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Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap

Bibliographic Data

ID15466247
AuthorsHongjia Zhang (0009-0001-4486-800X, Chang'an University), Yingshi Guo (0000-0001-9384-7558, Chang'an University), Yunxing Chen (0000-0003-4985-1669, Chang'an University), Qinyu Sun (0000-0002-6446-0570, Chang'an University), Chang Wang (0009-0005-4771-6916, Chang'an University, corresponding author)
Year2020
Volume17
Issue24
Pages9247-9247
Publication date2020-12-10
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph17249247
PMID33321945
OpenAlexW3112186230
LanguageEN
Citations received1
References cited24

Numerous traffic crashes occur every year on zebra crossings in China. Pedestrians are vulnerable road users who are usually injured severely or fatally during human-vehicle collisions. The development of an effective pedestrian street-crossing decision-making model is essential to improving pedestrian street-crossing safety. For this purpose, this paper carried out a naturalistic field experiment to collect a large number of vehicle and pedestrian motion data. Through interviewed with many pedestrians, it is found that they pay more attention to whether the driver can safely brake the vehicle before reaching the zebra crossing. Therefore, this work established a novel decision-making model based on the vehicle deceleration-safety gap (VD-SGM). The deceleration threshold of VD-SGM was determined based on signal detection theory (SDT). To verify the performance of VD-SGM proposed in this work, the model was compared with the Raff model. The results show that the VD-SGM performs better and the false alarm rate is lower. The VD-SGM proposed in this work is of great significance to improve pedestrians' safety. Meanwhile, the model can also increase the efficiency of autonomous vehicles

Brake · Pedestrian · Pedestrian crossing · Poison control · Simulation · Transport engineering · Work (physics · Autonomous Vehicle Technology and Safety · Computer Science · Engineering · Human-Automation Interaction and Safety · Medicine · Traffic and Road Safety · Automotive Engineering

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  • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

    Open Access•Cynthia Rudin•Nature Machine Intelligence•2019

Citations per year0.25
Citation span2022 - 2022 (1)
Highly citedNo

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