Several studies have developed pedestrian-vehicle interaction models. However, these studies failed to consider pedestrian distraction, which considerably influences the safety of these interactions. Utilizing data from two intersections in Vancouver, Canada, this research uses the Multi-agent Adversarial Inverse Reinforcement Learning (MA-AIRL) framework to make inferences about the behavioral dynamics of distracted and non-distracted pedestrians while interacting with vehicles. Results showed that distracted pedestrians maintained closer proximity to vehicles, moved at reduced speeds, and rarely yielded to oncoming vehicles. In addition, they rarely changed their interaction angles regardless of lateral proximity to vehicles, indicating that they mostly remain unaware of the surrounding environment and have decreased navigational efficiency. Conversely, non-distracted pedestrians executed safer maneuvers, kept greater distances from vehicles, yielded more frequently, and adjusted their speeds accordingly. For example, non-distracted pedestrian-vehicle interactions showed a 46.5% decrease in traffic conflicts severity (as measured by the average Time-to-Collision (TTC) values) and an average 30.2% increase in minimum distances when compared to distracted pedestrian-vehicle interactions. Vehicle drivers also demonstrated different behaviors in response to distracted pedestrians. They often opted to decelerate around distracted pedestrians, indicating recognition of potential risks. Furthermore, the MA-AIRL framework provided different results depending on the type of interactions. The performance of the distracted vehicle-pedestrian model was lower than the non-distracted model, suggesting that predicting non-distracted behavior might be relatively easier. These findings emphasize the importance of refining pedestrian simulation models to include the unique behavioral patterns from pedestrian distractions. This should assist in further examining the safety impacts of pedestrian distraction on the road environment.
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http://dx.doi.org/10.1016/j.aap.2024.107789 | DOI Listing |
Accid Anal Prev
March 2025
Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India. Electronic address:
Pedestrians use visual cues (i.e., gaze) to communicate with the other road users, and visual attention towards the surrounding environment is essential to be situationally aware and avoid oncoming conflicts.
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January 2025
Department of Systems Engineering, City University of Hong Kong, Hong Kong, China; Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China.
Autonomous vehicles (AVs) should prioritise pedestrian safety in a traffic accident. External human-machine interfaces (eHMIs), which enhance communication through visual and auditory signals, become essential as AVs become prevalent. This study aimed to investigate the current state of research on eHMIs, with a specific focus on pedestrian interactions with eHMI-equipped AVs.
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December 2024
Department of Civil Engineering, University of British Columbia, Canada. Electronic address:
Several studies have developed pedestrian-vehicle interaction models. However, these studies failed to consider pedestrian distraction, which considerably influences the safety of these interactions. Utilizing data from two intersections in Vancouver, Canada, this research uses the Multi-agent Adversarial Inverse Reinforcement Learning (MA-AIRL) framework to make inferences about the behavioral dynamics of distracted and non-distracted pedestrians while interacting with vehicles.
View Article and Find Full Text PDFDigit Health
August 2024
College of Nursing, Hanyang University, Seongdong-gu, Seoul, South Korea.
Objective: The objective of this study was to determine the most effective cut-off point for the Smombie Scale and evaluate its ability to screen for pedestrian safety risks among young adults.
Methods: Data were obtained from an online sample of 396 Korean young adults aged 18-39 years. Latent profile analysis was used to distinguish the risk group as a reference measure for the Smombie Scale.
Front Neurorobot
March 2024
Waymo LLC, Mountain View, CA, United States.
Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior.
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