A Motion Planning Framework Considering Opportunity Costs Based on Stackelberg Games in Interactive Scenarios
Chaojie Zhang, Jun Wang, Q. Zh. Liu · IEEE Transactions on Intelligent Transportation Systems · 2025
Autonomous vehicles face significant motion planning challenges in interactive scenarios, particularly when coordinating with dynamic traffic participants. Key issues include vehicle interdependencies, dynamic priority allocation, and real-time interactions, which critically impact traffic safety and efficiency. To address these issues, we propose a Stackelberg game-theoretic motion planning framework incorporating opportunity cost—an economic concept representing the value of the best alternative strategy foregone when making a decision. Firstly, a hybrid path planner is developed to capture the interdependencies among traffic participants. Secondly, road priority during interactions is quantified through a leader-follower model. Thirdly, a Stackelberg game-based speed planner is designed to enable the autonomous vehicle to adapt to interactive environments. The payoff function, which incorporates opportunity costs via reactive speed planning, is validated using the nuPlan dataset, and its parameters are identified accordingly. The framework facilitates decision-making through exploratory planning outcomes, effectively bridging the gap between decision and planning modules. It significantly enhances driving efficiency at intersections and demonstrates strong adaptability across various interactive scenarios in both simulations and real-world experiments.