Zuo, Wanqing (Shanghai Jiao Tong University), Xu, Yunwen (Shanghai Jiao Tong University), Chen, Youren (Shanghai Jiao Tong University), Li, Dewei (Department of Automation, Shanghai Jiao Tong University)

Signal Temporal Logic Based Vehicle Trajectory Planning under Mandatory Stop Rule for Heavy Vehicle Right Turns at Signal-Free Intersections

Scheduled for presentation during the Regular Session "Trajectory planning I" (WeBT9), Wednesday, September 25, 2024, 16:10−16:30, Salon 17

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

The blind spots of heavy vehicles during right turns arise a safety concern for traffic. Hence, the regulation mandating heavy vehicles to come to a complete stop before making right turns is crucial in ensuring intersection safety. However, the current trajectory planning methods struggle to integrate these traffic regulations, and the restrictions imposed by such traffic rules greatly impact traffic efficiency at intersections. This paper proposes a signal temporal logic (STL) based trajectory planning method for connected and autonomous vehicles (CAVs) at a signal-free intersection, where traffic rules for heavy vehicles are conveniently described by STL formulas. All CAVs at the intersection are modeled without predefined paths to enhance the flexibility of trajectories. Each STL formula is encoded in a standardized way as a conjunction of inequality and equality constraints for the optimization, and the whole trajectory planning of CAVs is achieved through solving a mixed-integer nonlinear programming (MINLP) problem. Simulation results indicate that our method improves traffic efficiency in the scenario with heavy vehicles right turns.