Planning Autonomous Vehicle Maneuvering in Work Zones Through Game-Theoretic Trajectory Generation

2026-03-19

AI Summary

This study addresses the high-risk lane-changing challenges faced by autonomous vehicles in construction zones, where constrained road geometry and unpredictable traffic behaviors significantly increase collision risks. To tackle this problem, the work proposes a novel trajectory planning framework based on non-cooperative game theory, modeling lane-change decisions as a strategic interaction among vehicles. By computing Nash equilibria, the approach generates trajectories that jointly optimize safety, traffic efficiency, and driving stability. Experimental results demonstrate that the proposed method reduces conflict frequency by 35% compared to conventional approaches and substantially decreases the occurrence of high-risk safety-critical events, thereby achieving a synergistic balance between safety and efficiency in dynamic construction zone environments.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Work zone navigation remains one of the most challenging manoeuvres for autonomous vehicles (AVs), where constrained geometries and unpredictable traffic patterns create a high-risk environment. Despite extensive research on AV trajectory planning, few studies address the decision-making required to navigate work zones safely. This paper proposes a novel game-theoretic framework for trajectory generation and control to enhance the safety of lane changes in a work zone environment. By modelling the lane change manoeuvre as a non-cooperative game between vehicles, we use a game-theoretic planner to generate trajectories that balance safety, progress, and traffic stability. The simulation results show that the proposed game-theoretic model reduces the frequency of conflicts by 35 percent and decreases the probability of high risk safety events compared to traditional vehicle behaviour planning models in safety-critical highway work-zone scenarios.

Problem

• autonomous vehicles
• work zones
• trajectory planning
• lane change
• safety

Innovation

• game-theoretic planning
• autonomous vehicle
• work zone navigation
• trajectory generation
• non-cooperative game