LightEMMA is a longitudinal evaluation framework that tests the autonomous driving performance of vision‑language models (VLMs) without fine‑tuning or prompt engineering. Using this protocol, the authors evaluated 15 VLMs from five major families on the nuScenes prediction benchmark and found that larger, more capable models do not consistently outperform earlier generations. The study identifies common failure modes such as overreliance on historical actions and difficulty reconciling conflicting visual cues, underscoring the need for domain‑specific adaptation to enhance VLM safety in autonomous driving.
By Zhijie Qiao, Haowei Li, Zhong Cao, Henry X. Liu
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
PRISM (Proactive Risk Intelligence and Safety Management) is an agentic multi-model architecture designed to shift autonomous transportation safety from reactive crash avoidance to proactive, continuous risk management. It uses inverse crash‑probability modeling to transform binary crash classifiers into dynamic safety scores, and runs three specialized models—trajectory kinematics, environmental risk, and VRU interaction—coordinated by a reinforcement‑learning reasoning layer. Across 1,296 naturalistic driving scenarios, PRISM achieved a mean safety score of 68/100, classified 77.6% of situations as advisory, and flagged 3.8% as near‑misses, with 11% requiring intervention or emergency response, highlighting trajectory risk and VRU proximity as key safety factors.
By Joyjit Roy, Samaresh Kumar Singh, Sushanta Das
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:2602. 18094v2 Announce Type: replace-cross Abstract: Existing Visual-Language Models (VLMs) have achieved significant progress by being trained on massive-scale datasets, typically under the assumption that data are independent and identically distributed (IID).
By Ling Lin, Yang Bai, Heng Su, Congcong Zhu, Yaoxing Wang, Yang Zhou, Huazhu Fu, Jingrun Chen
The paper introduces a new criticality metric specifically designed for vulnerable road users (VRUs) and a scenario‑independent prediction framework that applies to all traffic participants. The VRU‑centric metric improves pedestrian criticality classification by up to 50 %, while the prediction framework surpasses state‑of‑the‑art metrics by 275 %, achieving an F1‑score of 0.96 on the DeepAccident dataset. These advances enable more accurate, scenario‑agnostic safety assessments for autonomous driving systems.
By J\"org Gamerdinger, Victor Schwarzenberger, Philipp Schmid, Sven Teufel, Oliver Bringmann
The paper evaluates five vision‑language models on autonomous driving tasks under various visual input conditions, finding that visual corruption affects accuracy and confidence differently across models and datasets. It then tests Visual Evidence Augmentation (VEA) as an inference‑time technique to enhance reliability, observing mixed improvements depending on the model and setting.
By Manasa Mariam Mammen, Priyanka Mary Mammen, Zafer Kayatas, Stefan Wagner
arXiv:2603. 29759v2 Announce Type: replace-cross Abstract: Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment.
By Qiucheng Yu, Ruijie Xu, Mingang Chen Jianfeng Dong, Xin Tan
arXiv:2606. 17362v1 Announce Type: cross Abstract: Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-dependent.
By Xinglong Sun, Kevin Xie, Jenny Schmalfuss, Despoina Paschalidou, Xiuming Zhang, Sanja Fidler, Kashyap Chitta, Jose M. Alvarez
arXiv:2511.20022v3 Announce Type: replace-cross
Abstract: Recent advancements in multimodal large language models (MLLMs) have shown strong understanding of driving scenes, drawing interest in their...
By Seungjun Yu, Seonho Lee, Namho Kim, Jaeyo Shin, Junsung Park, Wonjeong Ryu, Raehyuk Jung, Hyunjung Shim
The paper introduces an automated pipeline that converts non‑critical driving scenes into safety‑critical scenarios by integrating computer vision, Large Language Models (LLMs), and Augmented Reality (AR). It detects and tracks road users, extracts safety features such as distance, velocity, motion direction, and Time‑to‑Collision (TTC), and evaluates scene criticality. Safe scenes are then modified by an LLM, which generates realistic collision‑inducing objects and behaviors that are overlaid onto the original scene using AR, achieving 97.52% safety classification accuracy on the nuScenes dataset and producing realistic scenarios like pedestrian crossings, rear overtaking vehicles, and sudden‑stop events.
By Noura Fady, Farah Khaled, Catherine M. Elias
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