arXiv:2601.17216v3 Announce Type: replace-cross 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 (
The proliferation of vehicular connectivity and advanced AI models enables novel approaches to real-time traffic safety challenges, overcoming previous bandwidth limitations.
This development significantly enhances proactive road safety systems by moving collision prediction from reactive to predictive, potentially reducing accidents and improving traffic flow efficiency.
Traditional raw data transmission for V2X collision prediction is being superseded by semantic embedding architectures, allowing more efficient and timely communication for safety-critical applications.
- · Automotive OEMs
- · Intelligent Transportation Systems providers
- · Smart City infrastructure developers
- · AI model developers for edge computing
- · Legacy V2X hardware manufacturers focused on high-bandwidth raw data
- · Insurance companies (potential long-term reduction in claims)
Reduction in traffic accidents and associated fatalities/injuries.
Accelerated adoption of autonomous driving features due to improved safety predictability.
Re-evaluation of urban planning and infrastructure design to integrate advanced V2X and RSU capabilities more deeply.
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Read at arXiv cs.LG