A Collision-Free Car-Following Model for Connected Automated Vehicles

Driving safety under predictable emergency conditions for automated vehicles is a main concern of both drivers and vehicle producers. A Collision-Free car-Following Model (CFFM) for connected automated vehicles that can prevent rear-end collisions when the preceding vehicle suddenly brakes is proposed in this paper. The first step is to obtain an acceleration set that can ensure safety under predictable dangerous conditions, and then to decide on the exact acceleration by considering efficiency and comfort. Based on subject and the preceding (leading) vehicle data obtained from connected vehicles, the following vehicle can determine its objective acceleration through CFFM. Compared to existing semi-automated cooperative driving car-following models, the advantages and disadvantages of CFFM as they relate to safety, efficiency and string stability are evaluated. Results show that CFFM can guarantee safety under both gentle and hard braking of preceding vehicles at any time and thus can be used in complex traffic environments, namely congested urban roads. Moreover, it is demonstrated that CFFM has outstanding performance in terms of both efficiency and string stability. Comparison with Newell’s trajectory replication model shows that CFFM is a practical model that both accomplishes trajectory replication and also supplements it in that Newell’s rule is only applicable in stable car-following scenarios while the CFFM can be applied in a variety of scenarios including catching-up.

• Supplemental Notes:
  • This paper was sponsored by TRB committee AHB45 Standing Committee on Traffic Flow Theory and Characteristics.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Li, Li
  • 0000-0002-7580-3559
  • Ma, Wanjing
• Conference:
  • Transportation Research Board 96th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2017-1-8 to 2017-1-12
• Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: 18p
• Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Autonomous intelligent cruise control; Car following; Crash avoidance systems; Mathematical models; Mechanical acceleration; Mobile communication systems; Rear end crashes; Vehicle trajectories
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01630618
• Record Type: Publication
• Report/Paper Numbers: 17-03236
• Files: TRIS, TRB, ATRI
• Created Date: Mar 28 2017 5:07PM