Intention Prediction and Mixed Strategy Nash Equilibrium-Based Decision-Making Framework for Auton

论文标题：Intention Prediction and Mixed Strategy Nash Equilibrium-Based Decision-Making Framework for Autonomous Driving in Uncontrolled Intersection

作者单位：Transportation Science and Engineering, Beihang University

发表期刊：IEEE Transactions on Vehicular Technology, 2022

摘要：Decision-making in uncontrolled intersection is one of the main challenges in urban autonomous driving. This paper proposed a new decision-making framework in uncontrolled intersection based on the intention prediction method and Mixed Strategy Nash Equilibrium theory. The framework is a three-stage method: target vehicle motion prediction, driving mode decision, and motion planning. The driving intention (left turn, right turn, or go straight) of the target vehicle at the intersection can be predicted using the combination algorithm of GMM-HMM and SVM. According to the driving intention and road structure, the trajectory fitting module would use the Bezier curve to fit the predicted trajectory of a target vehicle. Furthermore, combined with trajectories of the ego vehicle, the S-T diagram is used to judge whether there is a spatio-temporal conflict point of the target vehicle and ego vehicle. If there is a conflict point of the target vehicle and ego vehicle, the driving mode (‘yield’ or ‘cross’) of the ego vehicle is selected by using the Mixed Strategy Nash Equilibrium theory. This method can not only avoid premature or unnecessary deceleration due to conservation but also avoid a collision or violent deceleration due to greed. According to thedriving mode, the planning module uses the model predictive control algorithm to determine the optimal acceleration strategy. Have been verified by the vehicle test, it indicates that the proposed decision-making framework can make ego vehicles pass through the intersection safely and comfortably.

关键词：Autonomous driving, Mixed Strategy Nash Equilibrium theory, model predictive control and intention prediction.