A Trajectory-free Crash Detection Framework with Generative Approach and Segment Map Diffusion

• URL: http://arxiv.org/abs/2511.13795v1
• Date: Mon, 17 Nov 2025 05:50:00 GMT
• Title: A Trajectory-free Crash Detection Framework with Generative Approach and Segment Map Diffusion
• Abstract summary: Road segment maps recording individual-level traffic dynamic data were directly served in crash detection.<n>A novel two-stage trajectory-free crash detection framework was present to generate the rational future road segment map.<n> Experiments on real-world crashes indicate the effectiveness of the proposed two-stage method in accurately detecting crashes.
• Score: 24.86297144482785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time crash detection is essential for developing proactive safety management strategy and enhancing overall traffic efficiency. To address the limitations associated with trajectory acquisition and vehicle tracking, road segment maps recording the individual-level traffic dynamic data were directly served in crash detection. A novel two-stage trajectory-free crash detection framework, was present to generate the rational future road segment map and identify crashes. The first-stage diffusion-based segment map generation model, Mapfusion, conducts a noisy-to-normal process that progressively adds noise to the road segment map until the map is corrupted to pure Gaussian noise. The denoising process is guided by sequential embedding components capturing the temporal dynamics of segment map sequences. Furthermore, the generation model is designed to incorporate background context through ControlNet to enhance generation control. Crash detection is achieved by comparing the monitored segment map with the generations from diffusion model in second stage. Trained on non-crash vehicle motion data, Mapfusion successfully generates realistic road segment evolution maps based on learned motion patterns and remains robust across different sampling intervals. Experiments on real-world crashes indicate the effectiveness of the proposed two-stage method in accurately detecting crashes.

Related papers

• SynCrash: A Multi-Stage Pipeline for Zero-Shot Accident Detection and Localization in Traffic Surveillance Video [0.0]
  We present SynCrash, a pipeline for zero-shot accident detection, spatial localization, and collision-type classification in CCTV video.<n>Our approach addresses the ACCIDENT at CVPR 2026 Challenge, which requires predicting when an accident occurs, where in the frame the impact happens, and what type of collision it is.
  arXiv  Detail & Related papers  (2026-08-30T12:42:59Z)
• DriveCache: Action-Aware Caching for Driving World Model Inference [61.07117287683939]
  Diffusion-based driving generators evaluate large backbones across denoising steps, which limits generation throughput.<n>Existing diffusion acceleration methods reduce this cost, but general-purpose designs omit driving signals available before generation, such as ego speed and planned trajectories.<n>We propose DriveCache, a training-free, action-aware controller that uses planned motion to allocate reuse across scenes and dynamic programming to place it across denoising steps under a calibrated response budget.
  arXiv  Detail & Related papers  (2026-08-17T10:04:49Z)
• TRACE: Topology-aware Reconstruction of Accidents in CARLA for AV Evaluation [0.0]
  We introduce TRACE, a pipeline that automates the reconstruction of NHTSA crash reports into high-fidelity CARLA simulations.<n>Using this pipeline, we curated a benchmark of 52 diverse accident scenarios covering varied collision types, road topologies, and pre-crash maneuvers.
  arXiv  Detail & Related papers  (2026-04-23T20:48:30Z)
• Road Obstacle Video Segmentation [71.92123495914892]
  We demonstrate that the road-obstacle segmentation task is inherently temporal, since the segmentation maps for consecutive frames are strongly correlated.<n>Our approach establishes a new state-of-the-art in road-obstacle video segmentation for long-range video sequences.
  arXiv  Detail & Related papers  (2025-09-16T15:34:43Z)
• TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving [70.85436371436184]
  TopoStreamer is an end-to-end temporal perception model for lane segment topology reasoning.<n>TopoStreamer introduces three key improvements: streaming attribute constraints, dynamic lane boundary positional encoding, and lane segment denoising.<n>On the Open-Lane-V2 dataset, TopoStreamer demonstrates significant improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-01T12:10:46Z)
• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• Advance Real-time Detection of Traffic Incidents in Highways using Vehicle Trajectory Data [3.061662434597097]
  This study uses vehicle trajectory data and traffic incident data on I-10, one of the most crash-prone highways in Louisiana. Various machine learning algorithms are used to detect a trajectory that is likely to face an incident in the downstream road section. Results suggest that the Random Forest model achieves the best performance for predicting an incident with reasonable recall value and discrimination capability.
  arXiv  Detail & Related papers  (2024-08-15T00:51:48Z)
• uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories [5.6328191854587395]
  We present a framework called uTRAND, that shifts the problem of anomalous trajectory prediction from the pixel space to a semantic-topological domain. We show that uTRAND outperforms other state-of-the-art approaches on a dataset of anomalous trajectories collected in a real-world setting.
  arXiv  Detail & Related papers  (2024-04-19T08:46:33Z)
• Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos [22.16190711818432]
  We introduce TTHF, a novel single-stage method aligning video clips with text prompts, offering a new perspective on traffic anomaly detection. Unlike previous approaches, the supervised signal of our method is derived from languages rather than one-hot vectors, providing a more comprehensive representation. It is shown that our proposed TTHF achieves promising performance, outperforming state-of-the-art competitors by +5.4% AUC on the DoTA dataset.
  arXiv  Detail & Related papers  (2024-01-07T15:47:19Z)
• Holistic Representation Learning for Multitask Trajectory Anomaly Detection [65.72942351514956]
  We propose a holistic representation of skeleton trajectories to learn expected motions across segments at different times. We encode temporally occluded trajectories, jointly learn latent representations of the occluded segments, and reconstruct trajectories based on expected motions across different temporal segments.
  arXiv  Detail & Related papers  (2023-11-03T11:32:53Z)
• Spatio-Temporal Context Modeling for Road Obstacle Detection [12.464149169670735]
  A data-driven context-temporal model of the driving scene is constructed with the layouts of the training data. Obstacles are detected via state-of-the-art object detection algorithms, and the results are combined with the generated scene.
  arXiv  Detail & Related papers  (2023-01-19T07:06:35Z)
• Real-Time Accident Detection in Traffic Surveillance Using Deep Learning [0.8808993671472349]
  This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The proposed framework consists of three hierarchical steps, including efficient and accurate object detection based on the state-of-the-art YOLOv4 method. The robustness of the proposed framework is evaluated using video sequences collected from YouTube with diverse illumination conditions.
  arXiv  Detail & Related papers  (2022-08-12T19:07:20Z)
• Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction [61.17654438176999]
  We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design. Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
  arXiv  Detail & Related papers  (2020-11-05T11:34:12Z)
• Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection [54.98042023365694]
  We propose a noise-aware encoder-decoder framework to disentangle a clean saliency predictor from noisy training examples. The proposed model consists of two sub-models parameterized by neural networks.
  arXiv  Detail & Related papers  (2020-07-23T18:47:36Z)

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.