Pedestrian Behavior Interacting with Autonomous Vehicles during Unmarked Midblock Multilane Crossings: Role of Infrastructure Design, AV Operations and Signaling
Zou, Fengjiao, Ogle, Jennifer, Jin, Weimin, Gerard, Patrick, Petty, Daniel, Robb, Andrew
–arXiv.org Artificial Intelligence
ABSTRACT One of the main challenges autonomous vehicles (AVs) will face is interacting with pedestrians, especially at unmarked midblock locations where the right-of-way is unspecified. This study investigates pedestrians' crossing behavior given different roadway centerline features (i.e., undivided, two-way left-turn lane (TWLTL), and median) and various AV operational schemes portrayed to pedestrians through on-vehicle signals (i.e., no signal, yellow negotiating indication, and yellow/blue negotiating/no-yield indications). This study employs virtual reality (VR) to simulate an urban unmarked midblock environment where pedestrians interact with AVs as they cross a four-lane arterial roadway. Results demonstrate that both roadway centerline design features and AV operations and signaling significantly impact pedestrians' unmarked midblock crossing behavior, including the waiting time at the curb, waiting time in the middle of the road, and the total crossing time. Participants in the undivided scene spent a longer time waiting at the curb and walking on the road than in the median and TWLTL scenes, but they spent a shorter time waiting in the middle of the road. Compared to the AV without a signal, the design of yellow signal significantly reduced pedestrian waiting time at the curb and in the middle. But yellow/blue significantly increased the pedestrian waiting time. Interaction effects between roadway centerline design features and AV operations and signaling are significant only for waiting time in the middle of the road. For middle waiting time, yellow/blue signals had the most impact on the median roadway type and the least on the undivided road. Other factors, such as demographics, past behaviors, and walking exposure of pedestrians, are also explored. Results indicate that older individuals tend to wait longer before making crossing decisions, and pedestrians' past crossing behaviors and past walking exposures do not significantly impact pedestrian walking behavior interacting with AV. INTRODUCTION Between 2011 and 2020, the US witnessed a 46% increase in pedestrian fatalities in motor vehicle crashes, resulting in over 55,000 pedestrian deaths (NHTSA, 2020). In 2020 alone, 6,516 pedestrians were killed in traffic crashes, while approximately 54,769 were injured (NHTSA, 2022). On average, one pedestrian was killed every 81 minutes and injured every 10 minutes in traffic crashes, and pedestrian deaths accounted for 17 percent of all traffic fatalities in 2020 (NHTSA, 2022). Most of these pedestrian fatal and injury crashes occurred in urban areas (82%) rather than rural areas (18%), with 75% of them at midblock locations (NHTSA, 2022).
arXiv.org Artificial Intelligence
Mar-30-2023
- Country:
- Europe (0.04)
- North America > United States
- Iowa (0.04)
- New Jersey > Bergen County
- Mahwah (0.04)
- Genre:
- Research Report
- Experimental Study (1.00)
- New Finding (1.00)
- Research Report
- Industry:
- Transportation > Ground > Road (1.00)
- Technology: