Decision Making of Automated Vehicles in Mixed Environment Based on Bayesian Sequential Games

Automated Vehicles (AVs) will coexist with Human-Driven Vehicles (HDVs) for a long time. AVs must navigate safely among HDVs while maintaining smooth traffic flow. To facilitate this, the decision making system of AVs must accurately assess HDV intentions while accounting for inherent uncertainties. Current HDV intention prediction models often misclassify these intentions, leading to unsafe navigation decisions. This study introduces a three-stage Bayesian sequential game-based decision making architecture designed for AV operation. In the first stage, the AV utilizes a temporal neural network to classify vehicle intentions. In the second stage, a sequential game is solved to determine optimal actions by predicting future HDV states. The final stage, serving as a validation stage, identifies and corrects misclassifications from the first stage by predicting HDV future positions, incorporating models that account for potential deviations from the ground truth. Simulation results indicate a $93.5\pm 0.5$ % accuracy in initial intention predictions, facilitating swift and effective decision making. The validation stage further enhances safety by promptly correcting errors, ensuring reliable navigation for AVs in HDV environments.