The paper presents a single pretrained diffusion traffic model that serves both as an ego motion planner and as a controllable generator of safety‑critical scenarios for autonomous driving. It introduces a Single‑Stream Dual‑Stream diffusion‑transformer decoder (SSDS) that fuses scene context via joint attention, improving closed‑loop performance on the nuPlan benchmark, and a training‑free guidance scheme called Decoupled Annealing Posterior Sampling with Energy (DAPSE) that injects arbitrary energy functions at inference time. Using the same model, the authors generate realistic long‑tail driving interactions—such as aggressive cut‑ins and lead‑vehicle braking—through inference‑time guidance, exposing failure modes in black‑box planners that standard benchmarks miss.
By Arka Pal, Rajesh Kumar, Hannes Eriksson, R\'emi Lacombe, Arvid Laveno Ling, Ankit Gupta, Maciej Wozniak
arXiv:2606. 06423v1 Announce Type: cross Abstract: Safety-critical traffic scenario generation is essential for evaluating autonomous driving systems under rare but high-risk interactions.
By Qi Lan, Yining Tang, Yu Shen, Yi Zhou, Yuhao Wei, Jie Li, Guofa Li
arXiv:2501. 17015v2 Announce Type: replace Abstract: Simulation plays a crucial role in assessing autonomous driving systems, where the generation of realistic multi-agent behaviors is a key aspect.
By Longzhong Lin, Xuewu Lin, Kechun Xu, Haojian Lu, Lichao Huang, Rong Xiong, Yue Wang
arXiv:2606. 11569v1 Announce Type: cross Abstract: Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems.
By Qichao Zhang, Xing Fang, Jiaqi Fang, Zhenwen Cai, Jie Ling, Qiankun Yu, Dongbin Zhao
arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.
By Yining Xing, Zehong Ke, Zhiyuan Liu, Yanbo Jiang, Wenhao Yu, Jianqiang Wang
arXiv:2608.22187v1 Announce Type: cross
Abstract: Modern driving action models are increasingly improved in a self-improvement loop, where a learned world simulator imagines future observations and t...
By Jiaqi Wang, Zhuo Zhang, Haining Guan, Tingguang Zhou, Haowen Cui, Zhongyang Zhu, Yulong Zheng, ChuanYe Wang, Xuefeng Chen, Zhen Yang, Tianchen Deng, Feiyang Tan, Hangning Zhou, Bo Dai, Lixia Shen, Xiwu Chen, Xiyang Wang, Jiajun Zhu