World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving

• URL: http://arxiv.org/abs/2507.12762v1
• Date: Thu, 17 Jul 2025 03:34:54 GMT
• Title: World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving
• Authors: Yanchen Guan, Haicheng Liao, Chengyue Wang, Xingcheng Liu, Jiaxun Zhang, Zhenning Li,
• Abstract summary: We propose a comprehensive framework combining generative augmentation scene with adaptive temporal reasoning.<n>We develop a video generation pipeline that utilizes a world model by guided domain-informed prompts to create high-resolution, statistically consistent driving scenarios.<n>In parallel, we construct a dynamic prediction model that encodes-temporal relationships through strengthened graph convolutions and dilated temporal operators.
• Score: 1.8277374107085946
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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