Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula

Understand

ML-based motion planning is a promising approach to produce agents that exhibit complex behaviors, and automatically adapt to novel environments.

• In the context of autonomous driving, it is common to treat all available training data equally.
• However, this approach produces agents that do not perform robustly in safety-critical settings, an issue that cannot be addressed by simply adding more data to the training set - we show that an agent trained using only a 10% subset of the data performs just as well as an agent trained on the entire dataset.
• We present a method to predict the inherent difficulty of a driving situation given data collected from a fleet of autonomous vehicles deployed on public roads.