VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning

Understand

Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging.

• Existing learning-based planning methods follow a deterministic paradigm to directly regress the action, failing to cope with the uncertainty problem.
• In this work, we propose a probabilistic planning model for end-to-end autonomous driving, termed VADv2.
• We resort to a probabilistic field function to model the mapping from the action space to the probabilistic distribution.