PerceptiSync: Trustworthy Object Detection using Crowds-in-the-Loop for Cyber-Physical Systems

Abstract

Establishing reliable object detection in distributed environments is challenging, particularly when trust depends on results from multiple computer vision systems. In this manuscript, we introduce PerceptiSync, a novel and trustworthy Embodied-AI (EAI) framework. It is designed for shared perception across distributed Cyber-Physical Systems (CPS) that utilize object detection. This includes applications in Connected Autonomous Vehicles, drone swarms, and CCTV camera networks. PerceptiSync is designed around a Crowds-in-the-Loop (CITL) concept to enhance system reliability by incorporating four individual user configurations and the Dirichlet-Categorical trust model. PerceptiSync undergoes a two-stage evaluation. First, it is assessed using a benchmark Computer Vision (CV) dataset to track performance over time. Second, it is tested with integrated user configurations to evaluate trust accuracy and mitigation capabilities against false positives. The results show that PerceptiSync outperforms existing AI-only trust frameworks, achieving a higher mean Kendall's Tau coefficient of 0.228 compared to 0.051, demonstrating successful performance over time.