arXiv:2607.08402v1 Announce Type: cross Abstract: Large-scale and diverse datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportation system (ITS) application. Pedestrian intention and trajectory prediction are critical models used in AVs, requiring datasets involving diverse pedestrian images. Unrestricted access to these datasets imposes serious security risks, like identity theft and pedestrian tracking. The challenge is to apply privacy preservation procedures while maintaining the image attributes needed to train the mode
The increasing reliance on large-scale datasets for AI systems in sensitive applications like autonomous vehicles, coupled with growing privacy regulations, makes privacy-preserving techniques a critical and immediate concern.
This research addresses the fundamental tension between data utility for AI training and individual privacy, which impacts the deployment speed and societal acceptance of advanced AI systems, particularly in mobility.
The proposed dual-purpose pipeline allows for robust AI model training on diverse pedestrian data while simultaneously mitigating privacy risks from identity theft and tracking, potentially improving public trust and regulatory compliance.
- · AI developers
- · Autonomous vehicle companies
- · Privacy-preserving technology providers
- · Cities adopting smart transportation
- · Unsecured data holders
- · Entities engaging in unauthorized data exploitation
Wider adoption and faster deployment of AI-powered intelligent transportation systems due to enhanced privacy.
Increased investment in privacy-enhancing technologies becomes a competitive differentiator for AI development in sensitive sectors.
This could set a precedent for privacy-preserving AI development in other high-stakes domains, influencing global data governance standards for AI.
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Read at arXiv cs.AI