How do drivers react to crossing pedestrians at unsignalized roads? A contrast study for naturalistic driving and dummy pedestrian test

Safe street crossing relies on effective pedestrian-vehicle interaction; a delay in drivers’ recognition of pedestrian intentions can critically increase the risk of collision. Nevertheless, existing research lacks a contrasting exploration of driving behaviour at unsignalized streets for real pedestrians and pedestrian dummies. In this study, we develop a pedestrian crossing intention judgment system to quantify subjective driver assessments of pedestrian crossing intentions, focusing on how drivers adjust their behaviour to real pedestrians and pedestrian dummies. Specifically, we establish a naturalistic driving and pedestrian dummy experimental platform based on the campus unsignalized streets. Then, six different experimental scenarios of pedestrians crossing the street are designed. In addition, a subjective judgment of pedestrians’ crossing intentions (SJPCI) ranging from 0% to 100% is introduced to quantify drivers’ assessments of pedestrian crossing intentions. The proposed system is validated using 123 effective interactions extracted from 1923 natural driving events. Meanwhile, we designed the same dummy experiment based on the 123-group naturalistic driving scenarios. The results show that naturalistic driving experiments have relatively higher SJPCI values, gentler acceleration, and longer time-to-collision (TTC) compared with pedestrian dummy experiments. Effective communication with pedestrians allows drivers to better judge crossing intentions. Our finding is that real crossing pedestrians are significantly less able to have ambiguous intentions, which enhances understandability for drivers.

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Publication Details

Journal
Scientific Reports
Published
2026-01-13
DOI
https://doi.org/10.1038/s41598-025-34488-0
Primary Topic
Traffic and Road Safety
Type
article
Field-Weighted Citation Impact
0.00

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article

How do drivers react to crossing pedestrians at unsignalized roads? A contrast study for naturalistic driving and dummy pedestrian test

Shulei Sun, Wenxiao Zhang, Bingxu Chen, Jun Li et al.
Scientific Reports
Traffic and Road Safety
article

How do drivers react to crossing pedestrians at unsignalized roads? A contrast study for naturalistic driving and dummy pedestrian test

Shulei Sun, Wenxiao Zhang, Bingxu Chen, Jun Li, Xiaorong Huang, Ziqiang Zhang
article en

Abstract

Safe street crossing relies on effective pedestrian-vehicle interaction; a delay in drivers’ recognition of pedestrian intentions can critically increase the risk of collision. Nevertheless, existing research lacks a contrasting exploration of driving behaviour at unsignalized streets for real pedestrians and pedestrian dummies. In this study, we develop a pedestrian crossing intention judgment system to quantify subjective driver assessments of pedestrian crossing intentions, focusing on how drivers adjust their behaviour to real pedestrians and pedestrian dummies. Specifically, we establish a naturalistic driving and pedestrian dummy experimental platform based on the campus unsignalized streets. Then, six different experimental scenarios of pedestrians crossing the street are designed. In addition, a subjective judgment of pedestrians’ crossing intentions (SJPCI) ranging from 0% to 100% is introduced to quantify drivers’ assessments of pedestrian crossing intentions. The proposed system is validated using 123 effective interactions extracted from 1923 natural driving events. Meanwhile, we designed the same dummy experiment based on the 123-group naturalistic driving scenarios. The results show that naturalistic driving experiments have relatively higher SJPCI values, gentler acceleration, and longer time-to-collision (TTC) compared with pedestrian dummy experiments. Effective communication with pedestrians allows drivers to better judge crossing intentions. Our finding is that real crossing pedestrians are significantly less able to have ambiguous intentions, which enhances understandability for drivers.

Scientific ReportsVol. 16(1)
Xihua University (CN), Xinjiang Institute of Engineering (CN), Yibin University (CN)
Xihua University, Key Laboratory of Fluid and Power Machinery, Ministry of Education
Sustainable cities and communities
Openalex Percentile: Top 92%
Traffic and Road Safety
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