Intelligent Decision Making in Autonomous Vehicles using Cognition Aided Reinforcement Learning

As recent advances in sensing, computing, and communications expedite proliferation of autonomous vehicles (AV), their sharing the road with human driven vehicles presents a challenge that demands urgent investigation. AVs can excel at deterministic programmed behavior, still human drivers have the edge because of the faculty of cognition, which evolved over millennia. This paper presents Cognition Aided Reinforcement Learning (CARL) algorithm that harnesses inputs from five principles of cognition - memory, attention, language, perception, and intelligence. Sensors build perception, data facilitate memory, and safety messages enable language support. Intelligence fuses information with attention focused on specific actions for reward maximization. Simulation results show CARL to be 10 times faster as compared to the state of the art model-free reinforcement learning algorithms. Additionally, by using the principle of metacognition (art of learning how to learn), CARL achieves optimal rewards in a heterogeneous environment composed of vehicles with varying degrees of autonomy.