用预训练检测器的中间特征，实时预测交通事故。
Real-time Traffic Accident Anticipation with Feature Reuse
- 复用单个检测器的中间特征，省去额外计算模块。
- 在DAD和CCD数据集上实现13.6ms/帧（73.3 FPS）的低延迟。
- 引入注意力排序损失，提升事故相关物体的识别准确率与可解释性。
本文针对交通事故提前预测问题，提出轻量级框架RARE（Real-time Accident anticipation with Reused Embeddings），利用单个预训练目标检测器的中间特征，避免了额外的特征提取开销，显著降低延迟。同时引入一种新型注意力分数排序损失，强化对事故相关物体的关注，提升预测准确性和可解释性。在DAD和CCD基准测试中，RARE相比现有方法提速4-8倍，单帧处理延迟仅为13.6ms（73.3 FPS），部署于RTX 6000显卡。尽管模型复杂度降低，仍达到当前最优平均精度，实现实时、可靠的碰撞预警。结果表明RARE在需要及时且可解释预测的安全关键场景中具有重要应用潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。