Hazard-Aware Traffic Scene Graph Generation

• URL: http://arxiv.org/abs/2603.03584v1
• Date: Tue, 03 Mar 2026 23:29:11 GMT
• Title: Hazard-Aware Traffic Scene Graph Generation
• Authors: Yaoqi Huang, Julie Stephany Berrio, Mao Shan, Stewart Worrall,
• Abstract summary: We introduce a novel task, Traffic Scene Graph Generation, which captures traffic-specific relations between prominent hazards and the ego vehicle.<n>We propose a novel framework that explicitly uses traffic accident data and depth cues to supplement visual features and semantic information for reasoning.<n>The output traffic scene graphs provide intuitive guidelines that stress prominent hazards by color-coding their severity and notating their effect mechanism and relative location to the ego vehicle.
• Score: 9.549979680242195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Maintaining situational awareness in complex driving scenarios is challenging. It requires continuously prioritizing attention among extensive scene entities and understanding how prominent hazards might affect the ego vehicle. While existing studies excel at detecting specific semantic categories and visually salient regions, they lack the ability to assess safety-relevance. Meanwhile, the generic spatial predicates either for foreground objects only or for all scene entities modeled by existing scene graphs are inadequate for driving scenarios. To bridge this gap, we introduce a novel task, Traffic Scene Graph Generation, which captures traffic-specific relations between prominent hazards and the ego vehicle. We propose a novel framework that explicitly uses traffic accident data and depth cues to supplement visual features and semantic information for reasoning. The output traffic scene graphs provide intuitive guidelines that stress prominent hazards by color-coding their severity and notating their effect mechanism and relative location to the ego vehicle. We create relational annotations on Cityscapes dataset and evaluate our model on 10 tasks from 5 perspectives. The results in comparative experiments and ablation studies demonstrate our capacity in ego-centric reasoning for hazard-aware traffic scene understanding.

Related papers

• AVOID: The Adverse Visual Conditions Dataset with Obstacles for Driving Scene Understanding [48.97660297411286]
  We introduce AVOID, a new dataset for real-time obstacle detection in a simulated environment.<n>AVOID consists of a large set of unexpected road obstacles located along each path captured under various weather and time conditions.<n>Each image is coupled with the corresponding semantic and depth maps, raw and semantic LiDAR data, and waypoints.
  arXiv  Detail & Related papers  (2025-12-29T05:34:26Z)
• Semantic4Safety: Causal Insights from Zero-shot Street View Imagery Segmentation for Urban Road Safety [14.300819977717708]
  We propose a framework that applies zero-shot semantic segmentation to street-view imagery to derive 11 interpretable streetscape indicators.<n>We analyze approximately 30,000 accident records in Austin to uncover accident-type-specific causal patterns.<n>By bridging predictive modeling with causal inference, Semantic4Safety supports targeted interventions and high-risk corridor diagnosis.
  arXiv  Detail & Related papers  (2025-10-17T08:45:28Z)
• Enhancing Vision-Language Models with Scene Graphs for Traffic Accident Understanding [45.7444555195196]
  This work focuses on classifying traffic scenes into specific accident types.<n>We approach the problem by representing a traffic scene as a graph, where objects such as cars can be represented as nodes, and relative distances and directions between them as edges.
  arXiv  Detail & Related papers  (2024-07-08T13:15:11Z)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• Interpretable End-to-End Driving Model for Implicit Scene Understanding [3.4248756007722987]
  We propose an end-to-end Interpretable Implicit Driving Scene Understanding (II-DSU) model to extract implicit high-dimensional scene features. Our approach achieves the new state-of-the-art and is able to obtain scene features that embody richer scene information relevant to driving.
  arXiv  Detail & Related papers  (2023-08-02T14:43:08Z)
• OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
  We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
  arXiv  Detail & Related papers  (2023-04-20T16:31:22Z)
• Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark [77.54411007883962]
  We propose a Cognitive Accident Prediction (CAP) method that explicitly leverages human-inspired cognition of text description on the visual observation and the driver attention to facilitate model training. CAP is formulated by an attentive text-to-vision shift fusion module, an attentive scene context transfer module, and the driver attention guided accident prediction module. We construct a new large-scale benchmark consisting of 11,727 in-the-wild accident videos with over 2.19 million frames.
  arXiv  Detail & Related papers  (2022-12-19T11:43:02Z)
• RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments [72.04891523115535]
  We propose RSG-Net (Road Scene Graph Net): a graph convolutional network designed to predict potential semantic relationships from object proposals. The experimental results indicate that this network, trained on Road Scene Graph dataset, could efficiently predict potential semantic relationships among objects around the ego-vehicle.
  arXiv  Detail & Related papers  (2022-07-16T12:40:17Z)
• Vision based Pedestrian Potential Risk Analysis based on Automated Behavior Feature Extraction for Smart and Safe City [5.759189800028578]
  We propose a comprehensive analytical model for pedestrian potential risk using video footage gathered by road security cameras deployed at such crossings. The proposed system automatically detects vehicles and pedestrians, calculates trajectories by frames, and extracts behavioral features affecting the likelihood of potentially dangerous scenes between these objects. We validated feasibility and applicability by applying it in multiple crosswalks in Osan city, Korea.
  arXiv  Detail & Related papers  (2021-05-06T11:03:10Z)
• RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks [19.582873794287632]
  A key aspect of driving a road vehicle is to interact with other road users, assess their intentions and make risk-aware tactical decisions. We propose a novel driving framework for egocentric views based on traffic graphs. We claim that our framework learns risk-aware representations by improving on the task of risk object identification.
  arXiv  Detail & Related papers  (2020-11-17T15:49:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.