FLaRA: Predicting Future Latent Representations for Accident Anticipation

• URL: http://arxiv.org/abs/2606.14380v1
• Date: Fri, 12 Jun 2026 12:16:45 GMT
• Title: FLaRA: Predicting Future Latent Representations for Accident Anticipation
• Abstract summary: Anticipating traffic accidents from dashcam videos is a critical challenge in intelligent transportation systems.<n>We propose FLaRA, a novel predictive architecture that shifts this paradigm by forecasting future latent representations for accident anticipation.<n>Our approach achieves state-of-the-art performance while maintaining realistic early warning capabilities.
• Score: 15.994549120609358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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