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

使用深度强化学习对支持 V2X 的自动驾驶车辆进行智能集成控制

• 批准号: RGPIN-2021-02839
• 负责人: Lei, Lei
• 金额: $ 2.4万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742057
• 关键词: Intelligent; Integrated; Control; V2X; Enabled

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.

不可靠的决策系统导致的备受瞩目的自动驾驶汽车故障阻碍了它们的普及。这些故障通常是由于车辆的车载传感器获得的关于周围环境的有限和不可靠的信息。通过利用车辆到一切(V2X)通信，可以克服这一“感官”挑战，这将使自动驾驶车辆能够彼此之间、附近的基础设施和环境进行无线通信。当通过V2X通信接收到的关键信息与典型的感知数据相结合时，自动驾驶汽车可以实现对周围环境更准确和更全面的了解。为了实现自动驾驶和V2X通信的完美结合，需要一个智能化、集成化的控制决策系统。然而，如何在一个集成的框架内联合优化车辆控制和通信控制，以实现安全高效的自动驾驶，是一个尚未解决的挑战。该系统需要包括两种类型的控制：(1)车辆的自动驾驶控制；(2)车辆网络的通信控制。与传统的控制方法相比，深度强化学习(DRL)可以将环境智能引入到互联和自动驾驶车辆中。我提出的研究计划将为优化基于DRL的智能集成控制决策系统提供一个框架。它将包括三个活动：基于DRL的增强感知的车辆控制和通过V2X进行协作。我将开发DRL算法，它可以学习“通信什么以及何时通信”，以及如何在理想的免费通信的假设下，利用通信的信息来做出智能车辆控制决策。具有非理想V2X通信的基于DRL的稳健车辆控制。我将研究通信对改善控制性能的益处和通信成本之间的权衡，目的是在“通信什么和何时通信”问题上做出最佳决策。此外，我将为自动驾驶开发健壮且安全的DRL算法，以处理由于非理想的V2X通信而产生的不确定性。基于DRL的车辆和无线电资源联合控制，实现自动驾驶和V2X通信的完美结合。如何通信，即如何控制稀缺的无线电资源，与车辆控制密切相关，因为通信性能将影响驾驶感知。我的目标是考虑更紧密地整合这两种类型的控制，以实现支持V2X的自动驾驶的全球最优解决方案。通过上述活动，我的研究计划承诺提高自动驾驶汽车的可靠性，从而加快其采用速度，缓解道路拥堵，降低燃油消耗，并为乘客提供更舒适、更安全的体验。