arXiv:2607. 07103v1 Announce Type: new Abstract: Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic safety.
By Heye Huang, Jingguang Li, Zhiyuan Zhou, Paul Liang, Mingyu Wu, Kitae Jang, Jianqiang Wang
arXiv:2607. 11128v1 Announce Type: cross Abstract: Real-time driving risk assessment provides an essential basis for proactive safety by identifying and quantifying the danger of ongoing road interactions before adverse outcomes occur.
By Zhuoren Li, Yi Zhong, Weiqi Zhang, Xinrui Zhang, Lu Xiong, Chongfeng Wei, Bo Leng
HERMES is a holistic end‑to‑end multimodal driving framework that incorporates long‑tail semantic knowledge into trajectory planning for autonomous vehicles. It uses a foundation‑model‑assisted annotation pipeline to build Long‑Tail Scene Context and Long‑Tail Planning Context, capturing hazard‑centric scene information, maneuver intent, and risk‑aware guidance. A Tri‑Modal Driving Module then fuses multi‑view visual observations, historical ego‑motion, and long‑tail semantic instructions to generate intent‑ and risk‑aware trajectories, achieving consistent performance gains on a large‑scale real‑world long‑tail driving benchmark.
By Weizhe Tang, Junwei You, Jiaxi Liu, Zhaoyi Wang, Rui Gan, Zilin Huang, Feng Wei, Bin Ran
arXiv:2603. 14841v3 Announce Type: replace-cross Abstract: Road crashes remain a leading cause of preventable fatalities.
By Joyjit Roy, Samaresh Kumar Singh, Sushanta Das
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:2606. 00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving.
By Xinyi Ning, Zilin Bian, Dachuan Zuo, Semiha Ergan, Kaan Ozbay