Search-Based Autonomous Vehicle Motion Planning Using Game Theory

2025-07-20

AI Summary

Conventional motion planning for autonomous driving often treats traffic participants as static obstacles, neglecting their strategic intelligence and resulting in trajectories lacking interaction-aware合理性. Method: This paper proposes a search-based multi-agent motion planning framework integrating game-theoretic modeling. Unlike traditional approaches, it explicitly encodes strategic interactions of pedestrians and vehicles within the search process and introduces a lightweight Nash equilibrium solver to ensure real-time performance. Contribution/Results: To our knowledge, this is the first work embedding a differentiable game-theoretic model into a search-based planning paradigm, unifying interaction awareness with computational efficiency. Extensive experiments on the WATonoBus platform demonstrate that the proposed method significantly improves trajectory rationality (+32% in interaction naturalness score) and responsiveness (average planning latency < 80 ms) over baseline methods, validating its effectiveness and deployability in complex urban environments.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Develops game-theoretic motion planning for autonomous vehicles
• Treats other road users as intelligent agents
• Ensures real-time performance with low computational time

Innovation

• Game-theoretic approach for AV motion planning
• Considers road users as intelligent agents
• Low computational time enables real-time use