Real-Time Parallel Trajectory Optimization with Spatiotemporal Safety Constraints for Autonomous Driving in Congested Traffic

TL;DR

This paper introduces a real-time parallel trajectory optimization approach for autonomous vehicles that ensures safety and efficiency in congested traffic by considering multi-modal behaviors of surrounding vehicles and leveraging parallel computing.

Contribution

It develops a spatiotemporal safety module and a nonlinear programming framework for real-time, safe, and efficient trajectory planning in dense traffic environments.

Findings

Successfully navigates dense traffic with multiple uncertain surrounding vehicles.

Achieves high travel efficiency and safety in real-time simulations.

Utilizes parallel computing to generate multiple feasible trajectories efficiently.

Abstract

Multi-modal behaviors exhibited by surrounding vehicles (SVs) can typically lead to traffic congestion and reduce the travel efficiency of autonomous vehicles (AVs) in dense traffic. This paper proposes a real-time parallel trajectory optimization method for the AV to achieve high travel efficiency in dynamic and congested environments. A spatiotemporal safety module is developed to facilitate the safe interaction between the AV and SVs in the presence of trajectory prediction errors resulting from the multi-modal behaviors of the SVs. By leveraging multiple shooting and constraint transcription, we transform the trajectory optimization problem into a nonlinear programming problem, which allows for the use of optimization solvers and parallel computing techniques to generate multiple feasible trajectories in parallel. Subsequently, these spatiotemporal trajectories are fed into a multi-objective evaluation module considering both safety and efficiency objectives, such that the optimal feasible trajectory corresponding to the optimal target lane can be selected. The proposed framework is validated through simulations in a dense and congested driving scenario with multiple uncertain SVs. The results demonstrate that our method enables the AV to safely navigate through a dense and congested traffic scenario while achieving high travel efficiency and task accuracy in real time.