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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>STEER AV - Safety Tuned Emulation of Emerging Responses for Autonomous Vehicles
</title>
      <link>https://rip.trb.org/View/2640184</link>
      <description><![CDATA[Autonomous vehicles (AVs) produced by different manufacturers often display distinct driving styles because each system uses its own proprietary decision rules. These variations can affect safety and traffic flow during the long transition period when automated and human driven vehicles operate together. This project will study real world AV trajectory data to assess how AVs follow other vehicles, how they balance safety and efficiency, and which factors shape their decision making. The research team will use inverse reinforcement learning and interpretable generative methods to infer the policies that guide AV actions and to create models that reproduce these behaviors.

After the initial models are created, the project will incorporate additional constraints that guide the system toward safer behavior while preserving mobility. Simulation experiments will examine how these modified policies perform under a range of conditions and will evaluate possible trade offs between safety and efficiency. The resulting framework will support efforts to standardize and improve AV driving policies, help researchers understand AV decision patterns, and assist agencies and manufacturers as they prepare for increasing levels of automated travel.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:33:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640184</guid>
    </item>
    <item>
      <title>Analysis on Traffic Safety and Mobility for Tribal Communities under Severe Weather Conditions</title>
      <link>https://rip.trb.org/View/2343910</link>
      <description><![CDATA[Under the conditions extreme weather, potential power outage, loss of wireless signal coverage, and difficulty in accessibility may cause malfunction to the traffic signals and remain unaccounted for. The safety and mobility management for tribal communities under severe weather conditions is an under-resourced area. This research will provide scenario comparison based on scenario simulation on different alternatives for the case study region with selection and development of car following models. Accordingly, suggestions will be to tribal area traffic management authorities that can provide a safer and more mobile tribal community during the extreme weather conditions. This research can also serve as pioneering research for further traffic analysis in tribal areas.]]></description>
      <pubDate>Thu, 22 Feb 2024 16:17:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343910</guid>
    </item>
    <item>
      <title>Investigation of Driver Adaptations in a Mixed Traffic Environment</title>
      <link>https://rip.trb.org/View/2341499</link>
      <description><![CDATA[Existing mathematical models for car-following are mostly descriptive and do not inherently estimate behavioral responses due to different traffic conditions, such as changes in roadway, environment, or vehicle conditions. These models include behavioral parameters (e.g., reaction time, degree of aggressiveness, etc.), which are calibrated with aggregate data collected under various traffic conditions. However, these models fail to capture changes in driver behavior caused by changes in the driving environment and thus fail to address vehicle interactions and the mechanisms that lead to breakdown phenomena.

This issue becomes more apparent with the emergence of vehicle automation and advanced vehicle technologies as these directly impact the driving task. Through automation, drivers do not have immediate or direct control of their speed; therefore, task demand is expected to be different. Since speed modifications are expected to be slower with automation, it may be challenging to control driver’s workload level. Automation constrains driver capability through slower reaction times, information-processing capacity and speed. Driver’s activation (arousal) level is diminished, and drivers are more prone to be distracted. As we transition to partially automated or fully automated systems, the development of models that incorporate explanatory psychological constructs will be crucial. 

This project builds on the research team's previous work (Modeling Driver Behavior and Driver Aggressiveness Using Biobehavioral Methods – PART I, II, and III) where the team developed an extension to the Intelligent Driver Model (IDM) for manual driving, which captures three cognitive parameters: workload, situation awareness, and level of activation. The objective for this research project is to assess car-following behavioral changes of these cognitive parameters due to vehicle automation and build a framework for capturing these changes in a car-following model (e.g., the IDM). ]]></description>
      <pubDate>Sat, 17 Feb 2024 16:29:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2341499</guid>
    </item>
    <item>
      <title>Cybersecurity of Connected and Automated Vehicles via Traffic Anomaly Detection
</title>
      <link>https://rip.trb.org/View/2325918</link>
      <description><![CDATA[Connected and automated vehicles (CAVs) provide new opportunities for malicious actors to compromise vehicle security and compromise traffic flow. While obvious hacks that cause crashes may be easy to identify and isolate, other vehicle compromises may be more difficult to identify, especially if the hack impacts vehicle driving behavior or causes a vehicle to transmit faulty data via vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) connectivity.

In this work, the research team proposes to previous work funded by a University of Minnesota Center for Transportation Studies seed grant which conducted trajectory anomaly detection in compromised AVs (without connectivity) to consider potential data anomalies in a connected vehicle network and identify compromised vehicles on their driving behavior and the data they are sharing across the communication network (e.g., 5G connected vehicles). Specifically, the team proposes to use car following models to simulate traffic flow both of typical mixed autonomy traffic as well as traffic where some of the automated vehicles have been compromised and are sending compromised communications to other vehicles. The communication layer will also be modeled independently, with vehicles sharing basic safety messages (BSMs) across the network. Potential cyberattacks will be implemented in simulation, where compromised messages are communicated across the network, and the resulting traffic and communication data as well as traffic and communication data from uncompromised traffic flow will be compared to understand the potential impact of such attacks. Furthermore, the generated synthetic data will be used to develop anomaly detection techniques that leverage advancements in neural networks and autoencoders to identify atypical traffic and communication data.]]></description>
      <pubDate>Tue, 23 Jan 2024 17:16:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325918</guid>
    </item>
    <item>
      <title>Analyzing the Impact of Autonomous Maintenance Technology to Transportation Infrastructure Capacity for Condition Monitoring and Performance Management</title>
      <link>https://rip.trb.org/View/1742796</link>
      <description><![CDATA[The Autonomous Maintenance Technology (AMT) is a quickly emerging autonomous-vehicle-based technology for improving transportation infrastructure maintenance by removing drivers from risk. This project will develop models and algorithms to reveal its fundamental operating mechanism, and analyze its impact to transportation capacity for infrastructure condition monitoring and performance management. Newell car following model and moving-bottleneck-based traffic flow theory will be utilized to mathematically derive the roadway capacity under different scenarios. Multiple sensors, including high resolution Global Positioning System (GPS), Light Detection and Ranging (LiDAR), Radar, high definition camera, accelerometer and gyroscope installed on the AMT vehicles will collect real data from the field for model validations.]]></description>
      <pubDate>Mon, 05 Oct 2020 16:58:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/1742796</guid>
    </item>
    <item>
      <title>Modeling Driver Behavior and Driver Aggressiveness Using Biobehavioral Methods – Phase III</title>
      <link>https://rip.trb.org/View/1741268</link>
      <description><![CDATA[It is well known that driver inattention and human error are the primary causes of traffic accidents. In addition, existing driver behavioral modeling algorithms (e.g., car-following, lane changing) assume that driver variability is expressed through various distributions and random number generators. What constitutes aggressive driving, and which are the actions of aggressive drivers that negatively affect safety and traffic instability, are some of the topics that have not been studied thoroughly. At the same time, significant work has been done in the field of cognitive science and psychology, with emphasis in understanding, modeling, and predicting drivers’ intended actions. During the first two years of this project (Modeling Driver Behavior and Driver Aggressiveness Using Biobehavioral Methods – PART I and Part II), the research team conducted an extended driving simulator experiment and collected a multitude of measures of driver performance (speeds, accelerations, car-following), cognition (workload, situational awareness, level of activation), psychophysiological measures (brain activation, heart monitoring), and characteristics (demographics, personality, moral). Several scenarios with varying difficulty and presence of distraction were used. The data obtained through this experiment, will be used here to fulfill two major objectives: (1) calibrate a well-known car-following model (Intelligent Driver Model (IDM)) such that it captures driver heterogeneity as well as the impact of driving task on driver performance, and (2) develop a driver assessment tool that evaluates driver capability and performance. ]]></description>
      <pubDate>Fri, 25 Sep 2020 14:09:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/1741268</guid>
    </item>
    <item>
      <title>Machine Learning-based Trajectory Optimization of Connected and Autonomous Vehicles (CAVs)</title>
      <link>https://rip.trb.org/View/1669761</link>
      <description><![CDATA[Connected and autonomous vehicle (CAV) technologies provide solutions to the existing problems of the transportation systems. As widely known, CAVs can communicate with each other so that they can have coordinated accelerating or decelerating movements. In this manner, CAVs only need a smaller headway which will lead to a higher roadway capacity. For signalized intersections, CAVs can communicate with the signal lights to adjust their speeds when approaching the intersection, so that they can arrive at the intersection during green time periods. CAVs bring with them many benefits including improving safety, reducing emissions and increasing mobility of the transportation system.

In past decades, numerous research efforts have been made to focus on modeling longitudinal driver behaviors of traditional vehicles. Most microscopic models assume that human drivers react to the stimuli from leading vehicles to keep a safe headway with a desired velocity. In recent years, with the emerging of CAVs, new car following models have been introduced to accommodate the longitudinal driving behavior of CAVs. Efforts are needed to calibrate these car following models, and the results are highly related to the data availability, calibration method, and model structure. Despite different mechanisms and software interfaces, when multiple simulation software applications are compared, it seems that errors cannot be eliminated no matter how many parameters are introduced. On the other hand, machine learning has achieved much success in recent years. It allows the agent to keep learning from observations, actions conducted, and rewards received. When presented with a sequence of states and corresponding actions, extracted from the trajectory data, the algorithm can learn how the vehicles act when being faced with varying traffic conditions. The algorithm learns by associating any state observation, such as reaction time, speed, headway, and acceleration rate. The degree by which the agent action matches the vehicle’s action constitutes a reward in the learning sequence. In order to be better predict the upcoming states of CAVs under varying traffic conditions, there is a critical need to model the car following trajectory data using machine learning approach. 

This research will develop guidelines and recommendations for calibrating CAV car following model using trajectory data, and therefore will leading to a better understanding of how CAVs operate in the freeway system.
]]></description>
      <pubDate>Sun, 01 Dec 2019 22:22:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1669761</guid>
    </item>
    <item>
      <title>Implementation of SHRP2 Results within the Wyoming Connected Vehicle Variable Speed Limit System SHRP2 Implementation Assistance Program (Round 4)</title>
      <link>https://rip.trb.org/View/1571600</link>
      <description><![CDATA[The primary objective of the second phase is to model drivers responses to various adverse weather and road conditions, to specifically address: (1) Can trips occurring in inclement weather be identified efficiently and effectively using NDS and RID data? (2)  Can driver behavior during inclement weather conditions be characterized efficiently from the NDS? (3) What are the best surrogate measures for weather-related crashes that can be identified using the NDS data? (4) What type of analysis can be performed and conclusions be drawn from the resulting dataset? and (5) Can the NDS data be extrapolated to provide real-time weather information in the context of the Road Weather Connected Vehicle Applications?  

The vital purpose of Phase 2 is to generate models representing driving behavior changes as a function of weather conditions and includes investigation into speed selection, lane changing, and car following models.  ]]></description>
      <pubDate>Wed, 05 Dec 2018 12:40:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/1571600</guid>
    </item>
    <item>
      <title>HRDO-FY16-10 Analyzing NDS Data for Car-Following Behaviors</title>
      <link>https://rip.trb.org/View/1509485</link>
      <description><![CDATA[This project demonstrates how analyses of Naturalistic Driving Study (NDS) and the Roadway Information Database (RID) data can be used to develop car-following models under various driving conditions.]]></description>
      <pubDate>Tue, 24 Apr 2018 14:33:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/1509485</guid>
    </item>
    <item>
      <title>Simulation of Automated Vehicles’ Drive Cycles</title>
      <link>https://rip.trb.org/View/1419088</link>
      <description><![CDATA[Automated vehicles (AVs) are rapidly maturing; AVs will necessarily have different capabilities than human drivers, yet there is a major gap in understanding their likely drive cycles (the profile of speed versus time). Any changes in patterns of speed with respect to time will have structural consequences for the main outcomes from the transportation sector (e.g. mobility/accessibility, energy consumption, pollutant emissions, crash risk exposure, induced travel, etc.)

This research has two objectives: (1) to develop algorithms for plausible and legally-justifiable freeway carfollowing and arterial-street gap acceptance driving behavior for AVs; and (2) to implement these algorithms on a representative road network, in order to generate representative drive cycles for AVs that are both theoretically-grounded and based on empirical driving conditions.
The Main Proposal Narrative describes the current state of knowledge in this area; the research gap is that studies to date have considered neither the theoretical conditions under which faster travel speeds may be possible (or slower speeds may be required) on the basis of both vehicle kinematics and legal/liability considerations, nor the empirical distribution of the likely changes to speed profiles arising from them.

The theory underpinning the colloquial concept of defensive driving is known as Assured Clear Distance Ahead. ACDA-compliant driving strategies were initially implemented for AVs (in the specific context of queue discharge at signalized intersections) in research recently undertaken by the study team.

Addressing this gap in knowledge regarding AVsâ speed profiles will require extending the application of the ACDA concept to cover a broader set of representative contexts. This will lead to the first major contribution of this research, in the form of novel algorithms for AVsâ car-following and gapacceptance behavior that are grounded on the standard ACDA criterion which human drivers are also required to observe.

The algorithms will then be implemented in a representative empirical context, in order to generate the empirical drive cycles which are the second major product of this research. This requires a representative road network (the project will use the enhanced version of the standard Sioux Falls network abstraction), building footprints (available in geographic information system (GIS) format), digital elevation map (DEM), and traffic demands. The Sioux Falls network contains a diversity of roadway Page 2 of 2 environments (arterial and freeway), and has been widely-applied by researchers on network-analysis problems beginning in the mid-1970s. ArcGIS software will be used to generate sight lines, and the road network will be coded in VISSIM traffic microsimulation software.

AV driving behavior algorithms developed previously by the study team will be combined with those developed in the earlier phase of the present study, and will be applied in VISSIM using VISSIMâs âCOMâ developerâs toolkit. Simulations will be undertaken subject to a detailed experimental design. Results (drive cycles of AVs under a range of varying assumptions/inputs) will then be generated via statistical analysis of the vehicle trajectory data.

The targeted outlets for disseminating findings are the Summer 2017 edition of the Automated Vehicles Symposium Mid-Year TRB Conference, and the journal Transportation Research Part C: Emerging Technologies.]]></description>
      <pubDate>Mon, 08 Aug 2016 21:23:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/1419088</guid>
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