FLaRA: Predicting Future Latent Representations for Accident Anticipation

2026-06-12

AI Summary

This work addresses the limitation of existing dashcam-based accident prediction methods, which lack explicit modeling of future driving scene dynamics. The authors propose FLaRA, a novel architecture that reframes accident prediction as a task of forecasting future latent representations. Built upon V-JEPA2, FLaRA employs a conditional prediction network trained jointly with a feature-level reconstruction loss and a cross-entropy classification loss, ensuring that the predicted representations faithfully capture realistic scene evolution while supporting accurate accident classification. Evaluated on the Nexar dataset and multiple cross-domain benchmarks, FLaRA significantly outperforms current state-of-the-art approaches, achieving both high prediction accuracy and effective early warning capability.

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.

Problem

• accident anticipation
• future prediction
• latent representation
• dashcam videos
• intelligent transportation systems

Innovation

• future latent representation prediction
• accident anticipation
• predictive modeling
• joint training objective
• video joint-embedding