Integrated In-Vehicle Decision Support System for Driving at Signalized Intersections: A Prototype of Smart IoT in Transportation

Making inappropriate driving decisions at signalized intersections is one of the major reasons causing accidents. In this paper, the authors present an integrated in-vehicle decision support system to help making better stop/go decisions as the vehicle is approaching an intersection. The system integrates and utilizes the information from both the vehicle and intersection, supported by the Vehicle-to-Infrastructure (V2I) communications in the era of the Internet of Things (IoT). Its effective decision support models (DSM) are realized with the probabilistic sequential decision making process (PSDMP) which combines a variety of advantages gained from a set of decision rules, where each decision rule is responsible to specific situations and does not require complete information for right decisions. The authors extract decision rules from the existing models of the indecision zone problem. The authors also design new decision rules to utilize the key inputs from vehicle motion, vehicle-driver characteristics, signal timings, intersection geometry and topology, and the definitions of red light running (RLR). The performance of the decision support system is empirically evaluated with simulation experiments. The results show that the system enhances both the safety and the mobility of the vehicles approaching an intersection. The increase in safety and efficiency can be achieved by changing the inputs of the system in a large variable space, not only from the benefits of reducing the perception-reaction time, increasing the green countdown time, extending the yellow interval, and extending the all-red interval, but also from a seamless integration with the RLR definition and specific driver behavior.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AHB25 Standing Committee on Traffic Signal Systems.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Xie, Xiao-Feng
    • Wang, Zun-Jing
  • Conference:
  • Date: 2017


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 17p
  • Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01622937
  • Record Type: Publication
  • Report/Paper Numbers: 17-00671
  • Files: TRIS, TRB, ATRI
  • Created Date: Jan 24 2017 12:03PM