  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.
