<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?tc=NN%3AHbpkcn" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. National Academy of Sciences. All rights reserved.</copyright><docs>http://blogs.law.harvard.edu/tech/rss</docs><managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor><webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster><image><title>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Detection of Helmet Violations among Electric Bicycle Riders Through Multi-Network</title><link>http://pubsindex.trb.org/view/2479764</link><description><![CDATA[The use of helmets plays a crucial role in mitigating head injuries resulting from traffic accidents. However, manual supervision of helmet usage is both costly and prone to missing target objects. With the advancement of technology and widespread use of surveillance cameras, it has become technically feasible to automatically detect riders’ helmet wearing based on roadside videos. This capability holds significant potential in effectively guiding riders to wear helmets and enhancing driving safety. This paper presents a multi-network-based approach for automated detection of helmet wearing among electric bicycle riders. The object detection model is employed to detect electric bicycles and helmets. Additionally, the overall approach utilizes a human pose estimator to detect keypoints of human skeletons for selecting the region of interest (RoI) corresponding to the head region location. We propose location rules to extract the rider region within the electric bicycle box for distinguishing riders and locate the head region based on human skeleton keypoints for helmet wearing matching. The mean average precision (mAP) of the detection model trained on public datasets reaches 91.8%. To evaluate matching accuracy, we randomly selected 150 correctly detected images from the open-sourced two-wheeler helmet dataset (TWHD), achieving a matching accuracy of 95.6%. The detection model is trained and tested on public datasets which effectively facilitates the processes of data collection and annotation. Based on multi-network information fusion, we can accurately detect helmet violations among electric bicycle riders in multi-person interaction scenarios.]]></description><pubDate>Wed, 18 Dec 2024 13:21:30 GMT</pubDate><guid>http://pubsindex.trb.org/view/2479764</guid></item><item><title>Crash Risk of Cell Phone Use While Driving: Case-Crossover Study of SHRP 2 Naturalistic Driving Data</title><link>http://pubsindex.trb.org/view/1495640</link><description><![CDATA[The purpose of this study was to investigate the relationship between cell phone use and the risk of being involved in a motor vehicle crash while controlling for individual differences and situational factors that might influence crash risk. Data were from the SHRP 2 Naturalistic Driving Study, which recorded continuous video and kinematic data from a sample of 3,593 drivers for a period of several months between October 2010 and December 2013. The relationship between cell phone use while driving and the risk of crash involvement was quantified using a case-crossover study design in which a driver’s cell phone use in the 6 seconds immediately prior to a crash was compared to the same driver’s cell phone use in up to four instances driving under similar conditions within the three months prior to the crash. Odds ratios for crash involvement in relation to cell phone use were estimated using conditional logistic regression. Results were stratified by mode of cell phone use, crash severity, traffic density, crash type, and driver role in crash. The final study sample included 566 crashes and 1,749 matched baseline epochs. Visual-manual tasks overall and texting in particular were associated with significantly elevated crash risk relative to driving while not performing any observable secondary task; cell phone conversation in the absence of visual-manual interaction with the phone was not. Results confirm that visual-manual interaction with a cell phone while driving increases the risk of being involved in a crash.]]></description><pubDate>Thu, 22 Feb 2018 09:16:55 GMT</pubDate><guid>http://pubsindex.trb.org/view/1495640</guid></item><item><title>Comparison of Human Occupant Kinematics in Laboratory Impact and Run-Off-Road Crash Configurations</title><link>http://pubsindex.trb.org/view/1491484</link><description><![CDATA[An increasing number of passive safety requirements resulted in major improvements in vehicle safety. Advances in roadside hardware designs, road infrastructure, and crash avoidance systems have also improved road safety over the years. Despite these efforts, over 17,000 people are killed annually in roadway departure crashes in the United States. The first step in the effort to reduce fatalities and serious injuries in run-off-road crashes is to evaluate occupant kinematics and occupant interactions. Current laboratory crash tests evaluate near-side occupants only. Current roadside hardware tests and simulations often do not include analysis of occupant kinematics despite the fact that well-validated dummy and human occupant models exist. A mid-size sedan vehicle is equipped with human occupant models on the driver and passenger side and furnished with relevant restraint systems. Using this integrated occupant-vehicle model, occupant kinematics and interactions are first evaluated for laboratory side impact crash configurations. The same model is then exercised in a roadside hardware configuration, where the vehicle hits a New Jersey barrier at a 25 degree angle. This paper describes the methodologies used to conduct the integrated occupant-vehicle simulations. It provides insight into two areas that are not the main focus of today’s vehicle and roadside hardware developments: 1) the evaluation of both near-side and far-side occupant, and 2) occupant kinematics and interactions during run-off-road accidents compared to laboratory impacts.]]></description><pubDate>Thu, 14 Dec 2017 08:48:15 GMT</pubDate><guid>http://pubsindex.trb.org/view/1491484</guid></item><item><title>Analysis of Crash Severity Based on Vehicle Damage and Occupant Injuries</title><link>http://pubsindex.trb.org/view/1241311</link><description><![CDATA[In recent years, the reduction of injury crashes has been heralded as a great success. Improvements in federally mandated safety standards and advancements made by automotive industries to enhance vehicle safety can be partially credited with the decline. Now the national strategy on highway safety is to move toward zero deaths. From this vision zero perspective, one of the appropriate strategies is to manage kinetic energy in crashes and collisions—that is, to minimize the energy transferred to the human body—because the kinetic energy is responsible for occupant injuries and fatalities. Vehicle damage conditions are an unbiased indicator of kinetic energy in collisions, and injury severity is the ultimate measure of occupant risk. In this study, vehicle damage and occupant injury models were developed for single-vehicle and multiple-vehicle crashes. The results of these models provide a complete view of crash severity determinants and how they affect occupant injuries and vehicle damage. Some factors have a consistent impact across both injury severity and vehicle damage; others are contradictory. Combining information from both occupants and vehicles is valuable for an impartial evaluation of specific components in highway design; this combining also provides an accurate assessment of the impacts of occupant characteristics, driver behavior, and error on the resulting bodily injuries.]]></description><pubDate>Thu, 28 Mar 2013 09:02:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/1241311</guid></item><item><title>Injury Mechanism Analysis of Occupants in Road Crashes</title><link>http://pubsindex.trb.org/view/847815</link><description><![CDATA[Road accident is considered as a critical worldwide problem having its ranked position in terms of Disability Adjusted Life Years (DALYs) going upward in near future. The statistics of fatalities and permanently disabled injuries with huge accident associated costs over the past decades undoubtedly represent Thailand to be inflicted with a burning concern of major cause of deaths from road crashes among the South East Asian region. More importantly, the economically active segment of population becomes the direct victims due to such curse. The estimated amount of about US$2500 million per year due to road crashes significantly affects the Thai economy representing about 3.4 percent of GNP. Such an important issue draining the national economy should be sincerely addressed by the concerned authorities involved in road safety. In-depth study on occupants injury though crash investigation and reconstruction has yet been practiced in Thailand. This paper addresses this well researched study of conducting in-depth analysis focusing on injury mechanism in road crashes. The research study attempts to understand the injury causation of the occupants from the crash dynamics by illustrating an investigated case. The events from pre-crash to post-crash phases clearly demonstrate the sequences of injury pattern of different body regions through the contacts with interior of vehicle and movements of body parts due to inertia of motion.  This study also highlights the simulated scenario from two analysis software (PC-Crash and MADYMO) to describe the complete situation in a rear-end collision with estimated speed, force and acceleration that influenced injury sequences of occupants inside a pickup.]]></description><pubDate>Tue, 03 Jun 2008 07:32:11 GMT</pubDate><guid>http://pubsindex.trb.org/view/847815</guid></item></channel></rss>