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    <title>Research in Progress (RIP)</title>
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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>
    <image>
      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Investigating the Role of Human Factors, Vehicle Safety Features, and Types of Crashes on Injury Severity in Kansas</title>
      <link>https://rip.trb.org/View/2652672</link>
      <description><![CDATA[The Safe System Approach emphasizes designing countermeasures with an in-depth understanding of the human factors associated with traffic crashes. At the same time, it is important to investigate the role of better safety metrics, including the Insurance Institute for Highway Safety (IIHS) crash-worthiness and the National Highway Traffic Safety Administration (NHTSA safety ratings, in preventing serious injury crashes. Since crash injury severity is affected by multiple factors, it is important to account for vehicle crash worthiness (as defined by the IIHS), human factors, and the environment (network and the detailed sequence of most-harmful events as well as the types of crashes: rear-end, side-swipe, head-on) within an integrated modeling framework. A comprehensive crash severity model integrated with ArcGIS StoryMap will enable the Bureau of Transportation Safety of the Kansas Department of Transportation to promote effective safety countermeasures and create behavioral and instructional safety campaigns for drivers of various vehicle models. The research aims to develop a crash severity model accounting for vehicle attributes (make, model, year) and crash attributes (collision types, sequence of harmful events) with the ten years of crash data from Kansas (2012 – 2023) using the state crash database (the data can be extended to the most recent year based on availability). The goals of the project are as follows: 1. Perform statistical analyses of the relationship between accident type and vehicle year, model, and manufacturer using ten-year crash data; 2. Investigate possible correlations between vehicle attributes (make, model, year) and crash severity (sensitivity and cluster analyses); 3. Compare the percentage of vehicle types registered in Kansas to the percentage of crashes by vehicle types (representation ratio); 4. Compare findings of the estimated injury severity model with crash-worthiness scores by the IIHS. Examine the performance of certain safety features that may have been available within the vehicle types. The option to leverage NHTSA ratings data for comparison purposes will also be explored.]]></description>
      <pubDate>Tue, 13 Jan 2026 16:16:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652672</guid>
    </item>
    <item>
      <title>From Analysis to Action: Investigating Crash Readiness and the Role of Vehicle Features in Kansas Fatal and Serious Injury Crashes</title>
      <link>https://rip.trb.org/View/2652249</link>
      <description><![CDATA[The U.S. Department of Transportation's National Roadway Safety Strategy (NRSS) and other safety programs aim to eliminate road fatalities and serious injuries. The NRSS uses a Safe System Approach (SSA), which is a holistic and comprehensive approach that provides a guiding framework to make places safer for people. The SSA emphasizes infrastructure, human behavior, safe vehicle and transportation oversight, and emergency response which encompasses Safer People, Safer Roads, Safer Vehicles, Safer Speeds and Post-Crash Care as the objectives of the Safe System Approach. Some of the SSA objectives, such as, Safer People have been expanded upon by the National Highway Traffic Safety Administration (NHTSA) Countermeasures That Work, while the Federal Highway Administration's Proven Safety Countermeasures have focused on Safer Roads. While driver behavior and road conditions are extensively investigated, one issue that is often overlooked is the role of the vehicle and its characteristics. Safer Vehicles are vehicles “designed and regulated to minimize the occurrence and severity of collisions using safety measures that incorporate the latest technology” (FHWA). Between 2019 and 2023, there were a total of 8,423 crashes on Kansas roads, with fatal (K) and serious injury (A) crashes accounting for 1,817 and 6,606, respectively. In terms of crash type, angle-side impact collisions resulted in 384 fatalities and 1,500 serious injuries, while head-on collisions resulted in 238 fatalities and 419 serious injuries, and rear-end collisions resulted in 110 fatalities and 638 serious injuries. The Insurance Institute for Highway Safety (IIHS) and the National Highway Traffic Safety Administration’s (NHTSA) New Car Assessment Program (NCAP) evaluate a vehicle’s crash readiness through a series of tests that assess its crashworthiness and crash avoidance capabilities. However, a thorough analysis is needed to identify potential correlations between fatalities or serious injuries, crash types, and vehicle types and their safety features in Kansas. More specifically, it is important to identify the vehicle features that appear to be less involved in fatal or serious injury crashes, and therefore, have the potential to reduce crash severity. Such analysis will allow the the Kansas Department of Transportation (KsDOT) to better allocate resources and to make informed decisions concerning infrastructure policies to support vehicle safety features, as well as target behavioral safety and educational campaigns for drivers who use different vehicle models.]]></description>
      <pubDate>Tue, 13 Jan 2026 15:11:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652249</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 57-13. Practices to Improve DOT Employee Vehicle Safety and Reduce Workplace Driving Incidents</title>
      <link>https://rip.trb.org/View/2630484</link>
      <description><![CDATA[State department of transportation (DOT) employees spend a significant amount of work time behind the wheel in a variety of light-duty and heavy-duty vehicles. According to the Occupational Safety and Health Administration (OSHA) and the National Highway Traffic Safety Administration (NHTSA), motor vehicle crashes cost employers $60 billion annually in medical care, legal expenses, property damage, and lost productivity. The Smith System Driver Improvement Institute notes that driving is the leading cause of workplace deaths and results in 1.95 million workdays being lost each year. It identifies distracted driving, speeding, and failure to wear a seat belt as common contributing factors to motor vehicle crashes and injuries.  

Specifically for state DOTs, a number of fatal incidents have occurred involving vehicles running off the road or backing up. Some state DOTs use defensive driving, seatbelt use incentives, driver agreements, or other driver improvement training to improve education, awareness, and skills, but issues remain. As state DOTs continue to explore opportunities to improve their agency’s safety performance, a synthesis that captures initiatives regarding employee vehicle safety can inform the national audience about this impactful area. 

OBJECTIVE: The objective of this synthesis is to document state DOT practices regarding employee driving and vehicle safety programs for state DOT employees. An important focus of this synthesis project will be on incidents attributed to actions or behaviors of state DOT employees. 

Information to be gathered includes (but is not limited to): DOT characteristics that may influence training or safe driving enforcement (permanent or temporary employees; union or non-union workforces; civil service or non-civil service; human resources in the DOT or a statewide agency; a centralized, semi-centralized, or decentralized DOT); Employee characteristics and classifications (new or experienced employees, contract employees, or supervisory capacity); State DOT efforts to support safe operations (e.g., telematics); Components of an employee driving training/and fitness program; Existence of employee driving behavior metrics; Employee engagement in driving behavior training; Incentives or disincentives used related to employee driving behaviors; Benefits and challenges of employee driving programs; Implementation strategies for employee driving programs; and Written policies and procedures for employee driving programs. 

Information will be gathered through a literature review, a survey of state DOTs, and follow-up interviews with selected DOTs for the development of case examples. Information gaps and suggestions for research to address those gaps will be identified.]]></description>
      <pubDate>Wed, 26 Nov 2025 17:42:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2630484</guid>
    </item>
    <item>
      <title>High-Fidelity Attack Modeling and Resilience Analysis of Autonomous Vehicle Software
Stack</title>
      <link>https://rip.trb.org/View/2529890</link>
      <description><![CDATA[This project aims to improve the resilience of autonomous vehicles (AVs) against physical attacks. The researchers propose developing a high-fidelity modeling environment and integrating it with a resilience analysis framework. This will allow for a deeper understanding of how robust AV systems are under various scenarios and environmental conditions and ultimately contribute to safer AV technology.
This project aligns with the U.S. Department of Transportation (US DOT)’s strategic goal of safety, specifically aiming to reduce fatalities and serious injuries on the roads. By focusing on the resilience of AVs to physical attacks, the research addresses a critical safety concern. The project's approach to modeling and analyzing vulnerabilities in AV systems has the potential to transform how AV safety is evaluated and improved, contributing to a safer and more reliable transportation system.]]></description>
      <pubDate>Thu, 27 Mar 2025 15:36:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2529890</guid>
    </item>
    <item>
      <title>Resilient Autonomous Vehicle Perception Under Adversarial Settings </title>
      <link>https://rip.trb.org/View/2529892</link>
      <description><![CDATA[This project, titled Resilient Autonomous Vehicle Perception under Adversarial Settings, addresses critical challenges in the safety and reliability of autonomous vehicles (AVs) operating in real-world environments. Modern AV systems depend heavily on deep learning-based perception modules for tasks such as object detection, automated lane centering, and traffic sign recognition. However, these systems remain vulnerable to adversarial attacks, such as environmental modifications designed to mislead AV sensors and compromise decision-making. This research aims to develop robust model-end defenses by employing adversarial training and integrating Vision-Language Models (VLMs), ensuring AV perception systems are resilient to both known (white-box) and unknown (black-box) adversarial scenarios. This transformative research aligns with the broader goal of ensuring safer and more secure transportation systems as AV adoption accelerates.
This project directly supports the U.S. Department of Transportation’s (US DOT) priorities by advancing the safety, security, and reliability of the transportation system. It aligns with the US DOT Office of Research Development and Technology (RD&T) Strategic Plan goals, particularly in enhancing resilience and security for autonomous vehicle technologies. The research engages in breakthrough, transformative approaches, such as:
Developing advanced adversarial training techniques to strengthen deep learning models used in AV perception systems;
Incorporating Vision-Language Models to provide a multimodal, context-aware understanding of the AV environment;
Addressing safety-critical issues that improve public confidence and regulatory compliance, fostering the widespread adoption of AV technologies;
This project emphasizes innovative methodologies to mitigate adversarial threats, ensuring the long-term safety and sustainability of AV deployment.]]></description>
      <pubDate>Thu, 27 Mar 2025 14:53:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2529892</guid>
    </item>
    <item>
      <title>Cyber resilience of connected and autonomous transportation systems (Phase I): State-of-the-art and research gaps</title>
      <link>https://rip.trb.org/View/2440030</link>
      <description><![CDATA[Two trends are transforming transportation systems. First, the increasing complexity of cyber-physical technological advances, such as connected and autonomous mobility, which seamlessly integrate computation, communication, sensing, and control, holds great promise for societal and economic benefits. Second, these tightly coupled cyber-physical interdependencies can be self-defeating as they can pose new exposures to accelerating disruptions in cyber space (i.e., cyber-attacks), raising concerns about the safety, security, and privacy of the transportation system users. In view of these two overlapping trends, bolstering the cyber-physical resilience of transportation systems is crucial.

At its core, achieving cyber-physical resilience entails addressing its two distinguishing characteristics: (1) double-edged cyber-physical couplings, and (2) non-stationary uncertainties of cyber disruptions. The double-edged cyber-physical couplings require explicit investigation of both the bright and dark sides of these couplings, as well as their interactions through multi-agent modeling. For instance, computation (e.g., machine learning) can open doors to both cyber-attack and cyber-defense in autonomous mobility systems, while communication (e.g., connected/networked vehicles and infrastructure) can propagate cyber disruptions despite increasing network redundancy. The second distinguishing feature of cyber-physical resilience is that cyber-physical disruptions lead to non-stationary uncertainties, where adversaries can adapt cyber-attacks to cyber-defense mechanisms over time. This renders the classical resilience methods ineffective as they largely rely on the past experience of similar disruptions to tackle future ones assuming the associated uncertainties are stationary.

Addressing the above two inherent features of cyber-physical resilience requires transcending the conventional and siloed literature on cyber-physical systems and resilience. Current research on cyber-physical systems focuses on leveraging the inner workings of cyber-physical couplings to enhance engineered systems. Yet the inverse problem of tackling external forces (disruptions) exploiting the same couplings to damage these systems is underexplored. To address, this proposed project aims to survey the current research on transportation cyber-physical resilience, find research gaps, and suggest directions for future research. Through comprehensive and systematic investigation of this research area of national priority, this proposed project will lay the foundation for a series of future projects by the PI on the cyber-physical resilience of connected and autonomous transportation systems.]]></description>
      <pubDate>Sun, 13 Oct 2024 10:52:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440030</guid>
    </item>
    <item>
      <title>Certification of Connected and Automated Vehicles for Vulnerable Road Users</title>
      <link>https://rip.trb.org/View/2440019</link>
      <description><![CDATA[The autonomous systems industry in the Pittsburgh region supports 14,900 jobs and $ 1.2 billion in total labor income. It is estimated that within five years, the industry’s total scale will reach $ 10 billion. The key powerhouse is the development of connected and autonomous vehicles (CAVs). Albeit this huge opportunity, one hurdle to this transformative change is the concern of safety. Between 2013 and 2020, 31 states and the District of Columbia enacted legislation related to autonomous vehicles. The impact of state action is starting to manifest through the attraction of efforts that test autonomous systems to regions across the country as companies continue to advance their platforms. Numerous advancements have been developed to mitigate safety risks. For example, simulation tools, closed test grounds, and open corridors have been deployed by companies and universities. 

A critical research topic in safety is the evaluation of safety for vulnerable road users (VRUs), such as wheelchair users, people with strollers, vision-impaired people, service-dog users, and e-scooter users. Failure to ensure those people’s safety may result in criticism and backlash from the public and also objection and pushback from regulators. The primary goal of this project is to address this gap by designing and implementing a systematic CAV evaluation certificate program, along with simulation and physical tools, for VRUs.

This objective presents two challenges: (1) the limited data availability; (2) the lack of mature hardware for testing. The research team plans to address these by leveraging two strengths. 
The first strength is their expertise in multi-fidelity Generative Artificial Intelligence (AI). To provide stringent assessment, the team will leverage their previous work on adversarial, knowledge-based, and data-driven scenario generation to create extensive critical scenarios that pose significant risks to VRUs. PI Zhao has experience in utilizing large language models (LLMs) in autonomous vehicle (AV) legal behavior monitoring. To ensure the coverage of scenarios required by regulations and policies, the team will use similar approaches to assist the scenario design. The team will also utilize their previous work in accelerated evaluation to boost efficiency. These approaches are intended to mitigate the first challenge. 

The second strength is the expertise in both the automotive and robots. The team possesses the expertise to design systems with both autonomous vehicles and VRUs operated by robots. The team will develop a platform that can carry balloon pedestrians/wheelchair users in different terrains and mimic e-scooter users with their wheeled and legged robots. This will offer the advantage of agility and efficiency for self-reconstruction in the event of collisions. Testing robots developed in this project could serve as initial products for a spin-off start-up. 

In the next five years, Pittsburgh will encounter increasing competition from regions with signature state and regional initiatives that support autonomy applications. To maintain its position, Pittsburgh must establish programs to reinforce its current innovation ecosystem and root emerging companies and talent in the region. The team believes this project will establish a unique strength in the CAV safety evaluation area and secure Pittsburgh’s leading role in the field of autonomy. ]]></description>
      <pubDate>Sun, 13 Oct 2024 08:48:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440019</guid>
    </item>
    <item>
      <title>Safety Assurance and Demonstration of Connected Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/2440010</link>
      <description><![CDATA[This proposed larger-scale effort aims to re-define and demonstrate the vision of full autonomy to one of safe autonomy, where a learning-enabled
system is coupled with the foundations of cyber-physical systems to endow the system with an explicit awareness of both its capabilities and limitations. In turn, the system realizes when it is in or near a zone where its safety cannot be assured, and thereby transitions to a safe fallback state. A multi-pronged approach is adopted to achieve safe autonomy: (a) creating contextual awareness of the operating conditions to modify learning- and logic-based behaviors to reflect the operational context; (b) determining the location and orientation of the AV in absolute and relative coordinate frames to serve the needs of different tasks reliably and scalably; (c) defining and enforcing both static and dynamic guards for safe real-time actuation; (d) developing a powerful co-simulation framework to safely and efficiently test system performance under a range of clear and adverse operating conditions; and (e) validating and demonstrating the methodology on Carnegie Mellon University's (CMU’s) Cadillac CT6 autonomous vehicle.   The effort will also showcase physical demonstrations of vehicle capabilities to researchers and visiting dignitaries.

Recent advances in machine learning (ML) have been significant, and the application potential for ML seems limitless. However, using ML in its current form inevitably generates a non-zero amount of false positives and negatives, which in a safety-critical system can potentially be disastrous, causing damage to life and/or property. At the same time, the judicious use mathematical foundations, scientific principles and engineering ingenuity has led to the creation of large-scale but practical safety-critical systems such as aviation, nuclear power plants, electric grids and medical devices. In this effort, the research team builds on the conjecture that learning-enabled systems must necessarily be guided and fenced by logical, explainable and analyzable safeguards. Specifically, the team proposes to apply their methodology to the domain of connected and autonomous vehicles which must address a very long tail of known and unknown scenarios.]]></description>
      <pubDate>Sat, 12 Oct 2024 11:55:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440010</guid>
    </item>
    <item>
      <title>Transforming Transportation Policy and Planning for Safety
</title>
      <link>https://rip.trb.org/View/2440008</link>
      <description><![CDATA[Transportation policy studies and improved planning are essential for furthering goals of the University Transportation Centers and the US Department of Transportation.  This project is intended to build upon long-standing and successful activities in these areas.  Three tasks are envisioned.  

First, the research team will produce a policy brief on safety and ownership characteristics of battery electric vehicles (BEV).  The safety concerns will build upon the previous year’s research on BEV safety with respect to fires, vehicle weight and stopping distance. The ownership characteristics, focusing on equity issues, will come from the National Household Transportation Survey (NHTS, 2023).  The latest NHTS is for 2022 (released in 2023) so is recent enough to have a sample of BEV and includes extensive demographic data such as household income, numbers of vehicles and race.    PennDOT and Duquesne Light Company are heavily involved with charger implementations and will serve as deployment partners.

Second, the team will initiate analysis of fatality risks for vulnerable road users using the Fatality Analysis Reporting System (FARS 2023).  Data is released annually with considerable detail on crash characteristics and environment.  As an example of risks, a disproportionate number of pedestrian fatalities occur at night and this is a national issue, as recently described in a NY Times article, Why Are So Many American Pedestrians Dying at Night?  These risks will then be compared with automated and connected vehicle capabilities to identify potential risk reductions from these new technologies.  A recent CMU policy brief produced in part by the project team summarizes these capabilities (Martelero 2022).  This task is focused upon developing a professional paper that could form the basis of a policy brief.

Third, project participants will continue to work with Regional Industrial Development Corporation (RIDC) in the planning for Pennsylvania Safety Transportation and Research Track (PennSTART), a safety, training and research facility for autonomous vehicle testing and emergency responders.  The results of both tasks 1 and 2 can help inform appropriate test scenarios for Penn Start.



]]></description>
      <pubDate>Sat, 12 Oct 2024 11:30:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440008</guid>
    </item>
    <item>
      <title>Aftermarket Electronic Device Security for Heavy Vehicles</title>
      <link>https://rip.trb.org/View/2431172</link>
      <description><![CDATA[The security posture of aftermarket electronic devices connected to heavy vehicles is important to know, but hard to assess. Risk based approaches that consider assets, attacks, impacts, and feasibility can assist departments of transportation (DOTs) in understanding the risk profile of a cybersecurity attack against their vehicles and transportation systems, like the Automated Truck Mounted Attenuator (ATMA) and in-cab messaging. The project first endeavors to determine a representative inventory of devices connected to DOT operated vehicles. A subset of these devices will undergo penetration testing to determine the ease any discovered security vulnerabilities can be exploited. The final phase is to communicate these results in the form of a threat analysis and risk assessment to the stakeholders. ]]></description>
      <pubDate>Mon, 16 Sep 2024 09:25:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431172</guid>
    </item>
    <item>
      <title>Lessons from High-speed Level-5 AV Racing</title>
      <link>https://rip.trb.org/View/2425223</link>
      <description><![CDATA[The research is using Purdue’s Dallara, a Level 5 autonomous vehicle (AV) worth $1M, to identify lessons from the racing competitions that can be applied to facilitate safe AV operations at high-speed road corridors such as freeways. These lessons will be drawn from areas including issues related to sim-to-real transitions of the AV control, cyber-infrastructure design, and policy, fine-tuning of control algorithms for vehicle stabilization, and design of optimal trajectories at banked sections. The research is using literature reviews, surveys, simulations and theoretical analysis, AV runs at racetracks and road courses, and empirical analysis of data from AV runs at Putnam Park and Lucas Oil Raceway in Indy.]]></description>
      <pubDate>Thu, 05 Sep 2024 11:09:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2425223</guid>
    </item>
    <item>
      <title>Addressing Safety and Security Challenges in ML-based AV Software Stack - Remote Operation Support and Balancing Trade-offs</title>
      <link>https://rip.trb.org/View/2425174</link>
      <description><![CDATA[In this work, the research team proposes a framework that enables safe and secure human remote operation when AV systems require support due to the inherent limitations of ML models used. The team develops an approach that can effectively detect and mitigate potentially malicious remote human operators, and satisfy the real-time requirements of remote operation despite possibly variable network conditions impacting the communication channel between the AV system and the remote operator. The solution will be demonstrated on the Mcity 2.0 testbed as a means to validate the proposed design in realistic settings.
]]></description>
      <pubDate>Wed, 04 Sep 2024 17:18:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2425174</guid>
    </item>
    <item>
      <title>Electric Vehicle Fire Mitigation and Response Toolkit



</title>
      <link>https://rip.trb.org/View/2381748</link>
      <description><![CDATA[Due to lithium-ion battery fires, electric vehicles (EVs) present unique challenges in traffic incident management (TIM) and emergency management (EM). TIM is a planned and coordinated multidisciplinary process aimed at detecting, responding to, and clearing traffic incidents while restoring traffic flow as safely and quickly as possible. In TIM, many state departments of transportation (DOTs) have safety service patrols (SSPs) that offer services ranging from courtesy patrols, which provide simple motorist assistance, to more advanced services involving aggressive roadway clearance of disabled or wrecked vehicles. EM is a programmatic activity with a comprehensive approach to the full cycle of prevention, protection, mitigation, response, and recovery of all hazards (including natural and manmade disasters), accidental disruptions, and other emergencies. 

State DOTs encounter EV fires in both TIM and EM situations. During an SSP response, an EV fire may influence whether the vehicle is pushed, pulled, dragged, or driven. Safety concerns arise regarding the personal protective equipment used for responding to EV fires, as well as considerations for transporting or storing EVs after a fire. Additionally, the risks posed by lithium-ion battery fires affect state DOTs as they electrify their fleets and store the vehicles. EV fires can affect infrastructure (e.g., pavement and bridges), have environmental impacts, and sometimes require warning people nearby. EM situations can become cascading events that make responding to EV fires more difficult (e.g., EV fires during earthquakes, tornadoes, and hurricanes). Further, EV fires require different considerations depending on whether they occur in urban, rural, or remote areas.

Research is needed to support state DOTs as they manage lithium-ion battery EV fires during situations ranging from normal TIM to situations where hazards, accidental disruptions, and other emergencies are cascading events.

The objective of this research is to develop a toolkit that addresses the risks, opportunities, solutions, and costs associated with lithium-ion battery EV fires. The research shall consider the all-hazards approach to EM and its cycle.]]></description>
      <pubDate>Thu, 23 May 2024 10:09:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2381748</guid>
    </item>
    <item>
      <title>Use of Carbon Dots to Boost Energy Content of Biodiesel to Enable Next-Generation Hybrid Heavy Vehicles for Ground Transportation While Improving Safety</title>
      <link>https://rip.trb.org/View/2342031</link>
      <description><![CDATA[Increasing energy demands due to rapid industrialization and urbanization, stringent emission limits, and depleting sources of conventional fossil fuels urges the scientific community in search of renewable, reliable, cost-effective, and environmentally friendly alternative and sustainable options. In the transportation sector, this has translated as both electrification and increased adoption of biofuel. The electrification seems sufficient for light duty vehicles but for heavy duty vehicles, hybrid model will be the way forward during technology transition. Thus, biofuel, particularly biodiesel, has become a center of research initiatives as a replacement or a supplement to conventional petroleum-based fossil fuels [1-6]. Biodiesel was the second most produced and consumed biofuel in the United States in 2021 and accounted for about 11% and 12% of total U.S. biofuels production and consumption respectively [7]. Also, 1.64 billion gallons of biodiesel were produced in 2021 of which Soybean oil-based biodiesel contributes the most to this production (around 68%). Biodiesel can be blended and used in many different concentrations, including B100 (pure biodiesel), B20 (20% biodiesel, 80% petroleum diesel), B5 (5% biodiesel, 95% petroleum diesel), and B2 (2% biodiesel, 98% petroleum diesel). B20 is a common biodiesel blend in the United States.
     
Biodiesel advantages include low or no sulfur content, no aromatics content, high flash point, inherent lubricity, biodegradability, reduction of most regulated exhaust emissions, miscibility with petro-diesel in all blend ratios and compatibility with the existing fuel distribution infrastructure [4-6]. Technical challenges associated with biodiesel include reduction of NOx exhaust emissions, improvement in specific energy density and improvement of oxidative stability and cold flow properties. Achieving the same energy content as petro-diesel is a major challenge which will enable the widespread adoption of biodiesel for heavy duty diesel vehicles as biodiesel typically have ~10% lower energy content compared to their Petro-diesel counterpart. Carbon nanoparticles have emerged as a unique and potential addition to current fuel additives used in biodiesel and diesel fuels, resulting in lower emissions and improved engine performance [5-6]. Carbon nanoparticles offer unique features (such as greater surface area/volume ratio, higher combustion rate, increased energy density and so on) that make them ideal for various engineering purposes. In addition, nanometric materials may achieve the necessary chemical and thermal properties standard. Combining different nanoparticles with biodiesel provided evidence of enhancement in engine performance and reduce pollution. In addition, Carbon nanoparticles’ prospects as fire safety additives has been explored in the previous works [8-15]. However, the inclusion of Carbon nanoparticles is limited by their adverse effects on environment and health. Thus, biocompatible and bio-degradable carbon nanoparticles come into play for commercial use of nanoadditives for biodiesel. 
    
Hence, the focus of this project will be evaluating the biocompatible and bio-degradable carbon nanoparticles (e.g.: Carbon dots) as fuel additive for biodiesel particularly enhancing the energy content of biodiesel without compromising the positive benefits associated of using biodiesel. The work will also include technology transfer issues like testing, tuning, and validating the fuel additive mixture performance when combined with existing fuel additives, low and high temperature storage and operation, and other performance specifications as needed for commercial introduction.
]]></description>
      <pubDate>Mon, 19 Feb 2024 17:07:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2342031</guid>
    </item>
    <item>
      <title>Seamless Vehicle and Bridge Monitoring for Transportation and Infrastructure Safety
through a Wireless Internet-of-Things System – Phase I</title>
      <link>https://rip.trb.org/View/2341570</link>
      <description><![CDATA[The goal of this multi-phase project is to unite two traditionally separate vehicle and
bridge monitoring communities for a comprehensive evaluation of transportation and infrastructure safety. To achieve this goal, this project aims to (1) develop and validate a standalone, wireless Internet-of-Things (IoT) vehicle and bridge monitoring system for both collision and overstress detection, (2) deploy and calibrate the IoT system at a
highway bridge site with one type of representative trucks, (3) collect and store real-time traffic, meteorological, structural, and vehicle data, (4) cleanse and analyze heterogeneous data (numeric, image, audio, and video) through influence line analysis and machine learning for the extraction of features related to vehicle safety and infrastructure condition, and (5) develop and validate a visual mechanism to alert truck drivers as they drive underneath or across the highway bridge. The outcomes of this project are to mitigate collision-induced bridge damage, vehicle-related highway fatalities and injury rates through such an integrated vehicle and bridge monitoring in real time.

To address the first and second objectives, the scope of Phase I project includes, but is not limited to, (a) literature survey on bridge-weigh-in-motion (BWIM) and load tests, (b) development of a laboratory testbed of vehicle monitoring and BWIM system, and (c) scale-up of the laboratory testbed for field installation and validation.
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      <pubDate>Mon, 19 Feb 2024 16:28:06 GMT</pubDate>
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