Tong, Kailin (Virtual Vehicle Research), Dikic, Berin (Virtual Vehicle Research GmbH), Xiao, Wenbo (Tongji University, Graz University of Technology), Steinberger, Martin (TU Graz), Horn, Martin (TU Graz), Solmaz, Selim (Virtual Vehicle Research GmbH)

Safety Metric Aware Trajectory Repairing for Automated Driving

Scheduled for presentation during the Regular Session "Advanced Vehicle Safety Systems II" (ThAT8), Thursday, September 26, 2024, 11:50−12:10, Salon 16

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

Recent analyses highlight challenges in autonomous vehicle technologies, particularly failures in decision-making under dynamic or emergency conditions. Traditional automated driving systems recalculate the entire trajectory in a changing environment. Instead, a novel approach retains valid trajectory segments, minimizing the need for complete replanning and reducing violations of the original plan. This work introduces a trajectory repairing framework that calculates a feasible evasive trajectory while computing the Feasible Time-to-React (F-TTR), balancing the maintenance of the original plan with safety assurance. The framework employs a binary search algorithm to iteratively create repaired trajectories, guaranteeing both the safety and feasibility of the trajectory repairing result. In contrast to earlier approaches that separated the calculation of safety metrics from trajectory repairing, which resulted in unsuccessful plans for evasive maneuvers, our work has the anytime capability to provide both a Feasible Time-to-React and an evasive trajectory for further execution.