Intelligent speed adaptation in intelligent vehicle highway systems - A model predictive control approach


Reference:
L. Baskar, B. De Schutter, and H. Hellendoorn, "Intelligent speed adaptation in intelligent vehicle highway systems - A model predictive control approach," Proceedings of the 10th TRAIL Congress 2008 - TRAIL in Perspective - CD-ROM, Rotterdam, The Netherlands, 13 pp., Oct. 2008.

Abstract:
Intelligent Vehicle Highway Systems (IVHS) consist of automated highway systems in combination with intelligent vehicles and roadside controllers. The intelligent vehicles can communicate with each other and with the roadside infrastructure. The vehicles are organised in platoons with short intraplatoon distances, and larger distances between platoons. Moreover, all vehicles are assumed to be automated, i.e., throttle, braking, and steering commands are determined by an automated on-board controller. In this paper we first propose a model predictive control (MPC) approach to determine appropriate speeds for the platoons. Next, we discuss which prediction models are suited to be used as an on-line traffic prediction model in MPC for IVHS. The proposed approach is then applied to a simple simulation example in which the aim is to minimise the total time all vehicles spend in the network by optimally assigning speeds to the platoons.


Downloads:
 * Corresponding technical report: pdf file (136 KB)
      Note: More information on the pdf file format mentioned above can be found here.


Bibtex entry:

@inproceedings{BasDeS:08-021,
        author={L. Baskar and B. {D}e Schutter and H. Hellendoorn},
        title={Intelligent speed adaptation in intelligent vehicle highway systems -- {A} model predictive control approach},
        booktitle={Proceedings of the 10th TRAIL Congress 2008 -- TRAIL in Perspective -- CD-ROM},
        address={Rotterdam, The Netherlands},
        month=oct,
        year={2008}
        }



Go to the publications overview page.


This page is maintained by Bart De Schutter. Last update: March 21, 2022.