Assessing the Effectiveness of Connected Vehicle Technologies based on Driving Simulator Experiments

PI (Co-PI): Mohamed Abdel-Aty, Yina Wu

Funded by Safety Research Using Simulation (SAFER-SIM)

The new emerging Connected Vehicle (CV) technologies could assist road users to improve their safety with the advanced warnings of impending dangers. The CV technologies fulfill the real-time communication between vehicles (V2V), vehicles & roadway infrastructures (V2I), and vehicles & pedestrians (V2P), which enable various types of driving alerts could be provided to road users according to the types of potential crashes. Although extensive efforts are underway in developing and improving CV technologies, it is still unclear what is the safety benefits of CV technologies and how the impact of CV technologies is various at different warning situations. Thus, there is a real need to explore the difference in CV technologies’ impact and to quantify the effectiveness of CV technologies. In this project, two types of CV technologies, i.e. Cooperative Forward Collision Warning (CFCW) technology and Pedestrian-to-Vehicle (P2V) warning technology, would be tested and evaluated by driving simulator experiments. The CFCW technology targets rear-end crashes and could be more efficient than most of the current rear-end mitigation technologies by communicating with any surrounding vehicles that may relate to potential rear-end crashes. Meanwhile, since the number of the pedestrian-related crash has shown an increasing trend in recent years, the effectiveness of the P2V technology will also be explored in this project. The P2V warning technology could deal with the situation that a vehicle striking a pedestrian in the first event of a crash. The effectiveness of these two CV technologies will also be evaluated at different locations (e.g. intersection, arterial, and freeway) or under different environmental conditions (e.g. weather conditions, lighting conditions, etc.). Data from the National Automotive Sampling System (NASS), General Estimates System (GES), and Fatality Analysis Reporting System (FARS) will be collected to identify pre-crash scenarios as well as crash contributing factors that could be mitigated by the CFCW or P2V technologies. Then, the research team will design driving simulator experiments to embody the warnings using Heads-up Display (HUD). We will also calculate the reduced percentages for the crashes of interest by the CV technologies during the experiments and evaluate the drivers’ corrective actions (e.g., braking, lane changing) with the presence of CV technologies. By incorporating the experiment results, the crash data, and previous literature, the number of crashes that could be prevented by these CV technologies at different locations will be quantified. Besides, the optimal HUD design will be recommended based on driving behaviors.

There are three main general objectives to be fulfilled in the research:

  • Evaluate drivers’ responses to CFCW technology and P2V technology;
  • Quantify the effectiveness of CFCW technology and P2V technology based on both crash data and driving simulation data;
  • Recommend the CFCW technology and P2V technology warning systems’ design for different situations.

Illustration of P2V Warning