Quantifying the Role of Kinematic and Behavioral Features in Driver-Pedestrian Interaction across Environments: An Inverse Reinforcement Learning Approach
Name
noonan-et-al-2025-quantifying-the-role-of-kinematic-and-behavioral-features-in-driver-pedestrian-interaction-across.pdf
Description
Published version
Size
508.86 KB
Format
Adobe PDF
Checksum (MD5)
23d803f9980e067e929fcab8deafedc2
Author(s)
Noonan, T Zach
Gershon, Pnina
Domeyer, Josh
Mehler, Bruce
Reimer, Bryan
Date Issued
September 2025
Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Publisher
SAGE Publications
Citation
Noonan, T. Z., Gershon, P., Domeyer, J., Mehler, B., & Reimer, B. (2025). Quantifying the Role of Kinematic and Behavioral Features in Driver-Pedestrian Interaction across Environments: An Inverse Reinforcement Learning Approach. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 69(1), 1323-1328.
Version
Final published version
Abstract
This study examined real-world driver-pedestrian encounters to identify key interaction features and assess how the importance of these features is mediated by protection afforded by the environment. Using inverse reinforcement learning, we estimated the utility functions to evaluate the relative importance of different aspects of the interaction for each road user and how they differ between undesignated (e.g., jaywalking) and designated (e.g., zebra crossings) crossings. Pedestrian pausing behavior and dynamic features like acceleration changes and time gaps were important at designated crossings, whereas undesignated crossings relied on distances and bidirectional gaze, highlighting reliance on non-verbal cues. This work builds on previous studies analyzing the role of environmental features on interaction, communication, and negotiation between drivers and pedestrians. Understanding driver-pedestrian communication and identifying the most important interaction features may enhance the design of effective and coordinated driver-pedestrian interaction strategies, especially in the context of automated driving systems.
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
AgeLab (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/10711813251370741
