NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving

• URL: http://arxiv.org/abs/2509.25944v1
• Date: Tue, 30 Sep 2025 08:37:31 GMT
• Title: NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving
• Authors: Yuan Gao, Mattia Piccinini, Roberto Brusnicki, Yuchen Zhang, Johannes Betz,
• Abstract summary: Understanding risk in autonomous driving requires high-level reasoning about agent behavior and context.<n>Current Vision Language Models (Ms)-based methods primarily ground agents in static images.<n>We propose NuRisk as a critical benchmark for advancing explicit-temporal reasoning in autonomous driving.
• Score: 10.340969230365138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding risk in autonomous driving requires not only perception and prediction, but also high-level reasoning about agent behavior and context. Current Vision Language Models (VLMs)-based methods primarily ground agents in static images and provide qualitative judgments, lacking the spatio-temporal reasoning needed to capture how risks evolve over time. To address this gap, we propose NuRisk, a comprehensive Visual Question Answering (VQA) dataset comprising 2,900 scenarios and 1.1 million agent-level samples, built on real-world data from nuScenes and Waymo, supplemented with safety-critical scenarios from the CommonRoad simulator. The dataset provides Bird-Eye-View (BEV) based sequential images with quantitative, agent-level risk annotations, enabling spatio-temporal reasoning. We benchmark well-known VLMs across different prompting techniques and find that they fail to perform explicit spatio-temporal reasoning, resulting in a peak accuracy of 33% at high latency. To address these shortcomings, our fine-tuned 7B VLM agent improves accuracy to 41% and reduces latency by 75%, demonstrating explicit spatio-temporal reasoning capabilities that proprietary models lacked. While this represents a significant step forward, the modest accuracy underscores the profound challenge of the task, establishing NuRisk as a critical benchmark for advancing spatio-temporal reasoning in autonomous driving.

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