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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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Drone-in-a-Box: Enhancing Public Safety and Environmental Monitoring on Cape Cod</title>
      <link>https://rip.trb.org/View/2775052</link>
      <description><![CDATA[Cape Cod, a popular tourist destination, faces unique challenges due to its geographic isolation, seasonal population fluctuations, and increasing environmental concerns. This research proposes leveraging drone-in-a-box (DiB) technology, an autonomous drone system with automated launch, landing, and charging capabilities, to address these challenges. DiB systems offer persistent aerial surveillance and rapid response capabilities, making them ideal for various applications in remote and dynamic environments.

The research team for this project includes representatives from 
Massachusetts Department of Transportation (MassDOT) Aeronautics, MassAutonomy, and Endicott College.  Endicott College will identify appropriate team members based on the expertise needed for each aspect of the project.

This project is significantly strengthened by the collaboration and support of the Wellfleet Police and Fire Departments, who will provide valuable expertise, operational insights, and access to critical resources.

The objectives of this project are to: (1) Evaluate the technical feasibility of deploying DiB systems in the Cape Cod environment, considering factors such as weather conditions, Federal Aviation Administration (FAA) regulations, and communication infrastructure.
(2) Assess the operational effectiveness of DiB systems in each use case, measuring improvements in traffic flow, emergency response times, and shark detection accuracy.
(3) Analyze the cost-benefit of DiB implementation compared to traditional methods. 
(4) Investigate public perception and acceptance of drone technology for public safety and environmental monitoring.
The project will yield data and associated reports detailing the effectiveness of the use of drone-in-a-box (DiB) technology, an autonomous drone system with automated launch, landing, and charging capabilities, to address these challenges.
]]></description>
      <pubDate>Fri, 04 Sep 2026 14:40:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775052</guid>
    </item>
    <item>
      <title> 
Research Experience for Undergraduates (REU): Smart Cities Supplement Summer 2027
</title>
      <link>https://rip.trb.org/View/2762047</link>
      <description><![CDATA[Since 2020, the University of Nevada, Las Vegas (UNLV) has hosted an NSF Research Experiences for Undergraduates (REU) Site on Smart Cities focused on advanced mobility technologies, including Intelligent Transportation Systems (ITS), Connected and Automated Vehicles (CAVs), and vehicle-to-everything (V2X) communication. The program attracts more than 100 applicants annually and has trained over 45 students, producing more than one peer-reviewed publication per summer. This project supplements the existing site to support two additional undergraduate researchers working on Research and Education for Promoting Safety University Transportation Center (REPS UTC) safety-related projects.
The project will recruit two undergraduate students from relevant disciplines (electrical and computer engineering, civil engineering, computer science), with emphasis on outreach to institutions with varied student populations. Selected students will complete a ten-week summer research experience under faculty mentorship across the ITS, CAV, and V2X focus areas, complemented by co-curricular training including weekly cohort meetings, enrichment activities, and cohort-building. Key tasks include developing compelling safety research projects with UNLV mentors, national advertising and recruitment, candidate evaluation and selection, mentored summer research, and final reporting through the UNLV Undergraduate Research Symposium and academic publication venues.
Expected outcomes include expanded undergraduate participation in transportation safety research, student projects aligned with REPS UTC initiatives, student reports, presentations, and potential peer-reviewed publications. The project strengthens the pipeline of future transportation professionals and UNLV's research infrastructure in traffic safety, helping develop the next generation of researchers and practitioners equipped to address emerging safety challenges in transportation systems.
]]></description>
      <pubDate>Wed, 19 Aug 2026 15:59:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762047</guid>
    </item>
    <item>
      <title>Exploring Role-Based Human-Centered AI Alerts with Novel Wearable Sensors and Multi-Sensory Cues</title>
      <link>https://rip.trb.org/View/2762040</link>
      <description><![CDATA[As vehicles transition between manual control and varying levels of automation in response to roadway, weather, traffic, and system conditions, drivers frequently lack a clear understanding of the vehicle's current capability boundaries, the actions expected of them, and the urgency of required responses. These gaps elevate risk during safety-critical control transitions. This project designs and empirically evaluates role-based, human-centered artificial intelligence (AI) safety alerts delivered through novel wearable sensors (a smart ring and a haptic glove) and in-cabin interfaces (seat vibration, windshield visuals, and odor cues where feasible) to improve alert meaning, driver comprehension, and driving performance.
The research proceeds in two phases using a driving simulator with conditional automation. Phase I employs repeated-measures, counterbalanced design to compare smart ring, glove-based, and seat-vibration cues during standardized, time-limited takeover events, including periods of driver distraction, with urgency manipulated through available lead time. Dependent measures include takeover and response timing, minimum time-to-collision, lane-keeping and speed stability, braking and steering profiles, control smoothness, and subjective ratings of workload, trust, clarity, and comfort. Phase II examines how AI role communication, information framing and tone (neutral, supportive, stern) affect driver understanding, workload, and trust calibration when delivered through multi-sensory interfaces. Mixed-effects models will account for repeated events within drivers.
The project will deliver implemented alert prototypes, empirical evidence on whether wearable sensors provide measurable safety benefits over traditional seat vibration, a validated approach for communicating AI support roles and tone, and design guidance for transparent, higher-meaning driver alerts. Results will support agencies, vehicle manufacturers, and suppliers in refining alert strategies that reduce confusion, improve response quality, and promote appropriate trust calibration during automated driving.
]]></description>
      <pubDate>Wed, 19 Aug 2026 11:47:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762040</guid>
    </item>
    <item>
      <title>LIFT: Localization-Informed Flight Trajectories for UAV Resilience against GPS Loss  </title>
      <link>https://rip.trb.org/View/2762044</link>
      <description><![CDATA[Autonomous vehicles—such as autonomous cars and drones—require highly accurate localization to operate safely, yet global positioning system (GPS) alone often cannot provide the needed precision. Furthermore, many real-world applications—such as disaster response—often require localization in GPS-denied environments. Cooperative localization offers a promising alternative to GPS: connected vehicles share information to localize more accurately than any one vehicle can. The accuracy of this localization depends on how they move and the sensing geometry they create. These challenges motivate new approaches that integrate localization, sensing, and planning, which aligns closely with priorities from national agencies—such as the U.S. Department of Transportation—that are exploring sensing-based PNT solutions (link).  The research team proposes Localization-Informed Flight Trajectory (LIFT) planning to enable drone and other autonomous vehicle the ability to achieve precise and reliable localization across diverse environments, from dense urban areas to remote rural regions. This capability will improve resilience against GPS loss, which is particularly a concern in high-latitude locations (such as northern Minnesota) during geomagnetic storms. LIFT leverages sensing-enabled localization, where cooperative autonomous vehicles estimate their position relative to mobile or stationary anchors using sensor measurements (e.g., range and line-of-sight) instead of relying on GPS. Such sensing-based localization is lightweight and power-efficient, but its performance is highly sensitive to the geometric configuration of the autonomous vehicles and the anchors. This vehicle–anchor geometry directly affects localization accuracy, which in turn affects trajectory planning. Conversely, the planned trajectories influence future sensing geometry, creating a closed feedback loop among motion, sensing, and localization. This project develops LIFT, a unified framework that jointly reasoning over localization, sensing, and planning to generate UAV trajectories that maintain high localization accuracy. The team will introduce localization-informed constraints, develop real-time trajectory optimization algorithms, and validate the proposed LIFT framework experimentally in realistic GPS-denied environments. ]]></description>
      <pubDate>Wed, 19 Aug 2026 11:26:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762044</guid>
    </item>
    <item>
      <title>Leveraging AI to Improve the Safety of Connected and Automated Vehicles	in Winter Weather	</title>
      <link>https://rip.trb.org/View/2762041</link>
      <description><![CDATA[Connected and automated vehicles (CAVs) are socially important because they have the potential to reduce accidents, expand access to transportation, and improve efficiency. However, broader adoption of CAVs in the Minnesota region is hampered by many challenges. First, winter weather snowfall corrupts sensor data as LiDAR returns become noisier, camera imagery loses contrast, and both modalities suffer from occlusions and missing data. These corruptions lower the reliability of perception modules and introduce uncertainty into all subsequent decision-making stages. Second, cold weather affects battery chemistry, reducing range and increasing charging time, which increases uncertainty for drivers and automated vehicles alike. For instance, during a cold snap in January 2024 in Chicago, charging stations saw long lines of electric CAVs [15], many with dead batteries, exacerbating the range anxiety hampering adoption in cold-weather states. This project investigates the potential of recent artificial intelligence (AI) breakthroughs, such as diffusion models and generative AI, to enhance winter safety and increase the adoption rate of CAVs. To improve safety, the project evaluates the ability of diffusion models, a recent AI advance, to reduce noise in winter-degraded LiDAR, camera, and other sensor data during snowfall. To spur adoption and support the sustainability of CAVs, the project examines the value of AI in improving models for estimating CAV energy use and emissions, extending the range of CAVs, and facilitating eco-routing to help choose routes that reduce energy use and emissions.  ]]></description>
      <pubDate>Wed, 19 Aug 2026 11:03:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762041</guid>
    </item>
    <item>
      <title>Embedding Human-Understandable Symbolic Rules into Vision-Language Decision Systems for Safe, Transparent, and Ethical Autonomous Driving </title>
      <link>https://rip.trb.org/View/2762038</link>
      <description><![CDATA[In this project, the research team aims to embed human-understandable physical and symbolic rules into the decision-making process of Vision-Language Models (VLMs) for autonomous driving, enabling the model to justify and articulate its decision-making process and consequent driving behavior in real-time. Conventional end-to-end autonomous driving models, i.e., models that learn a direct mapping from raw sensor input (camera, radar, LiDAR) to low-level control commands (steering, throttle, and braking), typically operate as highly complex black-box models. While such models have achieved strong empirical autonomous driving performance, their internal reasoning and decision-making processes are opaque, which makes it difficult for human users to interpret or build trust in the models. Moreover, safety constraints and traffic regulations are often incorporated into these models only as soft constraint penalties during training. As a result, there is no formal guarantee that the probability of generating an unsafe or illegal action is strictly zero at inference. As a result, without an additional safety verification layer, the model may still produce rare but high-risk behaviors, which also may be amplified under distributional shifts or adversarial conditions. ]]></description>
      <pubDate>Wed, 19 Aug 2026 10:42:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762038</guid>
    </item>
    <item>
      <title>DriveOhio Research on Call (ROC) FY2027-2029</title>
      <link>https://rip.trb.org/View/2761961</link>
      <description><![CDATA[The Ohio Department of Transportation (ODOT) is charged with the management and maintenance of Ohio's vast transportation system. ODOT strives to execute this charge in the most effective and efficient manner possible. At times, ODOT encounters situations where low-cost, short-term, focused research tasks are needed to address an urgent issue. While important and potentially impactful, these research tasks do not warrant the level of a full-scale research project. Due to the time-sensitive nature of these tasks, it is possible that some of these tasks go unmet because the standard contracting process requires more time than available.  To address this issue, ODOT developed the Research-On-Call (ROC) program. The ROC is designed to provide direct, quick access to researchers in specific areas of expertise to conduct short-term, focused, urgent research tasks. This ROC will focus on tasks from DriveOhio.                            ]]></description>
      <pubDate>Tue, 18 Aug 2026 08:31:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2761961</guid>
    </item>
    <item>
      <title>Urban Flow Autonomous Wheelchair Phase 4: Fleet Management System and Health Facility Deployment</title>
      <link>https://rip.trb.org/View/2742156</link>
      <description><![CDATA[Morgan State University’s autonomous wheelchair (AW) project has completed three phases of research and development, culminating in a high-profile public demonstration at Baltimore/Washington International Thurgood Marshall Airport (BWI) in 2025, growing from a single prototype to a fleet of three LiDAR- and camera-equipped wheelchairs operable via the UrbanFlow smartphone application.

Phase 4 advances the project on three fronts, collectively moving the technology from a research prototype toward real-world, scalable deployment. First, researchers will complete a Fleet Management and Operator Dashboard, replacing the existing one-to-one operator model with a centralized system capable of controlling the full wheelchair fleet. Second, the team will deploy and test the AW system in a new indoor environment, the Morgan State University Health and Human Services (HHS) Center. Third, it will develop a Digital Twin of the UrbanFlow deployment environment, enabling virtual simulation of wheelchair navigation scenarios to accelerate testing and support future multi-site planning.

Organized across five tasks, Phase 4 will deliver a Fleet Management and Operator Dashboard enabling one operator to simultaneously oversee, dispatch, and control the entire wheelchair fleet, with live location tracking, battery and status monitoring, automated low-battery alerts, remote override, centralized dispatch, fleet analytics, and fault and maintenance flagging. The dashboard will be validated through live multi-wheelchair sessions at BWI and on campus with iterative operator usability testing. The team will map, configure, and deploy the AW system within the HHS Center to establish a fully operational navigation environment at a new site type, and will develop a Digital Twin, a virtual replica of the physical deployment environment integrated with UrbanFlow, as a simulation and planning tool.]]></description>
      <pubDate>Sat, 01 Aug 2026 10:43:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742156</guid>
    </item>
    <item>
      <title>Evaluating Real-World Multi-View Vulnerabilities in Autonomous Driving Perception and Planning for Improved Safety and Regulation</title>
      <link>https://rip.trb.org/View/2742144</link>
      <description><![CDATA[Autonomous driving systems (ADS) increasingly rely on vision-based perception and learning-based decision modules, yet real-world safety depends on whether these systems remain stable as the ego vehicle continuously changes distance and viewpoint relative to roadway objects. Current evaluation practices often emphasize single images or limited viewpoints, which can mask trajectory-dependent failure modes that emerge during real driving under changing illumination, partial occlusion, and motion blur.

This project studies the limitations of existing ADS through a measurement-driven, multi-view robustness evaluation framework designed to produce actionable engineering insights and evidence-based inputs for transportation safety policy. Building on a differentiable, view-consistent scene representation (3D Gaussian Splatting with view-dependent appearance modeling), the team will generate controlled, physically plausible appearance variations across realistic approach trajectories and use them as a diagnostic tool to quantify perception instability and downstream planning sensitivity.

The project will deliver a reproducible set of safety-relevant scenarios and ego-vehicle approach trajectories representing how a vehicle observes the same object over time; a controllable multi-view rendering and perturbation engine built on 3D Gaussian Splatting that synthesizes viewpoint-consistent observations under bounded, physically plausible appearance variations; and a multi-view robustness evaluation methodology and benchmark protocol using trajectory-based sampling. It will produce quantitative robustness indicators summarizing perception stability and planning sensitivity, a structured taxonomy of observed failure modes, and a reproducible reporting package of metrics definitions, evaluation scripts, and documentation templates. Validation will be performed on representative research ADS models, with black-box evaluation of commercial systems where feasible and safe.]]></description>
      <pubDate>Sat, 01 Aug 2026 10:27:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742144</guid>
    </item>
    <item>
      <title>Assessing Benefits of Mixed Traffic Platooning with Multi-Agent Reinforcement Learning and Cooperative Adaptive Cruise Control with Unconnected Vehicles </title>
      <link>https://rip.trb.org/View/2739300</link>
      <description><![CDATA[This project assesses the performance benefits of implementing multi-agent reinforcement learning (MARL)-based cooperative platooning and cooperative adaptive cruise control with unconnected vehicles (CACCu) in mixed traffic environments, comparing scenarios with and without these technologies under various connected automated vehicle (CAV) market penetrations. The main goal is to investigate when a policy to deploy these advanced technologies makes sense.

Cooperative platooning in mixed traffic, where CAVs must interact safely and efficiently with human-driven vehicles, remains a key barrier to realizing the full mobility, safety, and energy benefits of connected automation, a challenge amplified by uncertainty in human driving behavior. When a CAV’s immediate preceding vehicle is not connected, it may benefit from a lane change to follow a connected vehicle and form cooperative adaptive cruise control; the team’s MARL approach, built on a CNN QMIX architecture supporting centralized training with decentralized execution, learns coordination policies that adapt to surrounding vehicles rather than relying on fixed rules.

]]></description>
      <pubDate>Thu, 30 Jul 2026 16:05:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2739300</guid>
    </item>
    <item>
      <title>Automated Vehicle (AV) Pooled Fund Study (PFS) Phase 2</title>
      <link>https://rip.trb.org/View/2734822</link>
      <description><![CDATA[Automated Vehicles (AVs) are an emerging technology that will dramatically change the transportation system, potentially reducing crashes, expanding access, and promoting sustainability, safety, and efficiency. With over 41 states actively advancing AV testing, research, policy, and planning, state departments of transportation face challenges understanding what strategies and actions are needed to prepare for this evolution in emerging technology. While many states are advancing strategic plans and roadmaps, strategies vary and ideas of what it means to be implementation ready differ. The original Automated Vehicle (AV) Pooled Fund Study (PFS) [TPF-5(453)] started in 2020 to collaboratively coordinate funding, strategies and research to help State DOTs understand their role in this changing environment, and how to proactively prepare. The objectives of this new pooled fund study, AV PFS Phase 2, are to continue and expand on the achievements of TPF-5(453). The key goals of the AV PFS Phase 2 are to conduct research that supports the following items: (1) Infrastructure readiness: What infrastructure investments do DOTs need to be making to plan and prepare for AVs; (2) Operations: How do AVs impact operations, traffic safety and maintenance; (3) Policy frameworks: What laws and policies do states need to enact to develop clear, more uniform policy across regions to avoid a patchwork approach; (4) Interstate freight and multi-modal harmonization – How can DOTs advance freight harmonization and interstate networks to support autonomous delivery; (5) Workforce development: How do we skill the current and future workforce for new technologies, processes and impacts; (6) Communications and engagement: How do we message this work to internal and external stakeholders and engage partners, industry and communities; (7) Partnerships: How can infrastructure owner-operators (IOOs) partner with industry, researchers, communities and non-profits to plan for this unknown future; (8) Strategic investment: How can IOOs make strategic investments with limited resources that plan for emerging technology and AV innovation; (9) Planning: How should DOTs plan for AV technology in the short and long-term horizons How can DOTs follow trends and develop shared future scenarios; (10) Industry forum: How can we develop a collaborative forum where states together can meet with AV industry to share ideas and learn about industry goals similar to the CAT Coalition’s IOO-OEM forum.]]></description>
      <pubDate>Thu, 23 Jul 2026 14:52:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2734822</guid>
    </item>
    <item>
      <title>Potential Impact of Autonomous Vehicles on Reducing Congestion - Phase 2</title>
      <link>https://rip.trb.org/View/2733194</link>
      <description><![CDATA[Traffic congestion is a major problem in large metropolitan areas in the United States. In 2022, on average, a commuter lost about $1,259 in monetary terms annually due to congestion nationwide, which amounts to 8.7 billion lost hours in total.  The lack of coordination among individual users, who make routing decisions independently based on current traffic information without anticipating that others may follow similar decision-making patterns, contributes significantly to the high cost of congestion.    

The behavior of drivers optimizing their individual routes leads to a state known as the User Equilibrium, leading to travel times that can be significantly higher than travel times from the System Optimal, particularly in congested urban networks where the effects of individual decisions cascade throughout the system.  With the future emergence of autonomous vehicles, it is possible that organizations may now own more of the fleet of vehicles and control their routing, providing the organization more options for balancing route selections and thus making it possible to find routing solutions closer to the system optimal.  Driverless ride-hailing companies such as Waymo have already begun their service in five major cities across the United States and Tesla has started to test their Robotaxi service in Austin, Texas.

In Phase 1, the research team developed the research foundation for this problem. This work includes the literature review and the development of an online dispatch-and-relocation framework for a centrally controlled autonomous vehicle fleet. The Phase 1 framework matches requests to vehicles while accounting for pickup deadlines, near-term vehicle availability, and proactive repositioning toward forecasted demand. Phase 1 also establishes a comparison structure against a traditional human-driver ride-hailing system and an initial simulation capability that traces routes and estimates vehicle miles traveled, deadhead miles, passenger waiting time, revenue, and related performance measures.

Phase 2 will build directly on this foundation and is the primary focus of the next stage of the project. In Phase 2, the team will scale the optimization and simulation framework so it can solve problems at the size of major metropolitan areas. This includes extending the model to larger networks and richer demand patterns, improving computational tractability for larger instances, and strengthening the simulation module so it can evaluate passenger-vehicle matches and route decisions under more realistic operating conditions. To make the model scalable, the team will aggregate the service region into zones and solve the resulting problems repeatedly over short rolling horizons. The team will also need to calibrate the demand forecasting and routing inputs for large urban networks and test the algorithms on progressively larger instances to ensure that the solution quality and computation time remain practical. The purpose of Phase 2 is to determine how much centralized control of autonomous fleets can reduce system-wide travel, deadhead mileage, waiting times, and congestion when evaluated on realistic metropolitan-scale settings.

]]></description>
      <pubDate>Wed, 22 Jul 2026 17:37:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2733194</guid>
    </item>
    <item>
      <title>Routing Autonomous Trucks on Dedicate Lanes- Phase 2</title>
      <link>https://rip.trb.org/View/2733038</link>
      <description><![CDATA[Trucks are known to have a significant impact on congestion during traffic peak hours due to their size and slower dynamics. Human operated trucks for freight transport are faced with two constraints: those imposed by the service demand and those imposed by the human driver. For long haul operations, for example, truck drivers must meet the constraints of hours of service. For short haul they must meet family and personal constraints which often do not allow them to operate during odd hours. With automation the human constraints are removed which opens the way to view truck routing and scheduling under different and more flexible constraints. The major problem faced by automated trucks operating with the rest of traffic, however, is safety as due to the different sizes involved the sensing problem is more challenging and potential accidents can be catastrophic. Moving trucks from times of high congestion to times of no congestion will bring considerable benefits to trucking companies as well as to all other users of the road network, as fewer trucks will be operating during peak traffic hours. In addition, trucking companies that are short of truck drivers will be able to operate without disruptions and without human imposed constraints, saving on labor costs.

During the first phase of the project, the research team developed microscopic traffic simulation model which the team validated using real data from I-710. The network considered was part of I-710 and the team assumed as a first step single origin-destination (OD) flows. The team considered the scenario where trucks sharing the same road network as passenger cars become automated and operate on dedicated truck lanes at times that the traffic demand is very low, so that lanes can be switched dynamically to dedicated automated truck lanes without affecting traffic. By doing so we can keep the automated trucks separated from manually driven vehicles, thereby addressing the issue of safety.

The ongoing phase 1 study shows that by removing a number of trucks which are about 0.4% of all vehicles during a high peak traffic and have them automated and operating on dynamically dedicated lanes during off peak traffic the travel time for trucks is reduced by 4.5% while the travel time of passenger vehicles during the high peak traffic decreases by about 3%. These preliminary findings suggest that temporal rescheduling of freight demand, combined with dynamic lane management, could improve both freight and overall network performance. In phase 1 the team simply used the traffic simulator to test their ad hoc approach of moving trucks from high peak to low peak traffic without any form of optimization.

In phase 2 the team plans to extend the approach as follows: (1) The team will expand the road network to include some of the most popular truck routes covering short medium and long-haul scenarios. The issue of parking and refueling in the absence of driver will also be addressed. (2) The team will extend the results of phase 1 to multiple interacting OD pairs, allowing the framework to capture more realistic freight demand patterns and network-level coordination effects. (3) The team considers the case of truck platoons which will include fully automated truck platoons but also the more realistic case where the first truck in the platoon has a human driver. In other words, the lead truck will be driven by a human driving and following trucks will be electronically connected and fully automated. Truck platooning is an attractive concept as it has shown to have the potential of reducing aerodynamic drag and contribute to significant fuel savings. (4) The team plans to optimize their decisions of temporal rescheduling of freight demand, combined with dynamic lane management to achieve the best possible outcome. The team views the problem as assigning loads in 2 dimensions temporal and spatial in a way that reduces travel time and lowers fuel cost for both trucks and passenger vehicles.]]></description>
      <pubDate>Wed, 22 Jul 2026 17:28:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2733038</guid>
    </item>
    <item>
      <title>Connected and Automated Vehicle (CAV) Readiness Survey: Are MDOT Roads Machine Readable</title>
      <link>https://rip.trb.org/View/2731918</link>
      <description><![CDATA[Considering Michigan Department of Transportation (MDOT) Mission, Values, and Vision, with the evolving landscape of technologies within the connected and automated
vehicle (CAV) industry, there is a pressing need to investigate the requisite support from DOTs to enable seamless integration of
CAVs with infrastructure. As core sensors and systems defining these technologies become more established, understanding the
precise infrastructure requirements becomes paramount. Therefore, the research aims to address the question: "What specific
support and infrastructure enhancements are necessary from MDOT to facilitate effective detection and interaction of connected and
automated vehicles with the surrounding infrastructure?]]></description>
      <pubDate>Fri, 17 Jul 2026 11:47:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731918</guid>
    </item>
    <item>
      <title>Evaluating the Effectiveness of Drivers' Education Modules on Safety </title>
      <link>https://rip.trb.org/View/2714400</link>
      <description><![CDATA[There is mixed evidence as to the effectiveness of drivers’ education courses. It is also unknown whether drivers’ education can be used to train drivers on how to effectively use driving automation. The objective of this research is to examine: How differences in drivers' education program delivery affects novice drivers' behavior, crashes, and citations within 12 months of licensure; How differences in novice drivers' pre-license behaviors affect crashes and citations within 12 months of licensure; How driver education and training programs can help improve novice drivers' use and understanding of advance driver assistance systems (ADAS).]]></description>
      <pubDate>Mon, 15 Jun 2026 15:59:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2714400</guid>
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