A Data-Driven Optimal Control Decision-Making System for Multiple Autonomous Vehicles

Abstract

With the fast development and rising popularity of autonomous vehicle (AV) technology, multiple AVs may soon be driving on the same road simultaneously. Such multi-AV coexistence driving situations will lead to new and persistent challenges. Therefore, improvements on making control decisions for multiple AVs becomes necessary for continued driving safety. In this paper, we propose a multi-AV decision making system (MADM), which considers multi-AV coexistence driving situations during the decision-making process. In MADM, we first build a policy formation method to generate policies that learn the driving behaviors of an expert based on the expert's driving trajectory data. We then develop a multi-AV decision-making method, which adjusts the formed policies through multi-agent reinforcement learning. The adjusted policies make control decisions for multiple AVs with safety guarantee. We used a real-world traffic dataset to evaluate the decision making performance of MADM in comparison with several state-of-the-art methods. Experimental results show that MADM reduces emergency rate by as high as 51% when compared with existing methods.