arXiv:2606.09142v1 Announce Type: cross Abstract: Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexplored. In this work, we study the decoding of pedestrian crossing intentions from short egocentric video clips. We approach this by formulating the task as a closed-ended visual question answering (VQA) problem and leveraging vision language models (VLMs) to predict the pedestrians' intent. We first benchmark three families of state-of-the-art VLMs in a zero-shot setting, finding that they achieve
The rapid advancement and accessibility of Vision Language Models make their application to diverse real-world problems, such as traffic safety, a natural progression.
Improving the ability of autonomous systems to predict human intention, especially in complex environments like traffic, is crucial for safety and the broader adoption of AI in critical infrastructure.
This research introduces a novel application of VLMs for predicting pedestrian crossing intentions from egocentric video, potentially enhancing intelligent traffic systems and autonomous vehicle safety.
- · Autonomous Vehicle Developers
- · Smart City Infrastructure
- · AI Safety Researchers
- · Traditional traffic prediction models
More accurate pedestrian intention prediction can lead to enhanced safety features in autonomous systems.
Improved safety could accelerate public trust and regulatory approval for autonomous vehicles and smart transportation solutions.
Widespread deployment of such predictive AI in urban environments could fundamentally alter traffic flow, accident rates, and urban planning, reducing human-caused accidents.
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Read at arXiv cs.AI