Privacy-Preserving in Connected and Autonomous Vehicles Through Vision to Text Transformation

Abstract

Intelligent Transportation Systems (ITS) rely on a variety of devices that frequently process privacy-sensitive data. Roadside units are important because they use AI-equipped cameras to detect traffic violations in Connected and Autonomous Vehicles (CAV). However, although the interior of a vehicle is generally considered a private space, the privacy risks associated with captured imagery remain a major concern, as such data can be misused for identity theft, profiling, or unauthorized commercial purposes. Methods like face blurring reduce privacy risks, however individuals' privacy can still be compromised. This paper introduces a novel privacy-preserving framework that leverages feedback-based reinforcement learning (RL) and vision-language models (VLMs) to protect sensitive visual information captured by AIE cameras. The proposed idea transforms images into textual descriptions using an innovative method while the main scene details are preserved and protects privacy. A hierarchical RL strategy is employed to iteratively refine the generated text, enhancing both semantic accuracy and privacy. Unlike prior captioning-based methods, our model incorporates an iterative reinforcement-learning cycle with external knowledge feedback which progressively refines privacy-aware text. In addition to qualitative textual metric evaluations, the privacy-based metrics demonstrate significant improvements in privacy preservation where SSIM, PSNR, MSE, and SRRA values obtained using the proposed method on two different datasets outperform other methods.