A Sampling-Based Approach to Urban Motion Planning Games with Stochastic Dynamics

作     者：Khayyat, Michael Bolognani, Saverio Arrigoni, Stefano Braghin, Francesco 

作者机构：Department of Mechanical Engineering Politecnico di Milano Milano MI Italy Automatic Control Lab ETH Zürich Zürich Switzerland 

出 版 物：《IEEE Transactions on Intelligent Vehicles》 (IEEE Trans. Intell. Veh.)

年 卷 期：2024年

页      面：1-15页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0810[工学-信息与通信工程] 0711[理学-系统科学] 081203[工学-计算机应用技术] 0835[工学-软件工程] 0714[理学-统计学（可授理学、经济学学位）] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 082301[工学-道路与铁道工程] 081202[工学-计算机软件与理论] 

主　　题：Random processes 

摘      要：Urban driving is a challenging task that requires autonomous agents to account for the stochastic dynamics and interactions with other vehicles. In this paper, we propose a novel framework that models urban driving as a stochastic generalized Nash equilibrium problem (SGNEP) and solves it using information-theoretic model predictive control (IT-MPC). By exploiting the cooperative nature of urban driving, we transform the SGNEP into a stochastic potential game (SPG), which has desirable convergence guarantees. Furthermore, we provide an algorithm for isolating interacting vehicles and thus factorizing a game into multiple sub-games. Finally, we solve for the open-loop generalized Nash equilibrium of a stochastic game utilizing a sampling-based technique. We solve the problem in a receding-horizon fashion, and apply our framework to various urban scenarios, such as intersections, lane merges, and ramp merges, and show that it can achieve safe and efficient multi-agent navigation. IEEE