Typical-driving-style-oriented Personalized Adaptive Cruise Control design based on human driving data

Reflecting different driving styles in Adaptive Cruise Control (ACC) is of great importance for its market acceptance. A novel data-based method is presented for designing a Personalized Adaptive Cruise Control (PACC) system in this paper. First, a driving-data-acquisition platform is established, and a large amount of real-world driving data from 84 human drivers is collected. To measure the similarity of human drivers quantitatively, the driving data of every driver are regarded as a specific distribution of some features, fitted with a Gaussian mixture model (GMM). Kullback-Leibler (KL) divergence is introduced as the driving similarity index. After that, an unsupervised clustering algorithm is realized in this paper, and these drivers are grouped into three separate groups. A practical PACC structure is designed in the second stage based on the grouped driving data to include different driving characteristics, mainly in three aspects: speed control, distance control, and the switching rule. Then real-vehicle experiments are carried out. Results demonstrate the capabilities of the proposed PACC algorithm to reflect different driving styles.

• Record URL:
  • https://doi.org/10.1016/j.trc.2019.01.025
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X18306521
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhu, Bing
  • 0000-0002-6068-4040
  • Jiang, Yuande
  • Zhao, Jian
  • 0000-0002-9917-6836
  • He, Rui
  • 0000-0002-4288-5842
  • Bian, Ning
  • Deng, Weiwen
• Publication Date: 2019-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 274-288
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 100
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous intelligent cruise control; Driving; Human factors
• Uncontrolled Terms: Driving style; Personalized driver support systems
• Subject Areas: Data and Information Technology; Design; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01692660
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 13 2019 5:16PM