The self-driving car has long been a dream—one that we are getting closer and closer to attaining. Research in deep neural networks has achieved promising progress in various tasks in perception, control, and planning, which are central to implementing autonomous driving, but recent research has primarily been focused on improving accuracy. To actually deploy neural network models in autonomous vehicles, we also need to deal with other critical issues such as latency, energy efficiency, and model size. Bichen Wu explores perception tasks for autonomous driving and explains how to design efficient neural networks to address these critical issues.
Bichen Wu is a PhD student at UC Berkeley. His research focuses on deep learning, computer vision, and autonomous driving.
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