{"title":"行人自动紧急制动系统检测和减速的昼夜性能差异","authors":"Zeinab Bayati, Asad J. Khattak, Nastaran Moradloo","doi":"10.1016/j.aap.2025.108221","DOIUrl":null,"url":null,"abstract":"<div><div>The alarming increase in pedestrian fatalities highlights the urgent need for effective protection technologies. One such technology is the Pedestrian Automatic Emergency Braking (P-AEB) system, a driver assistance feature that provides temporary braking support to help prevent crashes. Despite their availability, the effectiveness of the P-AEB system varies significantly, necessitating continuous performance evaluations under various conditions, especially during nighttime, when 75% of fatal pedestrian crashes occur. This study evaluates the effectiveness of P-AEB systems under varying visibility conditions by analyzing 2,494 tests, comprising 1,654 nighttime tests and 840 daytime tests, conducted with 42 vehicles manufactured between 2021 and 2024. The experimental data were sourced from the Insurance Institute for Highway Safety. A random-effects Heckman sample selection model estimates the detection probabilities and deceleration rates, accounting for unobserved heterogeneity across vehicles and test scenarios. The results show that the detection rates were approximately 98% during the day, 87% at night under low-beam headlights, and 93% at night under high-beam headlights. Despite detection, crashes still occurred in 23% of low beam tests, compared to just 10% during the day. Additionally, crashes at night generally occurred at higher speeds. Furthermore, the model’s results show that halogen low beams can reduce detection capability by up to 43% compared to daytime, underscoring the need for improved P-AEB performance under low-light conditions. This study also incorporates pedestrian movement and vehicle characteristics, such as fuel type and size, revealing their notable association with P-AEB performance. The findings aim to improve pedestrian safety by enhancing P-AEB system effectiveness across varying lighting conditions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"221 ","pages":"Article 108221"},"PeriodicalIF":6.2000,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Day and night performance differences in detection and deceleration by pedestrian automatic emergency braking systems\",\"authors\":\"Zeinab Bayati, Asad J. Khattak, Nastaran Moradloo\",\"doi\":\"10.1016/j.aap.2025.108221\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The alarming increase in pedestrian fatalities highlights the urgent need for effective protection technologies. One such technology is the Pedestrian Automatic Emergency Braking (P-AEB) system, a driver assistance feature that provides temporary braking support to help prevent crashes. Despite their availability, the effectiveness of the P-AEB system varies significantly, necessitating continuous performance evaluations under various conditions, especially during nighttime, when 75% of fatal pedestrian crashes occur. This study evaluates the effectiveness of P-AEB systems under varying visibility conditions by analyzing 2,494 tests, comprising 1,654 nighttime tests and 840 daytime tests, conducted with 42 vehicles manufactured between 2021 and 2024. The experimental data were sourced from the Insurance Institute for Highway Safety. A random-effects Heckman sample selection model estimates the detection probabilities and deceleration rates, accounting for unobserved heterogeneity across vehicles and test scenarios. The results show that the detection rates were approximately 98% during the day, 87% at night under low-beam headlights, and 93% at night under high-beam headlights. Despite detection, crashes still occurred in 23% of low beam tests, compared to just 10% during the day. Additionally, crashes at night generally occurred at higher speeds. Furthermore, the model’s results show that halogen low beams can reduce detection capability by up to 43% compared to daytime, underscoring the need for improved P-AEB performance under low-light conditions. This study also incorporates pedestrian movement and vehicle characteristics, such as fuel type and size, revealing their notable association with P-AEB performance. The findings aim to improve pedestrian safety by enhancing P-AEB system effectiveness across varying lighting conditions.</div></div>\",\"PeriodicalId\":6926,\"journal\":{\"name\":\"Accident; analysis and prevention\",\"volume\":\"221 \",\"pages\":\"Article 108221\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2025-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accident; analysis and prevention\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0001457525003094\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ERGONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accident; analysis and prevention","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0001457525003094","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ERGONOMICS","Score":null,"Total":0}
Day and night performance differences in detection and deceleration by pedestrian automatic emergency braking systems
The alarming increase in pedestrian fatalities highlights the urgent need for effective protection technologies. One such technology is the Pedestrian Automatic Emergency Braking (P-AEB) system, a driver assistance feature that provides temporary braking support to help prevent crashes. Despite their availability, the effectiveness of the P-AEB system varies significantly, necessitating continuous performance evaluations under various conditions, especially during nighttime, when 75% of fatal pedestrian crashes occur. This study evaluates the effectiveness of P-AEB systems under varying visibility conditions by analyzing 2,494 tests, comprising 1,654 nighttime tests and 840 daytime tests, conducted with 42 vehicles manufactured between 2021 and 2024. The experimental data were sourced from the Insurance Institute for Highway Safety. A random-effects Heckman sample selection model estimates the detection probabilities and deceleration rates, accounting for unobserved heterogeneity across vehicles and test scenarios. The results show that the detection rates were approximately 98% during the day, 87% at night under low-beam headlights, and 93% at night under high-beam headlights. Despite detection, crashes still occurred in 23% of low beam tests, compared to just 10% during the day. Additionally, crashes at night generally occurred at higher speeds. Furthermore, the model’s results show that halogen low beams can reduce detection capability by up to 43% compared to daytime, underscoring the need for improved P-AEB performance under low-light conditions. This study also incorporates pedestrian movement and vehicle characteristics, such as fuel type and size, revealing their notable association with P-AEB performance. The findings aim to improve pedestrian safety by enhancing P-AEB system effectiveness across varying lighting conditions.
期刊介绍:
Accident Analysis & Prevention provides wide coverage of the general areas relating to accidental injury and damage, including the pre-injury and immediate post-injury phases. Published papers deal with medical, legal, economic, educational, behavioral, theoretical or empirical aspects of transportation accidents, as well as with accidents at other sites. Selected topics within the scope of the Journal may include: studies of human, environmental and vehicular factors influencing the occurrence, type and severity of accidents and injury; the design, implementation and evaluation of countermeasures; biomechanics of impact and human tolerance limits to injury; modelling and statistical analysis of accident data; policy, planning and decision-making in safety.