Real-time Traffic Accident Anticipation with Feature Reuse

• URL: http://arxiv.org/abs/2505.17449v1
• Date: Fri, 23 May 2025 04:09:26 GMT
• Title: Real-time Traffic Accident Anticipation with Feature Reuse
• Authors: Inpyo Song, Jangwon Lee,
• Abstract summary: We introduce a lightweight framework that capitalizes on intermediate features from a single pre-trained object detector.<n>RARE achieves a 4-8 times speedup over existing approaches on the DAD and CCD benchmarks.<n>Despite its reduced complexity, it attains state-of-the-art Average Precision and reliably anticipates imminent collisions in real time.
• Score: 2.9803250365852443
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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