By leveraging advanced technologies, Autonomous Vehicles (AVs) hold the potential to increase transportation safety and efficiency. This collection showcases USDOT-funded research and data concerning AVs. Bookmark this collection: https://rosap.ntl.bts.gov/collection_avs OR https://doi.org/10.21949/1x81-qs91.
The main objective of this research project is to enhance the accuracy of traffic speed prediction in ITS. The objectives of this project include: (1) Conducting a comprehensive review of traffic prediction techniques for CAVs. (2) Identifying a potential freeway segment and collecting the features of the selected scenario. (3) Developing and apply
...
Fan, W., & Hua, C. (2022). Real-Time Freeway Speed Prediction Based on Deep Learning in Connected and Autonomous Vehicles Environment (Report No. CAMMSE-UNCC-2022-UTC-Project-02). University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education. https://rosap.ntl.bts.gov/view/dot/64516
Fan, Wei and Chengying Hua. Real-Time Freeway Speed Prediction Based on Deep Learning in Connected and Autonomous Vehicles Environment. Report no. CAMMSE-UNCC-2022-UTC-Project-02. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2022. https://rosap.ntl.bts.gov/view/dot/64516.
Fan, Wei, and Chengying Hua Real-Time Freeway Speed Prediction Based on Deep Learning in Connected and Autonomous Vehicles Environment. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2022, Report no. CAMMSE-UNCC-2022-UTC-Project-02, ROSA P. https://rosap.ntl.bts.gov/view/dot/64516.
The Texas Connected Freight Corridors (TCFC) system is a connected vehicle (CV) environment that seeks to improve safety and mobility for the Texas Triangle, which consists of the Austin, Dallas/Fort Worth, Houston, San Antonio, and Laredo metropolitan regions, as seen in Figure 1.
Wood, N. (2022). Expand Applications for Texas Connected Freight Corridors {Summary]. Texas A&M Transportation Institute. https://rosap.ntl.bts.gov/view/dot/65826
Wood, Nick. Expand Applications for Texas Connected Freight Corridors {Summary]. Texas A&M Transportation Institute, 2022. https://rosap.ntl.bts.gov/view/dot/65826.
Wood, Nick Expand Applications for Texas Connected Freight Corridors {Summary]. Texas A&M Transportation Institute, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/65826.
Researchers simulated US transportation systems and forecasted the impacts of AVs and shared AVs (SAVs) on destination and mode choices of long-distance passenger and freight trips within the US, targeting a future 20+ years from now. They created demand sub-models for vehicle ownership, trip timing/scheduling and frequency, trip purpose and travel
...
Kockelman, K., Fakhrmoosavi, F., Huang, Y., Paithankar, P., Perrine, K. A., Zuniga-Garcia, N., & Hawkins, J. (2022). Understanding the Impact of Autonomous Vehicles on Long-Distance Passenger and Freight Travel in Texas: Project Summary (Report No. 0-7081). University of Texas at Austin. Center for Transportation Research. https://rosap.ntl.bts.gov/view/dot/68790
Kockelman, Kara, Fatemeh Fakhrmoosavi, Yantao Huang, Priyanka Paithankar, Kenneth A. Perrine, Natalia Zuniga-Garcia, and Jason Hawkins. Understanding the Impact of Autonomous Vehicles on Long-Distance Passenger and Freight Travel in Texas: Project Summary. Report no. 0-7081. University of Texas at Austin. Center for Transportation Research, 2022. https://rosap.ntl.bts.gov/view/dot/68790.
Kockelman, Kara, et al. Understanding the Impact of Autonomous Vehicles on Long-Distance Passenger and Freight Travel in Texas: Project Summary. University of Texas at Austin. Center for Transportation Research, 2022, Report no. 0-7081, ROSA P. https://rosap.ntl.bts.gov/view/dot/68790.
This study aims to investigate the effects of cooperative driving for two driving scenarios: non-signalized intersection (Fig 1) and freeway off-ramp. Multi-driver-in-the-loop co-simulation used for this research.
Yue, L., Abdel-Aty, M., & Wang, Z. (2022). Investigating the Effects of Cooperative Driving for CAVs in Different Driving Scenarios Using Multi-Driver Simulator Experiments [Summary Report]. Safety Research Using Simulation (SAFER-SIM) University Transportation Center. https://rosap.ntl.bts.gov/view/dot/75388
Yue, Lishengsa, Mohamed Abdel-Aty, and Zijin Wang. Investigating the Effects of Cooperative Driving for CAVs in Different Driving Scenarios Using Multi-Driver Simulator Experiments [Summary Report]. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2022. https://rosap.ntl.bts.gov/view/dot/75388.
Yue, Lishengsa, et al. Investigating the Effects of Cooperative Driving for CAVs in Different Driving Scenarios Using Multi-Driver Simulator Experiments [Summary Report]. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/75388.
Cooperative driving powered by connected vehicle (CV) technology is expected to improve traffic safety and efficiency, especially at locations with dense vehicle interactions. Although lots of research have developed their cooperative driving algorithms for different locations, the effects of human drivers in the loop and multi-agent driving decisi
...
Yue, L., Abdel-Aty, M., & Wang, Z. (2022). Investigating the Effects of Cooperative Driving for CAVs in Different Driving Scenarios Using Multi-Driver Simulator Experiments. Safety Research Using Simulation (SAFER-SIM) University Transportation Center. https://rosap.ntl.bts.gov/view/dot/75387
Yue, Lishengsa, Mohamed Abdel-Aty, and Zijin Wang. Investigating the Effects of Cooperative Driving for CAVs in Different Driving Scenarios Using Multi-Driver Simulator Experiments. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2022. https://rosap.ntl.bts.gov/view/dot/75387.
Yue, Lishengsa, et al. Investigating the Effects of Cooperative Driving for CAVs in Different Driving Scenarios Using Multi-Driver Simulator Experiments. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/75387.
The deployment of autonomous vehicle (AV) technologies may hold health and safety benefits for drivers across the driving lifespan (>18 years of age). However, up until now, the perceptions of such drivers about AVs have not been examined with a combined approach of using surveys and pre- and post-exposure to the actual technology. Lived experience
...
Classen, S., Sisiopiku, V. P., Mason, J., Hwangbo, S. W., Rogers, J., Yang, W., McKinney, B., & Li, Y. (2022). UF & UAB’s Phase II Demonstration Study: Developing a Model to Support Transportation System Decisions Considering the Experiences of Drivers of All Age Groups with Autonomous Vehicle Technology (Project A3) (Report No. Project A3). Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE). https://rosap.ntl.bts.gov/view/dot/68440
Classen, Sherrilene, Virginia P. Sisiopiku, Justin Mason, Seung-Woo Hwangbo, Jason Rogers, Wencui Yang, Brandy McKinney, and Yuan Li. UF & UAB’s Phase II Demonstration Study: Developing a Model to Support Transportation System Decisions Considering the Experiences of Drivers of All Age Groups with Autonomous Vehicle Technology (Project A3). Report no. Project A3. Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE), 2022. https://rosap.ntl.bts.gov/view/dot/68440.
Classen, Sherrilene, et al. UF & UAB’s Phase II Demonstration Study: Developing a Model to Support Transportation System Decisions Considering the Experiences of Drivers of All Age Groups with Autonomous Vehicle Technology (Project A3). Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE), 2022, Report no. Project A3, ROSA P. https://rosap.ntl.bts.gov/view/dot/68440.
The purpose of this proposal is to develop innovative reinforcement learning control methods for lane changing of connected and autonomous vehicles (CAVs) in mixed traffic. In the proposed framework, before the CAV changes to the target lane, it needs to predict most likely behavior of surrounding vehicles related to the lane change and then determ
...
Dataset
Jiang, Z. P., Ozbay, K., Chakraborty, S., & Cui, L. (2022). Lane Changing of Autonomous Vehicles in Mixed Traffic Environments: A Reinforcement Learning Approach [Supporting Dataset]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART). https://doi.org/10.5281/zenodo.6977370
Jiang, Zhong-Ping, Kaan Ozbay, Sayantan Chakraborty, and Leilei Cui. Lane Changing of Autonomous Vehicles in Mixed Traffic Environments: A Reinforcement Learning Approach [Supporting Dataset]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2022. https://doi.org/10.5281/zenodo.6977370.
Jiang, Zhong-Ping, et al. Lane Changing of Autonomous Vehicles in Mixed Traffic Environments: A Reinforcement Learning Approach [Supporting Dataset]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2022, ROSA P. https://doi.org/10.5281/zenodo.6977370.
Studies from around the world have started investigating aspects of ATV simulation. However, these efforts are still in their infancy, and are constrained by the limited amount of real-world data to validate and calibrate the developed models. The existence of some work on this subject does not mean that the conducted research up to this point is s
...
Hadi, M., Elefteriadou, L., Guin, A., Hunter, M., Rouphail, N., Samandar, S., Arafat, M., Amine, K., Bae, J., Das, T., Hunsanon, T., & Wang, X. (2022). Utilization of Connectivity and Automation in Support of Transportation Agencies’ Decision Making (Report No. Project G3). Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE). https://rosap.ntl.bts.gov/view/dot/63340
Hadi, Mohammed, Lily Elefteriadou, Angshuman Guin, Michael Hunter, Nagui Rouphail, Shoaib Samandar, and Mahmoud Arafat, et al.. Utilization of Connectivity and Automation in Support of Transportation Agencies’ Decision Making. Report no. Project G3. Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE), 2022. https://rosap.ntl.bts.gov/view/dot/63340.
Hadi, Mohammed, et al. Utilization of Connectivity and Automation in Support of Transportation Agencies’ Decision Making. Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE), 2022, Report no. Project G3, ROSA P. https://rosap.ntl.bts.gov/view/dot/63340.
Recent research activities are focused on improving Vehicle-to-Vehicle Communication (V2V) based on the 5G Technology. V2V applications are important because they are expected to reduce the risk of accidents up to 80%, enhance traffic management, mitigate congestion, and optimize fuel consumption. Typical autonomous vehicle applications require a h
...
Abuhdima, E., Comert, G., Huang, C. T., Pisu, P., Liu, J., & Zhao, C. (2022). Modeling Impact of Weather Conditions on 5G Communication and Mitigation Measures on Control of Automated Intersections. Center for Connected Multimodal Mobility, Clemson University. https://rosap.ntl.bts.gov/view/dot/67665
Abuhdima, Esmail, Gurcan Comert, Chin Tser Huang, Pierluigi Pisu, Jian Liu, and Chunheng Zhao. Modeling Impact of Weather Conditions on 5G Communication and Mitigation Measures on Control of Automated Intersections. Center for Connected Multimodal Mobility, Clemson University, 2022. https://rosap.ntl.bts.gov/view/dot/67665.
Abuhdima, Esmail, et al. Modeling Impact of Weather Conditions on 5G Communication and Mitigation Measures on Control of Automated Intersections. Center for Connected Multimodal Mobility, Clemson University, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/67665.
This report chronicles the work undertaken by researchers at the University of Illinois Urbana Champaign to identify policies and design guidelines to plan for connected and autonomous vehicles (CAVs) in mid-sized regions in Illinois. The report starts with the goals of this work followed by a review of existing literature. The review addresses CAV
...
Benkraouda, O., Braun, L. M., & Chakraborty, A. (2022). Policies and Design Guidelines To Plan for Connected and Autonomous Vehicles (Report No. FHWA-ICT-22-012). Illinois Center for Transportation. https://doi.org/10.36501/0197-9191/22-012
Benkraouda, Ouafa, Lindsay Maurer Braun, and Arnab Chakraborty. Policies and Design Guidelines To Plan for Connected and Autonomous Vehicles. Report no. FHWA-ICT-22-012. Illinois Center for Transportation, 2022. https://doi.org/10.36501/0197-9191/22-012.
Benkraouda, Ouafa, et al. Policies and Design Guidelines To Plan for Connected and Autonomous Vehicles. Illinois Center for Transportation, 2022, Report no. FHWA-ICT-22-012, ROSA P. https://doi.org/10.36501/0197-9191/22-012.
Connected and autonomous vehicles (CAVs) are an emerging technology that has great potential for increasing road capacity and reducing traffic incidents, congestion, fuel/energy consumption as well as emission, all of which may support safer and more reliable and efficient (and potentially sustainable) transportation systems. Given that transportat
...
Lee, J. Y., & Zhao, J. (2022). Effect of Connected and Autonomous Vehicles on Supply Chain Performance (Report No. 69A3551747133). University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education. https://rosap.ntl.bts.gov/view/dot/64504
Lee, Ji Yun and Jie Zhao. Effect of Connected and Autonomous Vehicles on Supply Chain Performance. Report no. 69A3551747133. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2022. https://rosap.ntl.bts.gov/view/dot/64504.
Lee, Ji Yun, and Jie Zhao Effect of Connected and Autonomous Vehicles on Supply Chain Performance. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2022, Report no. 69A3551747133, ROSA P. https://rosap.ntl.bts.gov/view/dot/64504.
Forty-three households in the Sacramento region representing diverse demographics, modal preferences, mobility barriers, and weekly vehicle miles traveled (VMT) were provided personal chauffeurs for one or two weeks to simulate travel behavior with a personally-owned, fully autonomous vehicle (AV). During the chauffeur week(s), the total number of
...
Harb, M., Malik, J., Circella, G., & Walker, J. L. (2022). Simulating Life with Personally-Owned Autonomous Vehicles through a Naturalistic Experiment with Personal Drivers (Report No. UC-ITS-2018-09). University of California Institute of Transportation Studies. https://doi.org/10.7922/G2WH2N96
Harb, Mustapha, Jai Malik, Giovanni Circella, and Joan L Walker. Simulating Life with Personally-Owned Autonomous Vehicles through a Naturalistic Experiment with Personal Drivers. Report no. UC-ITS-2018-09. University of California Institute of Transportation Studies, 2022. https://doi.org/10.7922/G2WH2N96.
Harb, Mustapha, et al. Simulating Life with Personally-Owned Autonomous Vehicles through a Naturalistic Experiment with Personal Drivers. University of California Institute of Transportation Studies, 2022, Report no. UC-ITS-2018-09, ROSA P. https://doi.org/10.7922/G2WH2N96.
This project will use a combination of laboratory experimentation and road demonstrations to better understand the reduction of LiDAR signal and object detection capability under adverse weather conditions found in Minnesota. It will also lead to concepts to improve LiDAR systems to adapt to such conditions through better signal processing image re
...
Northrop, W., Zhan, L., Haag, S., & Zarling, D. (2022). Can Automated Vehicles "See" in Minnesota? Ambient Particle Effects on LiDAR (Report No. MN 2022-03). Minnesota. Department of Transportation. Office of Research & Innovation. https://rosap.ntl.bts.gov/view/dot/65537
Northrop, William, Lu Zhan, Shawn Haag, and Darrick Zarling. Can Automated Vehicles "See" in Minnesota? Ambient Particle Effects on LiDAR. Report no. MN 2022-03. Minnesota. Department of Transportation. Office of Research & Innovation, 2022. https://rosap.ntl.bts.gov/view/dot/65537.
Northrop, William, et al. Can Automated Vehicles "See" in Minnesota? Ambient Particle Effects on LiDAR. Minnesota. Department of Transportation. Office of Research & Innovation, 2022, Report no. MN 2022-03, ROSA P. https://rosap.ntl.bts.gov/view/dot/65537.
VisioStack Inc. completed initial research to automate drone inspection flights over railroad track using real-time rail detection and track centerline following to simulate flight control without Global Positioning System (GPS) information. Uncrewed Aerial Systems (UAS, or drones) are platforms that may enable more frequent and safer track inspect
...
Stuart, C., & Doran, J. (2022). Automated Track Centerline Following for Drone Flight Automation [Research Results] (Report No. RR 22-15). United States. Department of Transportation. Federal Railroad Administration. https://rosap.ntl.bts.gov/view/dot/64766
Stuart, Cameron and Joshua Doran. Automated Track Centerline Following for Drone Flight Automation [Research Results]. Report no. RR 22-15. United States. Department of Transportation. Federal Railroad Administration, 2022. https://rosap.ntl.bts.gov/view/dot/64766.
Stuart, Cameron, and Joshua Doran Automated Track Centerline Following for Drone Flight Automation [Research Results]. United States. Department of Transportation. Federal Railroad Administration, 2022, Report no. RR 22-15, ROSA P. https://rosap.ntl.bts.gov/view/dot/64766.
In efforts to predict the long-distance travel impacts (for passengers and freight) of self-driving cars and trucks across Texas and the US, researchers estimated models for long-distance domestic passenger and freight trips before and after the introduction of autonomous vehicles (AVs) and applied the passenger models to a 10%synthetic US populati
...
Kockelman, K., Huang, Y., Fakhrmoosavi, F., Perrine, K. A., Paithankar, P., Hawkins, J., Zuniga-Garcia, N., & Vellimana, M. (2022). Understanding the Impact of Autonomous Vehicles on Long-Distance Passenger and Freight Travel in Texas: Final Report (Report No. FHWA/TX-23/0-7081-1). University of Texas at Austin. Center for Transportation Research. https://rosap.ntl.bts.gov/view/dot/68788
Kockelman, Kara, Yantao Huang, Fatemeh Fakhrmoosavi, Kenneth A. Perrine, Priyanka Paithankar, Jason Hawkins, Natalia Zuniga-Garcia, and Maithreyi Vellimana. Understanding the Impact of Autonomous Vehicles on Long-Distance Passenger and Freight Travel in Texas: Final Report. Report no. FHWA/TX-23/0-7081-1. University of Texas at Austin. Center for Transportation Research, 2022. https://rosap.ntl.bts.gov/view/dot/68788.
Kockelman, Kara, et al. Understanding the Impact of Autonomous Vehicles on Long-Distance Passenger and Freight Travel in Texas: Final Report. University of Texas at Austin. Center for Transportation Research, 2022, Report no. FHWA/TX-23/0-7081-1, ROSA P. https://rosap.ntl.bts.gov/view/dot/68788.
With the accelerated deployment of connected and automated vehicle (CAV) technologies, public agencies have urgent needs on how to utilize these rich data sources of CAVs to improve traffic mobility, safety, and environmental and energy impact. This research will tackle one of the big data challenges, which is mining driving behavior patterns using
...
Di, X., Jin, P., Huang, Y., & Mo, Z. (2022). Driving Behavioral Learning Leveraging Sensing Information from Innovation Hub (Report No. CAIT-UTC-REG46). Rutgers University. Center for Advanced Infrastructure and Transportation. https://rosap.ntl.bts.gov/view/dot/64136
Di, Xuan, Peter Jin, Yufei Huang, and Zhaobin Mo. Driving Behavioral Learning Leveraging Sensing Information from Innovation Hub. Report no. CAIT-UTC-REG46. Rutgers University. Center for Advanced Infrastructure and Transportation, 2022. https://rosap.ntl.bts.gov/view/dot/64136.
Di, Xuan, et al. Driving Behavioral Learning Leveraging Sensing Information from Innovation Hub. Rutgers University. Center for Advanced Infrastructure and Transportation, 2022, Report no. CAIT-UTC-REG46, ROSA P. https://rosap.ntl.bts.gov/view/dot/64136.
Work zones are critical for efficient and safe operation of a highway transportation system. Performing the maintenance required for a roadway infrastructure, however, could involve risks. In 2017 alone, a total of 158,000 total vehicle crashes occurred in our nation’s work zones, accounting for 61,000 injuries [1]. Many of these frequently involve
...
Hu, X., Tang, Q., & Liu, J. (2022). Analyzing the Impact of Autonomous Maintenance Technology to Transportation Infrastructure Capacity for Condition Monitoring and Performance Management (Report No. 2020-MST-05). Washington State University. National Center for Transportation Infrastructure Durability & Life Extension (TriDurLE) UTC. https://rosap.ntl.bts.gov/view/dot/68696
Hu, Xianbiao, Qing Tang, and Jenny Liu. Analyzing the Impact of Autonomous Maintenance Technology to Transportation Infrastructure Capacity for Condition Monitoring and Performance Management. Report no. 2020-MST-05. Washington State University. National Center for Transportation Infrastructure Durability & Life Extension (TriDurLE) UTC, 2022. https://rosap.ntl.bts.gov/view/dot/68696.
Hu, Xianbiao, et al. Analyzing the Impact of Autonomous Maintenance Technology to Transportation Infrastructure Capacity for Condition Monitoring and Performance Management. Washington State University. National Center for Transportation Infrastructure Durability & Life Extension (TriDurLE) UTC, 2022, Report no. 2020-MST-05, ROSA P. https://rosap.ntl.bts.gov/view/dot/68696.
In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, vehicles may expend extra energy as they try to form platoons. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the
...
Eksioglu, B., Schmid, M. J. A., Huynh, N., Comert, G., & Liu, D. (2022). Framework for Accommodating Emerging Autonomous Vehicles. Center for Connected Multimodal Mobility, Clemson University. https://rosap.ntl.bts.gov/view/dot/67656
Eksioglu, Burak, Matthias Josef Al Schmid, Nathan Huynh, Gurcan Comert, and Dahui Liu. Framework for Accommodating Emerging Autonomous Vehicles. Center for Connected Multimodal Mobility, Clemson University, 2022. https://rosap.ntl.bts.gov/view/dot/67656.
Eksioglu, Burak, et al. Framework for Accommodating Emerging Autonomous Vehicles. Center for Connected Multimodal Mobility, Clemson University, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/67656.
The United States Department of Transportation (USDOT) recognizes that cooperative automated driving systems will have a transformative impact on how the nation’s highways will operate in the future. One of the proposed near-term services is truck platooning. Truck platooning promises fuel savings to platooning trucks by enabling them to follow eac
...
Asare, S., Chang, J., & Staples, B. (2022). Truck Platooning Early Deployment Assessment – Independent Evaluation: Expanded Analysis Plan (Report No. FHWA-JPO-22-962). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/64246
Asare, Sampson, James Chang, and Barbara Staples. Truck Platooning Early Deployment Assessment – Independent Evaluation: Expanded Analysis Plan. Report no. FHWA-JPO-22-962. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2022. https://rosap.ntl.bts.gov/view/dot/64246.
Asare, Sampson, et al. Truck Platooning Early Deployment Assessment – Independent Evaluation: Expanded Analysis Plan. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2022, Report no. FHWA-JPO-22-962, ROSA P. https://rosap.ntl.bts.gov/view/dot/64246.
Crashes involving transit vehicles, bicyclists, and pedestrians are a concern in Texas, especially in urban areas. This research explored the potential of automated and connected vehicle (AV/CV) technology to reduce or eliminate these crashes. The project objectives focused on identifying safety concerns related to the interaction of transit vehicl
...
Turnbull, K. F., Sunkari, S., Turner, S., Higgins, L., Fitzpatrick, K., Pratt, M., Gick, B., & Charara, H. (2022). Automated and Connected Vehicle (AV/CV) Test Bed To Improve Transit, Bicycle, and Pedestrian Safety Phase III: Technical Report (Report No. FHWA/TX-22/0-6875-03-R1). Texas A&M Transportation Institute. https://rosap.ntl.bts.gov/view/dot/64269
Turnbull, Katherine F., Srinivasa Sunkari, Shawn Turner, Laura Higgins, Kay Fitzpatrick, Mike Pratt, Brittney Gick, and Hassan Charara. Automated and Connected Vehicle (AV/CV) Test Bed To Improve Transit, Bicycle, and Pedestrian Safety Phase III: Technical Report. Report no. FHWA/TX-22/0-6875-03-R1. Texas A&M Transportation Institute, 2022. https://rosap.ntl.bts.gov/view/dot/64269.
Turnbull, Katherine F., et al. Automated and Connected Vehicle (AV/CV) Test Bed To Improve Transit, Bicycle, and Pedestrian Safety Phase III: Technical Report. Texas A&M Transportation Institute, 2022, Report no. FHWA/TX-22/0-6875-03-R1, ROSA P. https://rosap.ntl.bts.gov/view/dot/64269.
Links with this icon indicate that you are leaving a Bureau of Transportation
Statistics (BTS)/National Transportation Library (NTL)
Web-based service.
Thank you for visiting.
You are about to access a non-government link outside of
the U.S. Department of Transportation's National
Transportation Library.
Please note: While links to Web sites outside of DOT are
offered for your convenience, when you exit DOT Web sites,
Federal privacy policy and Section 508 of the Rehabilitation
Act (accessibility requirements) no longer apply. In
addition, DOT does not attest to the accuracy, relevance,
timeliness or completeness of information provided by linked
sites. Linking to a Web site does not constitute an
endorsement by DOT of the sponsors of the site or the
products presented on the site. For more information, please
view DOT's Web site linking policy.
To get back to the page you were previously viewing, click
your Cancel button.