Okuda, Hiroyuki (Nagoya University), Hori, Toshiaki (Nagoya University), Chin, Hyuntai (Nagoya University), Suzuki, Tatsuya (Nagoya University)

Comparative Study of Distributed Computation for Cooperative Adaptive Cruise Control Using Model Prediction

Scheduled for presentation during the Regular Session "Autonomous driving" (FrBT3), Friday, September 27, 2024, 13:50−14:10, Salon 6

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 2024

Abstract

Cooperative adaptive cruise control (CACC) using vehicle-to-vehicle(V2V) communication is attracting considerable attention for reducing energy consumption. The operator (driver) cost and traffic congestion to improve the transportation efficiency. This study presents the distributed computation method to realize the CACC based on model predictive control (MPC) to reduce the computational cost without degrading the control performance. In the proposed method, the optimal input is computed in the computer on each car based on the MPC framework and the obtained result is transmitted to adjacent cars in the platoon instead of simultaneously computing the input for all cars. The solution of the proposed method is investigated analytically and compared with the CACC with centralized computation which can provide an optimal solution but consume much computational power. The control performance and the string stability of the proposed method are validated with the numerical simulation.