Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?
Zhiqi Li Zhiding Yu Shiyi Lan Jiahan Li Jan Kautz Tong Lu Jose M. Alvarez
End-to-end autonomous driving recently emerged as a promising research
direction to target autonomy from a full-stack perspective. Along this line,
many of the latest works follow an open-loop evaluation setting on nuScenes to
study the planning behavior. In this paper, we delve deeper into the problem by
conducting thorough analyses and demystifying more devils in the details. We
initially observed that the nuScenes dataset, characterized by relatively
simple driving scenarios, leads to an under-utilization of perception
information in end-to-end models incorporating ego status, such as the ego
vehicle's velocity. These models tend to rely predominantly on the ego
vehicle's status for future path planning. Beyond the limitations of the
dataset, we also note that current metrics do not comprehensively assess the
planning quality, leading to potentially biased conclusions drawn from existing
benchmarks. To address this issue, we introduce a new metric to evaluate
whether the predicted trajectories adhere to the road. We further propose a
simple baseline able to achieve competitive results without relying on
perception annotations. Given the current limitations on the benchmark and
metrics, we suggest the community reassess relevant prevailing research and be
cautious whether the continued pursuit of state-of-the-art would yield
convincing and universal conclusions. Code and models are available at
\url{https://github.com/NVlabs/BEV-Planner}