DocumentCode
185097
Title
Model predictive driver assistance control for cooperative cruise based on hybrid system driver model
Author
Okuda, Haruhisa ; Xiaolin Guo ; Tazaki, Yuichi ; Suzuki, Takumi ; Levedahl, Blaine
Author_Institution
Green Mobility Collaborative Res. Center, Nagoya Univ., Nagoya, Japan
fYear
2014
fDate
4-6 June 2014
Firstpage
4630
Lastpage
4636
Abstract
This paper presents a driver assisting system for cooperative cruising of multiple cars. In order to account for personal difference of individual drivers, each driver´s vehicle following skill on expressways is identified as a PrARX model, a continuous approximation of hybrid dynamical system. The PrARX model describes the driver´s logical decision making as well as continuous maneuvering in a uniform manner. The assisting acceleration is computed in the framework of model predictive control, where the plant model is a platoon of cars coupled with PrARX driver models. For computing assisting outputs in real time, a fast computation method for nonlinear model predictive control based on the continuation technique is employed. The proposed assisting system is tested in numerical simulations and on a driving simulator with a real human driver. The high speed calculation of the Homotopy method is also proved by comparison to the conventional method. Finally, the advantage of global optimization for cooperative safety is confirmed by comparing its control performance with the local optimization for individual safety.
Keywords
automobiles; driver information systems; nonlinear control systems; numerical analysis; optimisation; predictive control; vehicle dynamics; PrARX driver models; assisting acceleration; continuation technique; continuous approximation; continuous maneuvering; cooperative cruise; cooperative cruising; cooperative safety; driver assisting system; driver logical decision making; driver vehicle following skill; driving simulator; expressways; global optimization; high speed calculation; homotopy method; human driver; hybrid dynamical system; hybrid system driver model; local optimization; model predictive driver assistance control; multiple cars; nonlinear model predictive control; numerical simulations; plant model; platoon; Acceleration; Computational modeling; Mathematical model; Numerical models; Optimization; Predictive models; Vehicles; Automotive; Human-in-the-loop control; Predictive control for nonlinear systems;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference (ACC), 2014
Conference_Location
Portland, OR
ISSN
0743-1619
Print_ISBN
978-1-4799-3272-6
Type
conf
DOI
10.1109/ACC.2014.6859464
Filename
6859464
Link To Document