Comparison of Connected Automated Vehicle to Pedestrian Interaction Systems to Reduce Vehicle Waiting Times

As autonomous vehicle technology starts to become more common in daily life, a degree of uncertainty arises with respect to pedestrian safety. As the technology gets more and more advanced, there becomes greater possibility for lapses in connectivity and miscommunications between pedestrians and vehicles. Hence, it is important that society finds the best way for pedestrians to interact with all types of vehicles in order to keep them safe, in the midst of a continuous increase in pedestrian incidents. Thusly, the focus of this investigation is to determine the feasibility of crossing intersections reliant on pedestrian to vehicle connectivity. As vehicles increase in complexity with respect to their interaction with other vehicles, pedestrians, and the environment, intersections may not require physical infrastructure - autonomous vehicles may be built to recognize pedestrians and to stop accordingly, and signal timing may be periodically uploaded in the form of continuously changing datasets. Such intersections have ambiguous effects on pedestrian and vehicle interactions, partially due to the uncertainty of afore-mentioned connectivity dynamics. Connected and Autonomous Vehicles (CAVs) rely primarily on obstacle detection systems, a main connectivity dynamic between pedestrians and vehicles, to tell them to stop for pedestrians. These systems, many still deep in the development phase, may be flawed. This paper describes an experiment developed with the intention of comparing a traditional obstacle detection approach to pedestrian crossing with one in which vehicles stop based on pedestrians notifying them of their presence. More specifically, in order to cross, pedestrians must "signal" to vehicles that they are in the crossing area (in the real world, this could be done on a mobile phone) so that the vehicles will stop for them. The model, developed using Simio, outputs queue timing and density values for the vehicles and pedestrians. Data was gathered related to average wait times and flow of pedestrians and vehicles over a simulated time frame of a 4 week period. The results indicate that utilizing a pedestrian to vehicle communication framework, either in place of or in addition to traditional obstacle detection and avoidance systems, would prove beneficial in developing more efficient intersections.