Abstract

 

As autonomous vehicles enter the stage as a new form of transportation, researchers have developed theories and systems to understand their impact on cities. Autonomous Intersection Management (AIM), for example, rethinks urban intersection control systems when human drivers are replaced by autonomous driving agents. However, pedestrians are often ignored or insufficiently addressed in such systems. The main criteria for evaluating the success or failure of these systems are quantitative metrics such as speed of traffic. But, are these simulation platforms sufficient to represent the complex interactions of pedestrians in cities? This thesis takes a pedestrian-centered approach to this problem. The goal is to computationally identify and simulate the interaction between human and AV and propose a framework in which pedestrian experience in urban life can be studied. What I look for in this thesis is taking a new approach to explore and simulate pedestrians’ experiences in urban environments.

I introduce an immersive experimental framework in Virtual Reality incorporating EEG scanners to identify how an individual interacts with autonomous vehicles in five different urban scenarios. My study investigates how our understanding of pedestrian’s reactions such as excitement, stress, or concentration in conjunction with a qualitative study can foster a deeper understanding of (and design insight about) the new urban environments emerging in the age of autonomous technologies.