Applying Deep Learning to the Safety of Autonomous Ground Vehicles

点击翻译按钮获取中文摘要

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.