End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

Despite significant recent progress in the field of autonomous driving modern methods still
struggle and can incur serious accidents when encountering long-tail unforeseen events …

In contrast to extensive studies on general vision pre-training for scalable visual
autonomous driving remains seldom explored. Visual autonomous driving applications …

End-to-end driving systems have recently made rapid progress, in particular on CARLA.
Independent of their major contribution, they introduce changes to minor system …

End-to-end autonomous driving aims to build a fully differentiable system that takes raw
sensor data as inputs and directly outputs the planned trajectory or control signals of the ego …

World models can foresee the outcomes of different actions, which is of paramount
importance for autonomous driving. Nevertheless, existing driving world models still have …

Real-world autonomous driving (AD) like urban driving involves many corner cases. The
lately released AD Benchmark CARLA Leaderboard v2 (aka CARLA v2) involves 39 new …

Benchmarking vision-based driving policies is challenging. On one hand, open-loop
evaluation with real data is easy, but these results do not reflect closed-loop performance …

The advances in multimodal large language models (MLLMs) have led to growing interests
in LLM-based autonomous driving agents to leverage their strong reasoning capabilities …