Spatiotemporal Semantic V2X Framework for Cooperative Collision Prediction

TL;DR

This paper introduces a semantic V2X framework that uses spatiotemporal embeddings to predict collisions in real-time, significantly reducing communication bandwidth while maintaining high accuracy in urban traffic scenarios.

Contribution

It proposes a novel semantic V2X system utilizing V-JEPA for future frame embedding, enabling efficient and accurate collision prediction with minimal data transmission.

Findings

Achieves 10% F1-score improvement in collision prediction.

Reduces data transmission by four orders of magnitude.

Validates effectiveness in diverse urban traffic scenarios.

Abstract

Intelligent Transportation Systems (ITS) demand real-time collision prediction to ensure road safety and reduce accident severity. Conventional approaches rely on transmitting raw video or high-dimensional sensory data from roadside units (RSUs) to vehicles, which is impractical under vehicular communication bandwidth and latency constraints. In this work, we propose a semantic V2X framework in which RSU-mounted cameras generate spatiotemporal semantic embeddings of future frames using the Video Joint Embedding Predictive Architecture (V-JEPA). To evaluate the system, we construct a digital twin of an urban traffic environment enabling the generation of d verse traffic scenarios with both safe and collision events. These embeddings of the future frame, extracted from V-JEPA, capture task-relevant traffic dynamics and are transmitted via V2X links to vehicles, where a lightweight attentive probe and classifier decode them to predict imminent collisions. By transmitting only semantic embeddings instead of raw frames, the proposed system significantly reduces communication overhead while maintaining predictive accuracy. Experimental results demonstrate that the framework with an appropriate processing method achieves a 10% F1-score improvement for collision prediction while reducing transmission requirements by four orders of magnitude compared to raw video. This validates the potential of semantic V2X communication to enable cooperative, real-time collision prediction in ITS.