  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. In this work, to cope with the uncertainty problem, we propose
VADv2, an end-to-end driving model based on probabilistic planning. VADv2 takes
multi-view image sequences as input in a streaming manner, transforms sensor
data into environmental token embeddings, outputs the probabilistic
distribution of action, and samples one action to control the vehicle. Only
with camera sensors, VADv2 achieves state-of-the-art closed-loop performance on
the CARLA Town05 benchmark, significantly outperforming all existing methods.
It runs stably in a fully end-to-end manner, even without the rule-based
wrapper. Closed-loop demos are presented at https://hgao-cv.github.io/VADv2.
