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    <copyright>Copyright © 2026. 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>
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      <title>Research in Progress (RIP)</title>
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      <title>Preventing Rear and Side Crashes of Heavy-Duty Tractor Trailer Combinations with Smart Sensors and Vision Systems</title>
      <link>https://rip.trb.org/View/2440025</link>
      <description><![CDATA[The proposed project aims to prevent fatal rear and side crashes related to heavy-duty tractor-trailer combinations. Specifically, the research team proposes to develop and test smart trailer sensors/vision systems that infer "dynamic safety zones” and use lighting signals (or other communication modes) to alarm following and overtaking vehicles, pedestrians, or other non-occupant situations. The proposed trailer sensors/vision systems automatically analyze videos, vehicle size, and loading and brake data to infer collision risks between tractor-trailer combinations and approaching vehicles and people. From 2019 to 2021, fatal rear crashes with large trucks with trailers, where passenger vehicles travel under the rear of the truck, increased from 16.8% to 18.0%. In 2021, other vehicles in the large truck lane (26.5%) and others encroaching into the large truck lane (36.0%) were the two critical pre-crash events that caused such crashes. Drivers usually underestimate the required distance when the safe distance suddenly increases because of the large weights and sizes of the vehicles, unexpected pavement conditions, and terrains that require extra separations between vehicles. Inter-vehicle dynamic safety zones change and differ by situations and changes over time, so manually estimating the safe following and overtaking distances could be unreliable. Sometimes, illusions, slipperiness caused by weather, and poor lighting conditions can bias human estimates and make the reaction too late to stop. The recent integration of computer vision and motion sensors has shown the potential to improve passenger vehicles. However, heavy-duty vehicles, especially trailers, need special consideration of vehicle size, motion planning, road conditions, and occlusions to ensure a reliable assessment of side and rear collision risks in different positions of the tractors and trailers.
The proposed project will integrate the expertise of the project team and two industry partners in developing and testing an intelligent tractor-trailer sensor and vision system and provide benchmark datasets. In construction and airport safety, the project team has integrated computer vision, robotic motion simulation, and spatiotemporal analyses to implement dynamic safety zone estimation solutions for aircraft and construction equipment. The project team has also developed the technique to find safe actions when there is uncertainty in the dynamic system models or environments. The proposed project will adapt these intelligent dynamic safety zone estimation solutions to implement the proposed smart sensors and vision systems on tractor-trailer combinations. An industry collaborator, Clarience Technologies, will work with the project team to use their tractor and trailer fleet to collect video, vehicle, and telematics data to support the development and testing of the proposed smart safety system. Clarience will also leverage its automotive and vehicular engineering background to support the 4D simulation and motion analysis of heavy-duty vehicles in given road and terrain conditions. Another industry partner, Safety Emissions Solutions, has collaborated with the team in integrating inspection reports, crash, and telematic data into ‘vehicle deterioration models’ that predict the crash risks of heavy-duty vehicles. Integrating this expertise, software, data, and hardware from the researchers and industry will ensure the timely delivery of the proposed dynamic safety zone estimation solution and the benchmark data sets. ]]></description>
      <pubDate>Sun, 13 Oct 2024 09:43:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440025</guid>
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      <title>Safe and efficient automated freeway traffic control</title>
      <link>https://rip.trb.org/View/2292644</link>
      <description><![CDATA[Shockwaves are a naturally emerging phenomena in freeway traffic, but they represent one of the largest safety risks on freeways. Freeway drivers do not expect to encounter abrupt drops in speed or stopped traffic, as a result, shockwaves sharply increase the accident rates, particularly in the context of rear end collisions. For example, US interstate highways in 2021 saw the following rear-end collision numbers: Fatality 985, Injury-Only 71,408, Property-Damage-Only 152,011. Rear end collision severity is directly related to the relative speed between the involved vehicles, shockwaves increase these relative speeds, and thus, they also increase accident severity. Shockwaves also reduce freeway capacity and have a detrimental impact on fuel consumption and emissions because accelerating engines are less efficient than when cruising.  Connected and autonomous vehicles (CAV) hold the promise to attenuate and eliminate shockwaves (and thus, also reduce the severity and number of accidents), but only if the system is explicitly designed to do so. The very factors that give rise shockwaves in human driven vehicles (HDV) will also do so in CAV. While CAV offer new ways to manage traffic dynamics, an automated freeway will still be subject to traffic dynamics. For example, even with perfectly driven CAV, trucks and cars behave differently and the ever-changing mix of different vehicle types will give rise to rapidly varying bottleneck capacity. The real challenge is designing the CAV system so that it ensures the safest possible operation, and then within those bounds, the greatest operational efficiency (maximizing capacity, minimizing delays, etc.).  This research will approach CAV traffic control by first establishing the desired macroscopic traffic states along a freeway corridor and will use a rolling horizon to continually update the desired states in response to perturbations in the macroscopic traffic stream. Under this macroscopic framework, the CAV will know what behavior they should take simply by knowing where they are in space relative to the set of desired states. The main objective of the macro to micro control scheme is that the system can efficiently anticipate and respond to disturbances over large distances. It is this macroscopic look-ahead that will allow the system to detect and attenuate shockwaves. Although communications bandwidth is not the focus of this work, the macro to micro control scheme also has the potential to greatly reduce the necessary communication bandwidth to control the freeway traffic.  For this initial project the focus will be on detecting and dissipating large shockwaves after they have formed and begun propagating. The research will include developing the macroscopic framework to anticipate, detect and respond to shockwaves; developing the means to convey the macro to micro control scheme for the CAV to realize the macroscopic traffic states; and finally demonstrating the methodology using microscopic vehicle trajectory data from real shockwaves as both the initial conditions and bounding constraints of how the system can respond. If successful, it is anticipated that future research will explore mitigating shockwaves at the source- accommodating the variable bottleneck capacity and other unstable traffic dynamics.]]></description>
      <pubDate>Mon, 20 Nov 2023 19:52:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292644</guid>
    </item>
    <item>
      <title>Global Human Body Models Consortium - Year 3 of 5 </title>
      <link>https://rip.trb.org/View/2050281</link>
      <description><![CDATA[FY '21 activities to focus on include: (1) validation of models in high-speed rear impact; (2) application of new female-specific experimental data in female model validation; and (3) morphing of models to represent younger, middle-aged and older occupants/pedestrians.]]></description>
      <pubDate>Tue, 25 Oct 2022 10:24:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2050281</guid>
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      <title>Moving the Bus Safely Back into Traffic, Phase II</title>
      <link>https://rip.trb.org/View/1324980</link>
      <description><![CDATA[The difficulty experienced by transit buses in moving back into traffic safely from bus pullout bays has become a serious problem due to potential hazards between buses merging from pullout bays and the surrounding traffic. Previous studies have determined the need to closely examine the engineering side of the Yield to Bus (YTB) program and to develop effective countermeasures to address the issue. Phase I of this project focused on the comprehensive literature review, field observation, and data collection of three aspects: bus signing and lighting configuration, road signs, and YTB regulations. The Phase I project has determined the best practices of signing and lighting configurations on the rear of buses and roadside signs to assist transit buses to safely re-enter the traffic stream, and identify the needs for further research to evaluate the recommended countermeasures from Phase I. Specifically, the major recommendations including further research from Phase I for on-bus devices are as follows: (1) conduct a comprehensive data collection for evaluating the latest on-bus YTB Light Emitting Diode (LED) flashing signs on the back of buses, (2) assess safety and operational benefits of on-bus LED flashing yield signs, and (3) develop recommendations and implementation of an effective public awareness program to increase public awareness of YTB laws. In 2004 a crash study was conducted to examine all reported bus crashes during the period of 1998 to 2002 on the State Highway System. The results of this report indicated that 47 percent of all bus crashes were rear end collisions. In an attempt to reduce these types of crashes Phase 1 of this research was proposed and accomplished. Phase I of this research project was completed in November, 2007, which presented a comprehensive overview of the existing signage, lighting configurations, and existing YTB laws that were used to help buses merge back into traffic from bus pull-out bays. General engineering recommendations were developed in these three areas based on the statewide bus operators survey, field observations, and preliminary crash data analysis. The specific countermeasures included the implementation of the latest YTB LED flashing signs on the back of buses and advanced road signs and/or beacons. It is necessary to evaluate recommended countermeasures on moving buses safely back into traffic, as well as to assess safety and operational benefits to transit buses and surrounding vehicles by implementing the recommended countermeasures.]]></description>
      <pubDate>Wed, 01 Oct 2014 02:16:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1324980</guid>
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