Privacy-Preserving Collaborative Estimation for Networked Vehicles With Application to Collaborative Road Profile Estimation

Abstract

Road information such as road profile has been widely used in intelligent vehicle systems to improve road safety, ride comfort, and fuel economy. However, practical challenges, such as vehicle heterogeneity, parameter uncertainty, and measurement reliability, make it extremely difficult for a single vehicle to accurately and reliably estimate such information. To overcome these limitations, we propose a new learning-based collaborative estimation approach by fusing information from a fleet of networked vehicles. However, information exchange among these vehicles necessary for collaborative estimation may disclose sensitive information such as individual vehicle’s identity, which poses serious privacy threats. To address this issue, we propose a unified privacy-preserving collaborative estimation framework which allows connected vehicles to iteratively refine estimation results through exploiting sequential measurements made by multiple vehicles traversing the same road segment. The collaborative estimation approach systematically incorporates privacy-protection schemes into the estimation design and exploits estimation dynamics to obscure exchanged information. Different from patching conventional privacy mechanisms like differential privacy that will compromise algorithmic accuracy or homomorphic encryption that will incur heavy communication/computation overhead, the dynamics enabled privacy protection does not sacrifice accuracy or significantly increase communication/computation overhead. Numerical simulations confirm the effectiveness of our proposed approach.