Intelligent and Integrated Control of V2X-Enabled Autonomous Vehicles using Deep Reinforcement Learning

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High-profile autonomous vehicle failures caused by unreliable decision making systems have impeded their uptake. These failures are often due to the limited and unreliable information obtained by the vehicle's on-board sensors about its surrounding environment. This "sensory" challenge can be overcome by leveraging vehicle-to-everything (V2X) communications, which would enable autonomous vehicles to communicate wirelessly with each other, nearby infrastructure and the environment. Autonomous vehicles can achieve a more accurate and comprehensive picture of its surrounding when the critical information received through V2X communications is combined with the typical sensory data. An intelligent and integrated control decision making system is required to realize the perfect combination of autonomous driving and V2X communications. However, how to jointly optimize vehicle control and communication control in an integrated framework toward safe and efficient autonomous driving is an unsolved challenge. The system would need to include two types of controls: (1) autonomous driving control of the vehicles and; (2) communication control of the vehicular networks. Compared with the traditional control approaches, Deep Reinforcement Learning (DRL) can introduce ambient intelligence into connected and autonomous vehicles. My proposed research program will provide a framework to optimize an intelligent and integrated control decision making system based on DRL. It will span three activities: DRL-based Vehicle Control with Enhanced Perception and Cooperation through V2X. I will develop DRL algorithms that can learn "what and when to communicate", jointly with how to leverage the communicated information to make intelligent vehicle control decisions under the assumption of ideal communications with no cost. Robust DRL-based Vehicle Control with Non-Ideal V2X Communications. I will investigate the trade-off between the benefit of communications to improve control performance and the cost of communications, with the objective of making optimal decisions on the "what and when to communicate" problem. Moreover, I will develop robust and safe DRL algorithms for autonomous driving that can handle the uncertainty due to non-ideal V2X communications. Joint DRL-based Vehicle and Radio Resource Control toward Perfect Combination of Autonomous Driving and V2X Communications. "How to communicate", i.e., how to control the scarce radio resources, is closely interrelated with vehicle control, as the communication performance will impact the driving perception. My goal is to consider tighter integration of the two types of control to achieve a global optimal solution for V2X-enabled autonomous driving. Through the above activities, my research program promises to improve the reliability of autonomous vehicles thereby accelerating their adoption, easing road congestion, reducing fuel consumption, and providing a more comfortable and safer experience for passengers.