Helping Automated Vehicles With Left-Turn Maneuvers: A Game Theory-Based Decision Framework for Conflicting Maneuvers at Intersections

The deployment of connected, automated vehicles (CAVs) provides the opportunity to enhance the safety and efficiency of transportation systems. However, despite the rapid development of this technology, human-driven vehicles are predicted to predominate the vehicle fleet, compelling CAVs to be able to operate in a mixed traffic environment. The key to achieving a reliable and safe human-CAV collaboration in such environments is to characterize the interactions between the actors and incorporate the underlying decision-making mechanism of human drivers into CAVs' motion planning algorithms. Towards this goal and extending a previously developed game theoretical model, the present study proposes a decision-making dynamic to achieve more realistic models of human behavior when making conflicting maneuvers at intersections. A novel field test is conducted to extract the required modeling data directly from CAVs' perception system, facilitating the incorporation of the model into CAV navigation algorithms. Model validation and sensitivity analysis provided invaluable insights into the nature of human decisions and indicated that the proposed structure is robust to environmental uncertainties and can well capture the real-world behavior of human drivers in unprotected left-turn maneuvers. The derived knowledge can be directly used in CAV motion planning algorithms to provide the vehicle with more accurate predictions of human actions when operating in mixed traffic environments.

Automated summary

The study aims to simulate human behavior at intersections more realistically by proposing a decision-making dynamic and extracting modeling data from CAVs' perception system. Through validation and sensitivity analysis, the proposed model demonstrates robustness to environmental uncertainties and accurately captures human drivers' real-world behavior in unprotected left-turn maneuvers.