Deep Object-Centric Policies for Autonomous Driving [article]

Dequan Wang, Coline Devin, Qi-Zhi Cai, Fisher Yu, Trevor Darrell
2019 arXiv   pre-print
While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways. For robotics tasks, such as 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
more » ... m 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, where an object-centric model outperforms object-agnostic models in the low-data regimes.
arXiv:1811.05432v2 fatcat:eejtsmk44babjhn7jkifuudr74