arXiv AI By Rongxiang Zeng, Linsen Cai, Jiafu Zhang, Yijie Zhong, Yide Tao, Shuai Wang, Nan Zheng, Hai L. Vu, Alvaro Garcia Hernandez, Yongqi Dong

Risk-Aware World Modeling with Flow-Guided Occupancy Evolution for Selective Trajectory Planning in Automated Driving

Read the original on arXiv AI →

RiskWorld is a risk‑aware world modeling framework that forecasts shared occupancy and selectively replaces planned trajectories in automated driving. It fuses spatial risk fields, temporal actor context, and visual bird’s‑eye‑view features, using flow‑guided evolution to transport occupancy and signed residuals to correct it. In open‑loop planning on nuScenes, RiskWorld achieves the lowest collision rate over a 3‑second horizon and the second‑best average L2 error, running at 11.5 FPS on a single NVIDIA RTX 4090.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Aug 20

DA-WAM: Decision-Aligned Future Latents for Driving World Models

DA‑WAM is a framework that integrates predictive representation learning, action‑conditioned future modeling, and trajectory scoring into a single decision‑making objective for autonomous driving. It uses an online encoder with a stable momentum target to keep future representations aligned with the driving task, generating a distinct future latent for each trajectory candidate. A future‑latent‑conditioned scorer evaluates these latents, with expert‑matched trajectories supervised by observed futures and safety‑critical hard negatives providing additional guidance, achieving state‑of‑the‑art results on NAVSIM‑v1 and NAVSIM‑v2.

By Ruiguo Zhong, Benshan Ma, Xiaolong Chen, Lang Zhang, Mingyue Feng, Yaonong Wang, Pei Liu, Jun Ma