VAGNet: Vision-based Accident Anticipation with Global Features

• URL: http://arxiv.org/abs/2604.09305v3
• Date: Fri, 17 Apr 2026 05:37:25 GMT
• Title: VAGNet: Vision-based Accident Anticipation with Global Features
• Abstract summary: Accident anticipation enables timely intervention through driver alerts and collision avoidance maneuvers.<n>In autonomous driving, such predictive capabilities support proactive safety behaviors, such as initiating defensive driving and human takeover.<n>We propose VAGNet, a deep neural network that learns to predict accidents from dash-cam video using global features of traffic scenes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic accidents are a leading cause of fatalities and injuries across the globe. Therefore, the ability to anticipate hazardous situations in advance is essential. Automated accident anticipation enables timely intervention through driver alerts and collision avoidance maneuvers, forming a key component of advanced driver assistance systems. In autonomous driving, such predictive capabilities support proactive safety behaviors, such as initiating defensive driving and human takeover when required. Using dashcam video as input offers a cost-effective solution, but it is challenging due to the complexity of real-world driving scenes. Accident anticipation systems need to operate in real-time. However, current methods involve extracting features from each detected object, which is computationally intensive. We propose VAGNet, a deep neural network that learns to predict accidents from dash-cam video using global features of traffic scenes without requiring explicit object-level features. The network consists of transformer and graph modules, and we use the vision foundation model VideoMAE-V2 for global feature extraction. Experiments on four benchmark datasets (DAD, DoTA, DADA, and Nexar) show that our method anticipates accidents with higher average precision and mean time-to-accident while being computationally more efficient compared to existing methods.

Related papers

• STAGNet: A Spatio-Temporal Graph and LSTM Framework for Accident Anticipation [0.0]
  Accident and timely warnings play a key role in improving road safety by reducing the risk of injury to road users and minimizing property damage.<n>Advanced Driver Assistance Systems (ADAS) are designed to support human drivers and are especially useful when they can anticipate potential accidents before they happen.<n>While many existing systems depend on a range of sensors such as LiDAR, radar, and GPS, relying solely on dash-cam video input presents a more challenging but a more cost-effective and easily deployable solution.
  arXiv  Detail & Related papers  (2025-08-21T04:02:22Z)
• CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions [13.981748780317329]
  Accurately and promptly predicting accidents among surrounding traffic agents from camera footage is crucial for the safety of autonomous vehicles (AVs) This study introduces a novel accident anticipation framework for AVs, termed CRASH. It seamlessly integrates five components: object detector, feature extractor, object-aware module, context-aware module, and multi-layer fusion. Our model surpasses existing top baselines in critical evaluation metrics like Average Precision (AP) and mean Time-To-Accident (mTTA)
  arXiv  Detail & Related papers  (2024-07-25T04:12:49Z)
• DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving [76.29141888408265]
  We propose a large-scale dataset containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset.
  arXiv  Detail & Related papers  (2023-04-03T17:37:00Z)
• 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)
• FBLNet: FeedBack Loop Network for Driver Attention Prediction [50.936478241688114]
  Nonobjective driving experience is difficult to model, so a mechanism simulating the driver experience accumulation procedure is absent in existing methods.<n>We propose a FeedBack Loop Network (FBLNet), which attempts to model the driving experience accumulation procedure.<n>Our model exhibits a solid advantage over existing methods, achieving an outstanding performance improvement on two driver attention benchmark datasets.
  arXiv  Detail & Related papers  (2022-12-05T08:25:09Z)
• TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance [2.1076255329439304]
  Existing datasets in traffic accidents are either small-scale, not from surveillance cameras, not open-sourced, or not built for freeway scenes. After integration and annotation by various dimensions, a large-scale traffic accidents dataset named TAD is proposed in this work.
  arXiv  Detail & Related papers  (2022-09-26T03:00:50Z)
• Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving [104.32241082170044]
  We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map. Our approach is the first one that can predict the existence of unseen vehicles in most cases.
  arXiv  Detail & Related papers  (2021-09-03T13:33:33Z)
• DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation [36.350348194248014]
  Traffic accident anticipation aims to accurately and promptly predict the occurrence of a future accident from dashcam videos. Existing approaches typically focus on capturing the cues of spatial and temporal context before a future accident occurs. We propose Deep ReInforced accident anticipation with Visual Explanation, named DRIVE.
  arXiv  Detail & Related papers  (2021-07-21T16:33:21Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning [30.59728753059457]
  Traffic accident anticipation aims to predict accidents from dashcam videos as early as possible. Current deterministic deep neural networks could be overconfident in false predictions. We propose an uncertainty-based accident anticipation model with relational-temporal learning.
  arXiv  Detail & Related papers  (2020-08-01T20:21:48Z)
• Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring [52.557003792696484]
  We present a framework for the detection of the drivers' intention based on both in-cabin and traffic scene videos. Our framework achieves a prediction with the accuracy of 83.98% and F1-score of 84.3%.
  arXiv  Detail & Related papers  (2020-06-20T11:56:32Z)

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.