Abstract
This paper addresses the problem of anticipating traffic accidents, which aims to forecast potential accidents before they happen. Real-time anticipation is crucial for safe autonomous driving, yet most methods rely on computationally heavy modules like optical flow and intermediate feature extractors, making real-world deployment challenging. In this paper, we thus introduce RARE (Real-time Accident anticipation with Reused Embeddings), a lightweight framework that capitalizes on intermediate features from a single pre-trained object detector. By eliminating additional feature-extraction pipelines, RARE significantly reduces latency. Furthermore, we introduce a novel Attention Score Ranking Loss, which prioritizes higher attention on accident-related objects over non-relevant ones. This loss enhances both accuracy and interpretability. RARE demonstrates a 4-8 times speedup over existing approaches on the DAD and CCD benchmarks, achieving a latency of 13.6ms per frame (73.3 FPS) on an RTX 6000. Moreover, despite its reduced complexity, it attains state-of-the-art Average Precision and reliably anticipates imminent collisions in real time. These results highlight RARE's potential for safety-critical applications where timely and explainable anticipation is essential.
Abstract (translated)
本文解决的是预测交通事故的问题，旨在在事故发生前进行预测。实时预测对于自动驾驶的安全性至关重要，然而大多数方法依赖于计算量大的模块（如光流和中间特征提取器），这使得实际部署变得颇具挑战。为此，在本文中我们提出了RARE（Real-time Accident anticipation with Reused Embeddings），这是一个轻量级框架，利用了单一预训练对象检测器中的中间特征。通过消除额外的特征提取管道，RARE显著减少了延迟。 此外，我们还引入了一种新的注意力得分排序损失函数，该损失函数优先关注与事故相关的物体而非无关物体，从而提高了准确性和可解释性。在DAD和CCD基准测试上，RARE比现有的方法快4到8倍，在RTX 6000上实现了每帧13.6毫秒（73.3 FPS）的延迟。并且尽管其复杂度降低，它仍达到了最先进的平均精度，并且能够实时可靠地预测即将发生的碰撞。 这些结果突显了RARE在需要及时和可解释性的安全关键应用中的潜力。
URL
https://arxiv.org/abs/2505.17449
