Potential Field-based Path Planning with Interactive Speed Optimization for Autonomous Vehicles

TL;DR

This paper introduces a potential field-based path planning method with interactive speed optimization for autonomous vehicles, utilizing vehicle-to-vehicle communication to improve safety and comfort by considering the impact of the ego vehicle's path on others.

Contribution

It proposes a novel PF-based path planning approach that incorporates shared local paths and an interactive speed optimization to enhance AV coordination and safety.

Findings

Reduces oscillations in vehicle trajectories.

Improves safety and comfort in merging scenarios.

Effectively mitigates impact of obstacle vehicles' PFs.

Abstract

Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle's PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to-vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs' PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.