Objective: This study is designed to evaluate heavy-truck drivers' following behavior and how a crash warning system influences their headway maintenance.
Background: Rear-end crashes are one of the major crash types involving heavy trucks and are more likely than other crash types to result in fatalities. Previous studies have observed positive effects of in-vehicle crash warning systems in passenger car drivers. Although heavy-truck drivers are generally more experienced, driver-related errors are still the leading factors contributing to heavy-truck-related rear-end crashes.
Method: Data from a 10-month naturalistic driving study were used. Participants were 18 professional heavy-truck drivers who received warnings during the last 8 months of the study (treatment period) but not during the first 2 months (baseline period). Time headway and driver's brake reaction time were extracted and compared with condition variables, including one between-subjects variable (driver shift) and five within-subjects variables (treatment condition, roadway types, traffic density, wiper state, and trailer configuration).
Results: The presence of warnings resulted in a 0.28-s increase of mean time headway with dense on-road traffic and a 0.20-s increase with wipers on. Drivers also responded to the forward conflicts significantly faster (by 0.26 s, a 15% enhancement) in the treatment condition compared with responses in the baseline condition.
Conclusion: Positive effects on heavy-truck drivers' following performance were observed with the warning system.
Application: The installation of such in-vehicle crash warning systems can help heavy-truck drivers keep longer headway distances in challenging situations and respond quicker to potential traffic conflicts, therefore possibly increasing heavy-truck longitudinal driving safety.
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http://dx.doi.org/10.1177/0018720812439412 | DOI Listing |
Traffic Inj Prev
November 2024
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China.
Objective: The paper aims to explore the possibility of using traffic violations as indicators for spatial-temporal risk of traffic safety within road network constraints, identify key types of traffic violations that indicate spatial-temporal risks in road traffic safety, and investigate their distribution patterns at the road section level.
Methods: Firstly, we employ the Ripley's K function with network constraints and utilize rigorous statistical inference to thoroughly examine the spatial-temporal correlation between various types of traffic violations and crashes, identifying key types that exhibit significant correlation with crashes. Secondly, we combine Ripley's K function with network constraints, Network Kernel Density Estimation, and Local Moran's Index, to identify high-incidence road sections of these violations.
Traffic Inj Prev
November 2024
Insurance Institute for Highway Safety, Ruckersville, Virginia.
Objective: Automatic emergency braking systems with pedestrian detection (PAEB) are effective at preventing pedestrian crashes, but the safety benefits are not observed at night. This study used the Insurance Institute for Highway Safety (IIHS) PAEB test data to characterize PAEB responses in different lighting conditions and for different rated systems.
Methods: Data from 6,919 IIHS PAEB tests were retrieved from IIHS databases.
Hum Factors
October 2024
AAA Foundation for Traffic Safety, USA.
Objective: The current study investigated the factors that predict drowsy drivers' decisions regarding whether to take breaks versus continue driving during long simulator drives.
Background: Driver drowsiness contributes to substantial numbers of motor vehicle crashes, injuries, and deaths. Previous research has shown that taking a nap and consuming caffeine can temporarily mitigate drowsiness and enable continued safe driving.
Accid Anal Prev
December 2024
DidiChuxing, Hongyuan Business Center, No. 28, Shangdi West Road, Haidian District, Beijing, China. Electronic address:
The Forward Collision Warning (FCW) system has been widely equipped on vehicles to reduce rear-end crashes, which are considered the most common type of crash. However, existing FCW systems have the problem of low response rates, which restrict their safety improvement effects. This study aims to address this issue by building personalized FCW models based on human risk preferences.
View Article and Find Full Text PDFSci Rep
August 2024
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India.
Autonomous Vehicles (AV's) have achieved more popularity in vehicular technology in recent years. For the development of secure and safe driving, these AV's help to reduce the uncertainties such as crashes, heavy traffic, pedestrian behaviours, random objects, lane detection, different types of roads and their surrounding environments. In AV's, Lane Detection is one of the most important aspects which helps in lane holding guidance and lane departure warning.
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