Model Predictive Enhanced Adaptive Cruise Control for Multiple Driving Situations

Yongqiang DIng, Huiyan Chen, Jianwei Gong, Guangming Xiong, Gang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

This paper presents an Enhanced Adaptive Cruise Control (EACC) framework that can work in different modes according to the forward targets. The EACC system, which was proposed in this paper, is based on a unified model and can achieve speed tracking, stop go and autonomous emergency braking (AEB). Notably, speed tracking does not require a real preceding vehicle, a virtual vehicle can be set in front of the EACC vehicle. The mathematical method of setting the virtual preceding vehicle and the switching logic between the different working modes of the EACC system were given. Employing a constraints softening method to avoid computing infeasibility, an optimal control law is numerically calculated using the CVXGEN solver. Finally, real vehicle tests show that the EACC framework provides significant benefits in terms of speed-tracking capability, safety and comfort requirements while satisfying driver desired car following characteristics for different driving situations.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Vehicles Symposium, IV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1717-1722
Number of pages6
ISBN (Electronic)9781538644522
DOIs
Publication statusPublished - 18 Oct 2018
Event2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
Duration: 26 Sept 201830 Sept 2018

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2018-June

Conference

Conference2018 IEEE Intelligent Vehicles Symposium, IV 2018
Country/TerritoryChina
CityChangshu, Suzhou
Period26/09/1830/09/18

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