Deep Object Centric Policies for Autonomous Driving

Dequan Wang, Coline Devin, Qi-Zhi Cai, Fisher Yu, Trevor Darrell

Abstract:

While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways. For autonomous driving, models that explicitly represent objects may be more robust to new scenes
and provide intuitive visualizations. We describe a taxonomy of “object-centric" models which leverage both object instances and end-to-end learning. In the Grand Theft Auto V simulator, we show that object-centric models outperform object-agnostic methods in scenes with other vehicles and pedestrians, even with an imperfect detector. We also demonstrate that our architectures perform well on real-world environments by evaluating on the Berkeley DeepDrive Video dataset.