Abstract
Reliable anticipation of traffic accidents is essential for advancing autonomous driving systems. However, this objective is limited by two fundamental challenges: the scarcity of diverse, high-quality training data and the frequent absence of crucial object-level cues due to environmental disruptions or sensor deficiencies. To tackle these issues, we propose a comprehensive framework combining generative scene augmentation with adaptive temporal reasoning. Specifically, we develop a video generation pipeline that utilizes a world model guided by domain-informed prompts to create high-resolution, statistically consistent driving scenarios, particularly enriching the coverage of edge cases and complex interactions. In parallel, we construct a dynamic prediction model that encodes spatio-temporal relationships through strengthened graph convolutions and dilated temporal operators, effectively addressing data incompleteness and transient visual noise. Furthermore, we release a new benchmark dataset designed to better capture diverse real-world driving risks. Extensive experiments on public and newly released datasets confirm that our framework enhances both the accuracy and lead time of accident anticipation, offering a robust solution to current data and modeling limitations in safety-critical autonomous driving applications.
Abstract (translated)
可靠地预测交通事故对于推进自动驾驶系统的发展至关重要。然而，这一目标受到了两个基本挑战的限制：高质量多样化训练数据的稀缺性以及由于环境干扰或传感器缺陷而导致的重要对象级线索经常缺失的问题。为了解决这些问题，我们提出了一种结合生成场景增强和自适应时间推理的全面框架。 具体而言，我们开发了一个视频生成管道，该管道利用由领域信息提示指导的世界模型来创建高分辨率且统计上一致的驾驶场景，并特别丰富了边缘案例和复杂交互的情况。同时，我们构建了一个动态预测模型，通过加强的图卷积和膨胀的时间操作符编码空间-时间关系，有效地解决了数据不完整性和短暂视觉噪声的问题。 此外，我们发布了一套新的基准数据集，旨在更好地捕捉各种现实世界驾驶风险。在公开数据集和新发布的数据集上的广泛实验表明，我们的框架增强了事故预测的准确性和提前期，为安全关键性的自动驾驶应用提供了应对当前数据和建模限制的有效解决方案。
URL
https://arxiv.org/abs/2507.12762
