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Seeing before Observable: Potential Risk Reasoning in Autonomous Driving via Vision Language Models

Jiaxin Liu, Xiangyu Yan, Liang Peng, Lei Yang, Lingjun Zhang, Yuechen Luo, Yueming Tao, Ashton Yu Xuan Tan, Mu Li, Lei Zhang, Ziqi Zhan, Sai Guo, Hong Wang, Jun Li

TL;DR

<3-5 sentence high-level summary> The paper tackles the challenge of anticipatory safety in autonomous driving by defining potential risk as hazards not yet observable but inferable from precursors. It introduces PotentialRiskQA, a language-based dataset with a structured reasoning chain linking semantic precursors to inferred risks, and PR-Reasoner, a vision-language reasoning framework that uses auxiliary tasks to supervise a multimodal model. Experiments demonstrate that fine-tuning with PotentialRiskQA improves potential risk reasoning across diverse VLM backbones, and reveal that the reasoning paradigm reduces dependence on model size, enabling smaller, onboard-friendly models to perform competitively. Together, the dataset and framework enable proactive safety capabilities by enabling foresight, interpretability, and robust reasoning in complex driving scenarios.

Abstract

Ensuring safety remains a key challenge for autonomous vehicles (AVs), especially in rare and complex scenarios. One critical but understudied aspect is the \textbf{potential risk} situations, where the risk is \textbf{not yet observable} but can be inferred from subtle precursors, such as anomalous behaviors or commonsense violations. Recognizing these precursors requires strong semantic understanding and reasoning capabilities, which are often absent in current AV systems due to the scarcity of such cases in existing driving or risk-centric datasets. Moreover, current autonomous driving accident datasets often lack annotations of the causal reasoning chains behind incidents, which are essential for identifying potential risks before they become observable. To address these gaps, we introduce PotentialRiskQA, a novel vision-language dataset designed for reasoning about potential risks prior to observation. Each sample is annotated with structured scene descriptions, semantic precursors, and inferred risk outcomes. Based on this dataset, we further propose PR-Reasoner, a vision-language-model-based framework tailored for onboard potential risk reasoning. Experimental results show that fine-tuning on PotentialRiskQA enables PR-Reasoner to significantly enhance its performance on the potential risk reasoning task compared to baseline VLMs. Together, our dataset and model provide a foundation for developing autonomous systems with improved foresight and proactive safety capabilities, moving toward more intelligent and resilient AVs.

Seeing before Observable: Potential Risk Reasoning in Autonomous Driving via Vision Language Models

TL;DR

<3-5 sentence high-level summary> The paper tackles the challenge of anticipatory safety in autonomous driving by defining potential risk as hazards not yet observable but inferable from precursors. It introduces PotentialRiskQA, a language-based dataset with a structured reasoning chain linking semantic precursors to inferred risks, and PR-Reasoner, a vision-language reasoning framework that uses auxiliary tasks to supervise a multimodal model. Experiments demonstrate that fine-tuning with PotentialRiskQA improves potential risk reasoning across diverse VLM backbones, and reveal that the reasoning paradigm reduces dependence on model size, enabling smaller, onboard-friendly models to perform competitively. Together, the dataset and framework enable proactive safety capabilities by enabling foresight, interpretability, and robust reasoning in complex driving scenarios.

Abstract

Ensuring safety remains a key challenge for autonomous vehicles (AVs), especially in rare and complex scenarios. One critical but understudied aspect is the \textbf{potential risk} situations, where the risk is \textbf{not yet observable} but can be inferred from subtle precursors, such as anomalous behaviors or commonsense violations. Recognizing these precursors requires strong semantic understanding and reasoning capabilities, which are often absent in current AV systems due to the scarcity of such cases in existing driving or risk-centric datasets. Moreover, current autonomous driving accident datasets often lack annotations of the causal reasoning chains behind incidents, which are essential for identifying potential risks before they become observable. To address these gaps, we introduce PotentialRiskQA, a novel vision-language dataset designed for reasoning about potential risks prior to observation. Each sample is annotated with structured scene descriptions, semantic precursors, and inferred risk outcomes. Based on this dataset, we further propose PR-Reasoner, a vision-language-model-based framework tailored for onboard potential risk reasoning. Experimental results show that fine-tuning on PotentialRiskQA enables PR-Reasoner to significantly enhance its performance on the potential risk reasoning task compared to baseline VLMs. Together, our dataset and model provide a foundation for developing autonomous systems with improved foresight and proactive safety capabilities, moving toward more intelligent and resilient AVs.

Paper Structure

This paper contains 26 sections, 3 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Potential Risk Cognition
  4. Risk-Centric Datasets for Autonomous Driving
  5. Methodology
  6. Overview of PR-Reasoner
  7. Vision Encoder
  8. Textual Tokenizer
  9. VLM Backbones
  10. Reasoning Chain with Auxiliary Sub-tasks
  11. PotentialRiskQA Dataset
  12. Dataset Overview
  13. Dataset Statistics
  14. Dataset Construction
  15. Data Collection
  16. ...and 11 more sections

Figures (3)

  • Figure 1: The structure and scenario examples of the PotentialRiskQA dataset. The key feature of potential risks is that they are not directly observable, but can be inferred from observable precursors.
  • Figure 2: The proposed PR-Reasoner framework. Based on VLMs, the proposed model takes a frame sequence and textual prompts as inputs. The output is a structured reasoning chain.
  • Figure 3: Dataset statistics of PotentialRiskQA dataset: (a) The count of different risk categories. The detailed definition of the categories can be found in the Supplementary. (b) The comparison of Risk to Collision time and Precursor to Collision time. (c) The distribution of the additional risk mitigation time earned by indication precursors. (d-f) The count of different (d) weather; (e) types of traffic participants; (f) road structure in the dataset.