

Abstract

In this paper, a trajectory control approach using model predictive control is proposed for cooperative (automated) vehicles. This control approach optimizes accelerations of the controlled connected and automated vehicle (CAV) platoon along a corridor with signalized intersections. The objectives of the proposed approach are to maximize the throughput first and optimize comfort, travel delay, and fuel consumption simultaneously after that. The throughput is determined according to the maximal number of CAVs that can pass the intersection during the green phase. Safety is included by penalizing smaller gaps between CAVs in the running cost. The red phase is taken into account as a virtual vehicle at the stop-line during the red time, thus the safe gap penalty with the virtual vehicle causes the first-stopping vehicle to decelerate or even stop facing the red phase. The acceleration and speed are constrained within the upper and lower bounds. The proposed approach is flexible in dealing with platoon merging, splitting, stopping, and queue-discharging characteristics at signalized intersections. Finally, the proposed control approach is verified by simulation under a baseline scenario and four scenarios, which consider signal settings and the anticipation of the red phase. The simulation results demonstrate the benefits of the proposed control approach on fuel savings, compared with the state-of-art approach which used the virtual vehicle term without anticipation. The adjustments of signal parameters in Scenario 3 and Scenario 4 demonstrate the applicability of the control approach under actuated signal control.

Details

Original languageEnglish
JournalTransportation research record
Publication statusPublished - 2020
Peer-reviewedYes
Externally publishedYes

External IDs

Scopus 85099478800

Keywords