A Universal Control Scheme of Human-Like Steering in Multiple Driving Scenarios

To incorporate the inherent superiorities of an experienced, proficient driver, a human-like control scheme for automated steering systems is proposed in this paper based on the concept of “staying within the safety zone”. Designed in the precognitive architecture, the novel control scheme takes into account key characteristics of human steering behaviors in high-speed conditions by determining the steering activation and the steering intensity based on the inversed-time-to-lane-crossing (iTLC), generating smooth, moderate steering actions to maintain the vehicle within a predefined safety zone. The proposed control scheme provides a universal solution for steering control in multiple driving scenarios by accordingly allocating the safety zone to ensure the driving safety therein. A simulator experiment was conducted to test both the fully autonomous and the driver-in-the-loop performance of the proposed control scheme in lane keeping and lane changing. A traditional error-minimizing control algorithm was implemented as a comparison. Results indicate that the novel scheme outperforms the conventional control schemes by yielding higher steering performance, lower steering effort, and higher robustness, proving the benefits of imitating the patterns of human control behaviors in the control algorithm of an automated steering system.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2020.2982002
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Cheng, Shuai
  • Song, Jian
  • Fang, Shengnan
• Publication Date: 2021-5

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 3135-3145
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 5
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Automatic steering control; Autonomous vehicles; Driver support systems; Drivers; Driving simulators; Human beings; Risk assessment; Vehicle safety
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01773638
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: May 31 2021 8:19PM