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 this https URL

通过大规模驾驶示范学习人类驾驶行为模型，提出基于概率规划的 VADv2 端到端驾驶模型，解决不确定性问题，在 CARLA Town05 基准测试中表现优异，在没有基于规则的封装的情况下稳定运行。