Applying Deep Learning to the Safety of Autonomous Ground Vehicles

将深度学习应用于自主地面车辆的安全

• 批准号: RGPIN-2021-03893
• 负责人: Ren, Jing
• 金额: $ 2.04万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752818
• 关键词: Applying; Deep; Learning; Safety; Autonomous

Recently, the research of autonomous ground vehicles has moved out of the lab to local traffic and even highways. While autonomous driving has more and more become the reality, road safety with autonomous vehicles is more of a concern for manufactures, policy makers and the public. At present, vehicle accidents result in many human fatalities and injuries every year, and significant economic loss. According to statistics, accidents occur mainly due to three critical reasons: driver errors, environment factors and vehicle components' failure or degradation. Autonomous vehicles, in theory, can excel over human drivers in almost all accident scenarios and result in much fewer car accidents. However, it needs a significant amount of research work to realize its full potential for road safety. Many techniques from different disciplines have been developed and applied to autonomous vehicles. In the past decade, on another research front, we have witnessed a boom of deep learning (DL) techniques, including deep reinforcement learning (DRL), deep auto-encoders, deep convolutional neural networks, long short-term memory, and deep belief networks. These methods have recently dramatically pushed forward the state of the art in diverse domains such as robotics, computer vision and control. These deep networks have a great potential to achieve a much better performance than traditional techniques in many research fronts including autonomous vehicles design and operation. Given the advantages of autonomous vehicles and the rapid advances of artificial intelligence or deep learning, the combination of these technologies has a great potential to achieve a near accident-free autonomous driving experience by applying deep learning techniques to many challenging driving scenarios. My five-year plan is to develop and apply novel deep learning algorithms and deep neural network architectures for collision-free path planning, fault detection and fault diagnosis, and road emergency detection and control. I will investigate along the following three research directions: 1) Collision-free path planning with dynamic obstacles using DRL, 2) Sensor fusion based road emergency detection using DL, and 3) Fault detection and fault diagnosis using DL. I believe that the successful completion of these projects will greatly improve the performance of autonomous ground vehicles and the road safety, and pave the way for a near accident-free autonomous driving experience. It will save human lives and costs resulting from the car accidents. It will also be of great benefit to vehicle manufacturers, insurance companies and the general public.

最近，自动驾驶地面车辆的研究已经走出实验室，走向了当地交通甚至高速公路。随着自动驾驶越来越成为现实，自动驾驶汽车的道路安全越来越受到制造商、政策制定者和公众的关注。目前，交通事故每年造成大量人员伤亡，造成重大经济损失。据统计，事故的发生主要有三个关键原因：驾驶员失误、环境因素和车辆部件失效或退化。从理论上讲，自动驾驶汽车在几乎所有事故场景中都优于人类驾驶员，并且导致的车祸要少得多。然而，它需要大量的研究工作才能充分发挥其在道路安全方面的潜力。来自不同学科的许多技术已被开发并应用于自动驾驶汽车。在过去的十年里，在另一个研究前沿，我们见证了深度学习（DL）技术的蓬勃发展，包括深度强化学习（DRL）、深度自动编码器、深度卷积神经网络、长短期记忆和深度信念网络。这些方法最近在机器人、计算机视觉和控制等不同领域极大地推动了技术的发展。这些深度网络在包括自动驾驶汽车设计和操作在内的许多研究领域具有比传统技术更好的性能潜力。考虑到自动驾驶汽车的优势和人工智能或深度学习的快速发展，这些技术的结合有很大的潜力，通过将深度学习技术应用于许多具有挑战性的驾驶场景，实现近乎无事故的自动驾驶体验。我的五年计划是开发和应用新的深度学习算法和深度神经网络架构，用于无碰撞路径规划，故障检测和故障诊断，道路应急检测和控制。我将沿着以下三个研究方向进行研究：1)基于DRL的动态障碍物无碰撞路径规划，2)基于深度学习的基于传感器融合的道路应急检测，以及3)基于深度学习的故障检测和故障诊断。我相信，这些项目的成功完成将大大提高自动驾驶地面车辆的性能和道路安全性，为接近零事故的自动驾驶体验铺平道路。它将节省人的生命和成本，从汽车事故。这对汽车制造商、保险公司和公众也有很大的好处。