Time-to-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles

TL;DR

This paper introduces a time-to-collision-aware lane-change strategy for autonomous vehicles that improves safety and efficiency by generating shorter, smoother trajectories using potential fields and cubic polynomial fitting.

Contribution

It proposes a novel TTCA-LC method integrating TTC constraints into potential field-based planning, enhancing safety and trajectory quality during lane changes.

Findings

Trajectory length reduced by over 27.1%

Curvature decreased by approximately 56.1%

Outperforms conventional PF-based lane change methods

Abstract

Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1\%, and the curvature is reduced by approximately 56.1\% compared with the CPF-LC method.