AccidentSim is a framework that generates physically realistic vehicle collision videos by extracting physical clues from real-world accident reports. It uses a reliable physical simulator to replicate post-collision trajectories, builds a trajectory dataset, fine‑tunes a language model to predict consistent trajectories from user prompts, and finally renders high‑quality videos with Neural Radiance Fields. The resulting videos show strong visual and physical authenticity compared to existing methods.
By Xiangwen Zhang, Qian Zhang, Longfei Han, Qiang Qu, Xiaoming Chen, Weidong Cai
arXiv:2609.39486v1 Announce Type: new
Abstract: Estimating accident mechanics from real-world crashes is important for vehicle-safety analysis, injury modeling, crash-severity prediction, and operati...
By Ond\v{r}ej Valach, V\'aclav Divi\v{s}, Ivan Gruber
arXiv:2608.29759v1 Announce Type: cross
Abstract: We present SynCrash, a multi-stage pipeline for zero-shot accident detection, spatial localization, and collision-type classification in fixed-view C...
By Arkya Jyoti Bagchi, Ritul Jangir, Varun Raskar
arXiv:2608. 19380v1 Announce Type: new Abstract: While modern autonomous driving systems excel at perception tasks such as object detection and trajectory prediction, they lack the high-level causal reasoning required to interpret traffic accidents.
By Sparsh Garg, Yi-Wen Chen, Vijay Kumar B G, Abhishek Aich
arXiv:2608. 04776v1 Announce Type: new Abstract: The ability to accurately assess and anticipate risks in safety-critical scenarios is crucial for autonomous driving systems.
By Yu Zhao, Jiangyu Pan, Tao Hu, Ming Yin, Fan Yang, Jiangfan Liu, Xiubo Liang
arXiv:2607. 08745v1 Announce Type: new Abstract: Recent advances in Vision-Language Models, Large Language Models, and Multimodal Large Language Models have improved autonomous driving tasks such as scene understanding, decision making, trajectory prediction, and visual question answering.
By Siddharth Damodharan, Radhika Gupta, Ali Alshami, Ryan Rabinowitz, Jugal Kalita
arXiv:2609.07206v1 Announce Type: cross
Abstract: Trajectory representation learning underpins a wide range of trajectory analytics tasks; however, most existing self-supervised approaches, whether d...
By Sean Bin Yang, Ying Sun, Jilin Hu, Zongyi Xu, Kristian Torp, Hua Lu, Bin Yang, Christian S. Jensen
arXiv:2606. 12047v1 Announce Type: cross Abstract: In this paper, we address the problem of zero-shot understanding of accidents from surveillance videos by identifying when an impact event occurs, what type of impact it is, and where in the frame it occurs using natural language.
By Tarandeep Singh, Soumyanetra Pal, Soham Biswas, Nishanth Chandran
arXiv:2606. 15749v1 Announce Type: cross Abstract: Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics.
By Maonan Wang, Zhengyan Huang, Kemou Jiang, Yuhang Fu, Jiayue Zhu, Yuxin Cai, Xingchen Zou, Qiaosheng Zhang, Yi Yu, Ding Wang, Xi Chen, Ben M. Chen, Yuxuan Liang, Zhiyong Cui, Man On Pun, Yirong Chen
arXiv:2607. 09428v1 Announce Type: cross Abstract: Large-scale autonomous-driving datasets contain vast numbers of recorded scenarios, creating a need for efficient retrieval methods that can identify situations similar to a given query.
By Tam\'as Matuszka, Andr\'as Tam\'asy, Bal\'azs Szol\'ar
arXiv:2608. 16480v1 Announce Type: cross Abstract: We present RISE (Roadside Infrastructure Sequence Understanding and Evaluation), a framework spanning metric 3D tracking and structured vision-language reasoning in roadside sequences.
By Yanbo Jiang, Haotian Zheng, Jiahao Wang, Hanxiao Ren, Yitao Xu, Yining Xing, Zehong Ke, Hao Cheng, Yiqian Tu, Jinhao Li, Zhiyuan Xuan, Fang Zhang, Jianqiang Wang
Long-horizon future-frame prediction is important for autonomous driving, traffic surveillance, and intelligent transportation systems, yet remains challenging due to temporal ghosting, geometry drift, and inconsistent object motion. Recent latent video diffusion models have achieved impressive visual quality, but directly applying them to structured traffic scenes often leads to unstable geometry and degraded temporal coherence over extended horizons.