Sequential Convex Programming for Collaboration of Connected and Automated Vehicles

TL;DR

This paper presents a sequential convex programming approach to efficiently solve nonlinear, nonconvex model predictive control problems for connected and automated vehicles, enabling real-time collaboration in autonomous driving scenarios.

Contribution

It introduces a SCP-based method that linearizes, discretizes, and convexifies the MPC problem, significantly reducing computational complexity for real-time application.

Findings

SCP effectively reduces computation time for CAV coordination.

The approach is validated in three autonomous driving scenarios.

Simulation results demonstrate improved efficiency and practicality.

Abstract

This paper investigates the collaboration of multiple connected and automated vehicles (CAVs) in different scenarios. In general, the collaboration of CAVs can be formulated as a nonlinear and nonconvex model predictive control (MPC) problem. Most of the existing approaches available for utilization to solve such an optimization problem suffer from the drawback of considerable computational burden, which hinders the practical implementation in real time. This paper proposes the use of sequential convex programming (SCP), which is a powerful approach to solving the nonlinear and nonconvex MPC problem in real time. To appropriately deploy the methodology, as a first stage, SCP requires linearization and discretization when addressing the nonlinear dynamics of the system model adequately. Based on the linearization and discretization, the original MPC problem can be transformed into a quadratically constrained quadratic programming (QCQP) problem. Besides, SCP also involves convexification to handle the associated nonconvex constraints. Thus, the nonconvex QCQP can be reduced to a quadratic programming (QP) problem that can be solved rather quickly. Therefore, the computational efficiency is suitably improved despite the existence of nonlinear and nonconvex characteristics, whereby the implementation is realized in real time. Furthermore, simulation results in three different scenarios of autonomous driving are presented to validate the effectiveness and efficiency of our proposed approach.