  Accurately and promptly predicting accidents among surrounding traffic agents
from camera footage is crucial for the safety of autonomous vehicles (AVs).
This task presents substantial challenges stemming from the unpredictable
nature of traffic accidents, their long-tail distribution, the intricacies of
traffic scene dynamics, and the inherently constrained field of vision of
onboard cameras. To address these challenges, this study introduces a novel
accident anticipation framework for AVs, termed CRASH. It seamlessly integrates
five components: object detector, feature extractor, object-aware module,
context-aware module, and multi-layer fusion. Specifically, we develop the
object-aware module to prioritize high-risk objects in complex and ambiguous
environments by calculating the spatial-temporal relationships between traffic
agents. In parallel, the context-aware is also devised to extend global visual
information from the temporal to the frequency domain using the Fast Fourier
Transform (FFT) and capture fine-grained visual features of potential objects
and broader context cues within traffic scenes. To capture a wider range of
visual cues, we further propose a multi-layer fusion that dynamically computes
the temporal dependencies between different scenes and iteratively updates the
correlations between different visual features for accurate and timely accident
prediction. Evaluated on real-world datasets--Dashcam Accident Dataset (DAD),
Car Crash Dataset (CCD), and AnAn Accident Detection (A3D) datasets--our model
surpasses existing top baselines in critical evaluation metrics like Average
Precision (AP) and mean Time-To-Accident (mTTA). Importantly, its robustness
and adaptability are particularly evident in challenging driving scenarios with
missing or limited training data, demonstrating significant potential for
application in real-world autonomous driving systems.
