Dissertation Talk: End to End Learning in Autonomous Driving System
Seminar | December 16 | 2-3 p.m. | 405 Soda Hall
Yang Gao, EECS Department
Autonomous driving has attracted a lot of attention in the past few years. A typical self-driving system needs manual design and coordination across multiple modules, such as perception, behavior prediction, planning and trajectory generation. Those manually designed information flow might be far from optimal. Deep learning has dramatically improved image recognition performance by replacing the manually developed pipeline. Inspired by this revolution, we study the possibility of end-to-end approach in autonomous driving. This talk contains three parts. First, we investigate whether the end-to-end method can learn complex behaviors in urban driving scenarios. After that, we talked about how to run the above-trained agent on a real vehicle. In the end, we introduce a new training scheme that combines imitation learning and reinforcement learning in a unified framework, which could achieve high sample efficiency and promising performance at the same time.