A Decision-Making Model for Autonomous Vehicles at Urban Intersections Based on Conflict Resolution

The decision-making models that are able to deal with complex and dynamic urban intersections are critical for the development of autonomous vehicles. A key challenge in operating autonomous vehicles robustly is to accurately detect the trajectories of other participants and to consider safety and efficiency concurrently into interactions between vehicles. In this work, we propose an approach for developing a tactical decision-making model for vehicles which is capable of predicting the trajectories of incoming vehicles and employs the conflict resolution theory to model vehicle interactions. The proposed algorithm can help autonomous vehicles cross intersections safely. Firstly, Gaussian process regression models were trained with the data collected at intersections using subgrade sensors and a retrofit autonomous vehicle to predict the trajectories of incoming vehicles. Then, we proposed a multiobjective optimization problem (MOP) decision-making model based on efficient conflict resolution theory at intersections. After that, a nondominated sorting genetic algorithm (NSGA-II) and deep deterministic policy gradient (DDPG) are employed to select the optimal motions in comparison with each other. Finally, a simulation and verification platform was built based on Matlab/Simulink and PreScan. The reliability and effectiveness of the tactical decision-making model was verified by simulations. The results indicate that DDPG is more reliable and effective than NSGA-II to solve the MOP model, which provides a theoretical basis for the in-depth study of decision-making in a complex and uncertain intersection environment.