VAGNet: Vision-based Accident Anticipation with Global Features

Vipooshan Vipulananthan; Charith D. Chitraranjan

cs.CVApr 10, 2026

VAGNet: Vision-based Accident Anticipation with Global Features

Authors: Vipooshan Vipulananthan, Charith D. Chitraranjan

Organizations: Department of Computer Science and Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka

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 dash-cam 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 while being computationally more efficient compared to existing methods. It also achieves higher mean time-to-accident scores in most cases, while being competitive in others.

Figures & tables

Figure 1: Architecture of the proposed VAGNet accident anticipation framework.

Dataset	Positive	Negative	FPS
DAD [ 4 ]	620	1130	20
DoTA [ 49 ]	745	745	10
DADA [ 10 ]	938	1361	10
Nexar [ 27 ]	1422	1422	10

Table 1: Dataset Statistics.

Figure 2: Distribution of crash-object categories in the DoTA and DADA datasets. Crash-object category annotations are not available for the Nexar dataset.

Panel of Figure 3 — Figure 3: Samples showing the variation of predicted accident probability across time/frames for (a) True positive (b) True negative (c) False positive and (d) False negative cases.

Panel of Figure 4 — Figure 4: Samples of visual explanations for accidents across different weather/lighting conditions. For each condition, the top row shows the original video frames in temporal order, and the bottom row shows the corresponding Grad-CAM [ 33 ] maps highlighting areas in the image that contribute to accident anticipation. Higher intensity indicates regions that have a stronger influence on the model’s anticipation of a potential accident.

Dataset	Method	AP (%)	mTTA (s)
DAD [ 4 ]	DSA (2017) [ 4 ]	48.1	1.34
	adaLEA (2018) [ 39 ]	52.3	3.43
	UString (2020) [ 1 ]	53.7	3.53
	DSTA (2022) [ 14 ]	56.1	3.66
	EGSTL (2023) [ 23 ]	56.3	3.23
	GSC (2024) [ 48 ]	60.4	3.53

Table 2: Performance comparison of different methods.

Dataset	Global Features	AP (%)	mTTA (s)
DAD	VGG16	54.83	4.15
	I3D	59.27	4.13
	SlowFast	62.90	3.74
	VideoMAE-V2	77.98	4.41
DoTA	VGG16	88.74	3.25
	I3D	91.71	3.20

Table 3: Performance of VAGNet under different global feature extraction backbones.

Data	TE	GT	AP (%)	mTTA (s)
DAD [ 4 ]	✗	✗	64.15	2.54
	✓	✗	76.58	4.35
	✗	✓	71.37	4.12
	✓	✓	77.98	4.41
DoTA [ 49 ]	✗	✗	87.50	2.45
	✓	✗	95.87	3.31

Table 4: Results of the ablation study. Each model variant removes one or more components (Transformer Encoder or Graph) to analyze their individual impact.

Method	GFLOPs
Method	Object Detection & Feature Extraction	Global Feature Extraction	Model Inference	Total
UString	322.46 (worst case: 19 objects/frame)	15.47 (VGG16)	10.49	348.42
Graph (Graph)		41.75 (I3D)	2.14	366.35
Zhong [ 56 ]		41.75 (I3D)	2.05	366.26
AAT-DA		15.47 (VGG16)	1.05	461.96*
STAGNet		50.58 (SlowFast)	2.20	375.24

Table 5: FLOP counts of different methods. Counts for different stages are reported in GFLOPs/frame. The counts were obtained using the Python library THOP [ 43 ] .

Method	Object Detection & Feature Extraction (s/f)	Global Feature Extraction (s/f)	Inference (s/f)	Overall speed (FPS)
UString	0.032	0.004 (VGG16)	0.024	17
Graph(Graph)		0.01 (I3D)	0.001	23
AAT-DA		0.004 (VGG16)	0.094*	8
STAGNet		0.014 (SlowFast)	0.001	21
VAGNet	0	0.025 (VideoMAE)	0.001	38

Table 6: The computational efficiency of the proposed and baseline methods is evaluated in terms of execution time. Module-level performance is reported in seconds per frame (s/f), whereas overall inference efficiency is reported as frames per second (FPS). For AAT-DA, the reported inference time additionally accounts for the generation of driver attention maps. Note that although we do not report the inference time for the method proposed by Zhong et. al [ 56 ] due to the unavailability of its implementation, we expect it to operate close to 23 FPS on our platform as it uses VGG16 and I3D as feature extractors.

Traffic Accident Anticipation

Traffic accident anticipation predicts imminent collisions or rising crash risk early enough to support driver warnings and proactive road-safety decisions.

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FLaRA: Predicting Future Latent Representations for Accident Anticipation

Lorenzo Caselli, Tomaso Trinci, Tommaso Bianconcini, Simone Magistri+3

Department of Information Engineering, University of Florence, Italy. · 2Verizon Connect, Florence, Italy.

Autonomous Driving Traffic Accident Anticipation Predictive Representation Learning

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Anticipating traffic accidents from dashcam videos is a critical challenge in intelligent transportation systems. Existing methods typically map visual context directly to a collision probability without explicitly modeling the future evolution of the driving scene. In this paper we propose FLaRA (Predicting Future Latent Representations for Accident Anticipation), a novel predictive architecture that shifts this paradigm by forecasting future latent representations for accident anticipation. Building upon the Video Joint-Embedding Predictive Architecture (V-JEPA2), our model conditions a predictor network on observed context frames to predict the forthcoming latent features of the scene. A classifier then operates on these predicted future representations rather than only on past observations. To ensure these forecasts remain grounded in realistic future dynamics, we introduce a joint training objective that simultaneously optimizes an auxiliary feature-level reconstruction loss and a cross-entropy classification loss. Extensive evaluations on the Nexar dataset, alongside cross-domain validations on the DAD, DADA-2000, and DoTA benchmarks, demonstrate that our approach achieves state-of-the-art performance while maintaining realistic early warning capabilities.

Abstract

Traffic Accident Anticipation

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