Planning Autonomous Vehicle Maneuvering in Work Zones Through Game-Theoretic Trajectory Generation
This addresses safety challenges for autonomous vehicles in high-risk work zone environments, representing an incremental improvement over existing methods.
The paper tackles the problem of autonomous vehicle maneuvering in work zones by proposing a game-theoretic framework for trajectory generation, resulting in a 35% reduction in conflict frequency and decreased probability of high-risk safety events compared to traditional models.
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.