Search-Based Autonomous Vehicle Motion Planning Using Game Theory

TL;DR

This paper introduces a real-time, game-theoretic search-based motion planning method for autonomous vehicles that models other road users as intelligent agents, resulting in more realistic paths and validated through experiments.

Contribution

It presents a novel game-theoretic approach to search-based motion planning that considers other road users as intelligent agents, improving realism and computational efficiency.

Findings

The proposed method generates more realistic paths than traditional approaches.

It operates in real-time suitable for practical applications.

Experimental validation confirms improved performance over existing techniques.

Abstract

In this paper, we propose a search-based interactive motion planning scheme for autonomous vehicles (AVs), using a game-theoretic approach. In contrast to traditional search-based approaches, the newly developed approach considers other road users (e.g. drivers and pedestrians) as intelligent agents rather than static obstacles. This leads to the generation of a more realistic path for the AV. Due to the low computational time, the proposed motion planning scheme is implementable in real-time applications. The performance of the developed motion planning scheme is compared with existing motion planning techniques and validated through experiments using WATonoBus, an electrical all-weather autonomous shuttle bus.