MPC-CSAS: Multi-Party Computation for Real-time Privacy-preserving Speed Advisory Systems

TL;DR

This paper introduces MPC-CSAS, a multi-party computation approach enabling real-time, privacy-preserving consensus speed advisory for vehicles, overcoming previous limitations of slow convergence and restricted cost functions.

Contribution

The paper presents a novel MPC-based method for privacy-preserving CSAS that is simple, fast, and applicable to all vehicle cost functions, suitable for real-time decision making.

Findings

Achieves system performance in one iteration

Applicable to all vehicle cost functions

No extra infrastructure needed

Abstract

As a part of Advanced Driver Assistance Systems (ADASs), Consensus-based Speed Advisory Systems (CSAS) have been proposed to recommend a common speed to a group of vehicles for specific application purposes, such as emission control and energy management. With Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) technologies and advanced control theories in place, state-of-the-art CSAS can be designed to get an optimal speed in a privacy-preserving and decentralized manner. However, the current method only works for specific cost functions of vehicles, and its execution usually involves many algorithm iterations leading long convergence time. Therefore, the state-of-the-art design method is not applicable to a CSAS design which requires real-time decision making. In this paper, we address the problem by introducing MPC-CSAS, a Multi-Party Computation (MPC) based design approach for privacy-preserving CSAS. Our proposed method is simple to implement and applicable to all types of cost functions of vehicles. Moreover, our simulation results show that the proposed MPC-CSAS can achieve very promising system performance in just one algorithm iteration without using extra infrastructure for a typical CSAS.