We proposed a smart IoT prototype to make in-vehicle decisions for improving both traffic safety and mobility. This technique can be combined with the smart traffic signals to gain more potential in achieving safer and more sustainable urban mobility.

  • X. Xie and Z. Wang, “Integrated in-vehicle decision support system for driving at signalized intersections: A prototype of smart IoT in transportation,” in Transportation Research Board (TRB) Annual Meeting, Washington, DC, 2017. [DOI] [Bibtex]
    title={Integrated in-vehicle decision support system for driving at signalized intersections: A prototype of smart {IoT} in transportation},
    author={Xie, Xiao-Feng and Wang, Zun-Jing},
    Booktitle = {Transportation Research Board (TRB) Annual Meeting},
    Address = {Washington, DC},

Making inappropriate driving decisions at signalized intersections is one of the major reasons causing accidents. In this work, we 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.

Smart IoT for Transportation: In-Vehicle System to Make Better & Safer Decisions
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