Game Theoretic Decision-Making for Autonomous Driving Considering Traffic Rules at Roundabouts

Vehicle interactions at roundabouts are complex, with significant issues arising from right-of-way assignments and the uncertainty of driver behavior. A decentralized game-theoretic decision-making model is proposed, integrating traffic rules to effectively manage complex vehicle interactions in the dynamic traffic scenarios. Linear Temporal Logic (LTL) is used to digitize traffic rules, and vehicle trajectories are extracted from the INTERACTION dataset. These data serve as input for the random forest model to determine the ambiguous thresholds in the digitized traffic rules. These rules are then transformed into constraints and cost functions in the game-theoretic model to enhance decision-making safety and rule compliance. The performances of the proposed decision-making model are validated on the roundabout scenes extracted from the INTERACTION dataset. The results indicate that our model surpasses others in terms of safety, efficiency, and comfort, enabling safe and efficient decision-making in complex traffic scenarios, thus improving the decision-making performance and adaptability of autonomous driving systems at roundabout.