Yalda Rahmati is a Data Scientist at General Motors LLC. She has received her PhD in transportation engineering from University of Illinois at Urbana–Champaign in 2020. Her research focus is on developing a collaborative, connected automated driving environment via modeling the interactions among automated vehicles, human-driven vehicles, and non-motorized modes of transportation
Continuing evolution in automotive technology along with the growing number of related research in academia and industry gives the impression that self-driving cars are no longer a fantasy. Fueled by recent advances in computation capabilities, sensing, and navigation technologies, autonomous vehicles are envisaged to provide new levels of safety, mobility, and efficiency by taking human errors out of the driving equations. Yet, numerous critical hurdles remain, impeding the full (or even partial) operation of connected automated vehicles (CAVs) on public roads beyond the testing phases. Automated vehicles’ operation hinges on standard algorithms developed for robotic applications. CAVs are, in essence, automated decision-making systems designed to perform driving tasks in a connected environment. Thanks to technology advancements, the problems of robot localization, control, and route finding in stationary environments around inanimate obstacles seem to be largely solved. However, the majority of robotic operations are designed and tested in confined environments with no/minimum human-robot interactions. Proximity to humans introduces a new set of system complexities that justifies the need for a reliable technical framework to ensure safe and efficient human-robot coexistence. In fact, in order to assess the overall impact of CAVs on traffic flow dynamics, human-CAV interactions in mixed traffic environments should be evaluated from both the humans’ and CAVs’ perspectives. From CAVs’ perspective, navigating in dynamic environments relies on estimating the future motion of surrounding obstacles and predicting potential conflicts. This becomes even more critical where no clear traffic rule defines priority, such as jaywalking pedestrians, parking lots, and unmarked intersections. Since direct human-CAV communication is not often possible, CAVs should resort to algorithms that use available sensory data to predict the future movement of surrounding road users and plan accordingly. A realistic model of human behavior is then vital to capture humans’ interactive behavior with others and provide the vehicle with accurate predictions of their future decisions. The second perspective in analyzing human-CAV interactions is to focus on human road users and explore how they might react to sharing roads with automated vehicles.
شما می توانید ویدئوی جلسه برگزار شده را مشاهده نموده و مستندات مربوط به آن را از طریق لینک زیر دانلود نمایید.
