Interactions between pedestrians, bikers, and human-driven vehicles have been a major concern in traffic safety over the years. The upcoming age of autonomous vehicles will further raise major problems on whether self-driving cars can accurately avoid accidents; on the other hand, usability issues arise on whether human-driven cars and pedestrian can dominate the road at the expense of the autonomous vehicles which will be programmed to avoid accidents. This paper proposes some game theoretical models applied to related traffic scenarios. In the first two games the reciprocal influence between a pedestrian and a vehicle (either autonomous or not) is analyzed, while the third game investigates the intersection of two vehicles, possibly autonomous. The games have been simulated in order to demonstrate the theoretical analysis and the predicted behaviors. These investigations can shed new lights on how novel urban traffic regulations could be required to allow for a better interaction of vehicles and a general improved management of traffic and communication vehicular networks.
In urban environments traffic safety is primarily determined by the successful interaction between road users. In order to define behavioral requirements for future autonomous vehicles, an understanding of these interactions is needed. Therefore this paper analyzes the interaction between drivers and pedestrians at crosswalks from both the driver's and the pedestrian's perspective. A study with driving experiments in real-life traffic was conducted. The study not only investigates how drivers respond to pedestrians who intend to use the crosswalk, but also their response to pedestrians who waive their right-of-way. An analysis of recorded driving and video data identified typical behavior patterns of drivers approaching a crosswalk, as well as of pedestrians crossing on it. Supplemented by questionnaires filled in by drivers and pedestrians, we determined factors which have a relevant impact on successful driver-pedestrian interaction. We confirmed that with higher initial speeds, drivers tend to exhibit more anticipatory driving behavior. Contrary to previous studies, the presented results show a significant influence of the vehicle's speed on the pedestrian's decision process. The gained insights into the driver-pedestrian interaction process provide an opportunity to develop autonomous driving functions with a cooperative driving behavior that conforms to the expectations of both pedestrians and vehicle occupants. Interactions between vehicles and pedestrians have always been a major problem in traffic safety. Experienced human drivers are able to analyze the environment and choose driving strategies that will help them avoid crashes. What is not yet clear, however, is how automated vehicles will interact with pedestrians. This paper proposes a new method for evaluating the safety and feasibility of the driving strategy of automated vehicles when encountering unsignalized crossings. MobilEye sensors installed on buses in Ann Arbor, Michigan, collected data on 2,973 valid crossing events. A stochastic interaction model was then created using a multivariate Gaussian mixture model. This model allowed us to simulate the movements of pedestrians reacting to an oncoming vehicle when approaching unsignalized crossings, and to evaluate the passing strategies of automated vehicles. A simulation was then conducted to demonstrate the evaluation procedure. 