  In recent years, vision-centric perception has flourished in various
autonomous driving tasks, including 3D detection, semantic map construction,
motion forecasting, and depth estimation. Nevertheless, the latency of
vision-centric approaches is too high for practical deployment (e.g., most
camera-based 3D detectors have a runtime greater than 300ms). To bridge the gap
between ideal research and real-world applications, it is necessary to quantify
the trade-off between performance and efficiency. Traditionally,
autonomous-driving perception benchmarks perform the offline evaluation,
neglecting the inference time delay. To mitigate the problem, we propose the
Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first
benchmark to evaluate the online performance of vision-centric perception in
autonomous driving. On the basis of the 2Hz annotated nuScenes dataset, we
first propose an annotation-extending pipeline to generate high-frame-rate
labels for the 12Hz raw images. Referring to the practical deployment, the
Streaming Perception Under constRained-computation (SPUR) evaluation protocol
is further constructed, where the 12Hz inputs are utilized for streaming
evaluation under the constraints of different computational resources. In the
ASAP benchmark, comprehensive experiment results reveal that the model rank
alters under different constraints, suggesting that the model latency and
computation budget should be considered as design choices to optimize the
practical deployment. To facilitate further research, we establish baselines
for camera-based streaming 3D detection, which consistently enhance the
streaming performance across various hardware. ASAP project page:
https://github.com/JeffWang987/ASAP.
