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.
A significant majority of state-of-the-art autonomous sensing and navigation technologies rely on good lane markings or detailed 3D maps of the environment, making them more suited for urban communities. Conversely, many rural roads in the U.S. do not have lane markings and have irregular boundaries. These challenges are common to many small and ru
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Ninan, S., & Rathinam, S. (2023). Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas (Report No. 06-004). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/70413
Ninan, Stephen and Sivakumar Rathinam. Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas. Report no. 06-004. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/70413.
Ninan, Stephen, and Sivakumar Rathinam Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. 06-004, ROSA P. https://rosap.ntl.bts.gov/view/dot/70413.
This report lays the theoretical groundwork for participatory traffic control that integrates traditional infrastructure like traffic signals with connected and automated vehicles (CAVs) acting as mobile actuators. The study is divided into two main parts. First, we introduce a robust traffic state estimation method that leverages real-time data fr
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Supporting Files
Wu, M., Wang, X., Yin, Y., Liu, H., Wang, B., & Lynch, J. P. (2023). Leveraging Connected and Automated Vehicles for Participatory Traffic Control (Report No. CCAT Report No. 56). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.7302/8088
Wu, Minghui, Xingmin Wang, Yafeng Yin, Henry Liu, Ben Wang, and Jerome P Lynch. Leveraging Connected and Automated Vehicles for Participatory Traffic Control. Report no. CCAT Report No. 56. University of Michigan. Center for Connected and Automated Transportation, 2023. https://doi.org/10.7302/8088.
Wu, Minghui, et al. Leveraging Connected and Automated Vehicles for Participatory Traffic Control. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. CCAT Report No. 56, ROSA P. https://doi.org/10.7302/8088.
With the arrival of new technologies like connected and self-driving autonomous vehicles (AVs), the workload of regional dispatchers will increase. To this end, the CADS (Congestion Alerting Decision Support) tool was developed to support strategic transportation planning (on the order of weeks to months) and tactical transportation planning (on th
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Cummings, M., Rouphail, N. M., Srinivasan, R., Samandar, S., Das, T., Saleem, T., & Shah, N. (2023). Smart Connected and Automated Vehicle Fleet Management: Developing Regional Dispatch Decision Support for Congestion Mitigation (Report No. FHWA/NC/TCE2020-02 (P2)). North Carolina Department of Transportation. Research and Development Unit. https://rosap.ntl.bts.gov/view/dot/78872
Cummings, M, Nagui M. Rouphail, R Srinivasan, S Samandar, T Das, T Saleem, and NR Shah. Smart Connected and Automated Vehicle Fleet Management: Developing Regional Dispatch Decision Support for Congestion Mitigation. Report no. FHWA/NC/TCE2020-02 (P2). North Carolina Department of Transportation. Research and Development Unit, 2023. https://rosap.ntl.bts.gov/view/dot/78872.
Cummings, M, et al. Smart Connected and Automated Vehicle Fleet Management: Developing Regional Dispatch Decision Support for Congestion Mitigation. North Carolina Department of Transportation. Research and Development Unit, 2023, Report no. FHWA/NC/TCE2020-02 (P2), ROSA P. https://rosap.ntl.bts.gov/view/dot/78872.
The research team aims to investigate how networked autonomous mobility, such as self-driving taxis or delivery robots, will reshape our understanding of privacy and explore technical tools for privacy-preserving operation on the individual level and group level. The team will conduct comprehensive and realistic analysis using public datasets colle
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Zhao, D., & Wu, Z. S. (2023). Towards Privacy-Preserving Networked Autonomous Mobility: Analysis, Tools Development, and Real-World Evaluation. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/68539
Zhao, Ding and Zhiwei Steven Wu. Towards Privacy-Preserving Networked Autonomous Mobility: Analysis, Tools Development, and Real-World Evaluation. Mobility21, Carnegie Mellon University, 2023. https://rosap.ntl.bts.gov/view/dot/68539.
Zhao, Ding, and Zhiwei Steven Wu Towards Privacy-Preserving Networked Autonomous Mobility: Analysis, Tools Development, and Real-World Evaluation. Mobility21, Carnegie Mellon University, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68539.
There are numerous applications of the Autonomous Maintenance Technology (AMT) that have yet to be fully utilized or widely shared. There are many slow-moving operations conducted by Departments of Transportation (DOTs) with an attenuator (e.g., work zone set up, operations, take down, repairs, mowing, sweeping), platooning of two or more vehicles
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Miller, E., & Young, C. (2023). Literature Review Synthesizing the Current and Potential ATMA Applications (Report No. CDOT-2023-14). Colorado Department of Transportation. Applied Research & Innovations Branch. https://rosap.ntl.bts.gov/view/dot/80139
Miller, Erika and Chandler Young. Literature Review Synthesizing the Current and Potential ATMA Applications. Report no. CDOT-2023-14. Colorado Department of Transportation. Applied Research & Innovations Branch, 2023. https://rosap.ntl.bts.gov/view/dot/80139.
Miller, Erika, and Chandler Young Literature Review Synthesizing the Current and Potential ATMA Applications. Colorado Department of Transportation. Applied Research & Innovations Branch, 2023, Report no. CDOT-2023-14, ROSA P. https://rosap.ntl.bts.gov/view/dot/80139.
The authors will apply their results from neuroscience and safe control to improve driver-assistance technology as follows. First, the authors will study the use of visual attention information to detect risks early, before the failure to detect risk-critical obstacles can be identified from the drivers' control action and vehicle states. Second, t
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Jing, H., Wang, Z., & Nakahira, Y. (2023). Safety Assurance System Utilizing Visual Attention for Advanced Driver-Assistance Systems. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/68701
Jing, Haoming, Zhuoyuan Wang, and Yorie Nakahira. Safety Assurance System Utilizing Visual Attention for Advanced Driver-Assistance Systems. Mobility21, Carnegie Mellon University, 2023. https://rosap.ntl.bts.gov/view/dot/68701.
Jing, Haoming, et al. Safety Assurance System Utilizing Visual Attention for Advanced Driver-Assistance Systems. Mobility21, Carnegie Mellon University, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68701.
According to studies done by Pew Research, PAVE, and AAA [2], [5], [3], people are quite apprehensive about being passengers in self-driving vehicles. They are concerned about the ethical choices that AV systems might make and how AV manufacturers prioritize pedestrians, passengers, or other drivers in an unavoidable accident. Drivers worry whether
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Mangharam, R., Loeb, H., & Qiao, Z. (2023). Training Drivers to Automated Vehicles. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/70407
Mangharam, Rahul, Helen Loeb, and Zhijie Qiao. Training Drivers to Automated Vehicles. Mobility21, Carnegie Mellon University, 2023. https://rosap.ntl.bts.gov/view/dot/70407.
Mangharam, Rahul, et al. Training Drivers to Automated Vehicles. Mobility21, Carnegie Mellon University, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/70407.
The aim of Project 0-7129 was to demonstrate that AV trucking data can be effectively ingested and reported to TxDOT maintenance personnel, improving operational efficiency. Texas leads the nation in AV testing and deployment, with significant stakeholder support for the IRMF’s potential to reduce reporting latency and improve geographical and seve
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Chin, K., Gold, A., McAuley, A., Werner, M., & Zhang, Z. (2023). Working With Autonomous Trucks To Improve Routine Maintenance Operations [Research Brief] (Report No. 0-7129). University of Texas at Austin. Center for Transportation Research. https://rosap.ntl.bts.gov/view/dot/77787
Chin, Kristie, Andrea Gold, Anna McAuley, Mark Werner, and Zhanmin Zhang. Working With Autonomous Trucks To Improve Routine Maintenance Operations [Research Brief]. Report no. 0-7129. University of Texas at Austin. Center for Transportation Research, 2023. https://rosap.ntl.bts.gov/view/dot/77787.
Chin, Kristie, et al. Working With Autonomous Trucks To Improve Routine Maintenance Operations [Research Brief]. University of Texas at Austin. Center for Transportation Research, 2023, Report no. 0-7129, ROSA P. https://rosap.ntl.bts.gov/view/dot/77787.
This project will explore several potential applications of image processing, including NN/deep learning technologies, to the analysis of traffic scenes involving passenger and transit vehicles. The project team outlines three potential applications below- the exact distribution of effort and topics addressed will depend on the availability of stud
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Yang, D., Ozguner, U., Redmill, K., Yurtsever, E., Capito, L., & Zhang, H. (2023). Image Processing Approaches to Traffic Situation Understanding, Risk Assessment, and Safety. Mobility21 (UTC). https://rosap.ntl.bts.gov/view/dot/68477
Yang, Dongfang, Umit Ozguner, Keith Redmill, Ekim Yurtsever, Linda Capito, and Haolin Zhang. Image Processing Approaches to Traffic Situation Understanding, Risk Assessment, and Safety. Mobility21 (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/68477.
Yang, Dongfang, et al. Image Processing Approaches to Traffic Situation Understanding, Risk Assessment, and Safety. Mobility21 (UTC), 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68477.
Vehicle-pedestrian interactions in shared spaces represents a complex safety problem. Ideally, the vehicle must react safely to any pedestrian behavior, while the pedestrian behavior itself can be very complex and unpredictable. To emphasize this safety problem, in a 2019 Traffic Safety Facts report by the National Highway Traffic Safety Administra
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Hejase, B., Saim, M., Yang, D., Ko, M., Johora, F., Redmill, K., & Ozguner, U. (2023). Effect of Pedestrian and Crowds on Vehicle Motion and Traffic Flow. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/68737
Hejase, Bilal, Muhammad Saim, Dongfang Yang, Mert Ko, FatemaT Johora, Keith Redmill, and Umit Ozguner. Effect of Pedestrian and Crowds on Vehicle Motion and Traffic Flow. Mobility21, Carnegie Mellon University, 2023. https://rosap.ntl.bts.gov/view/dot/68737.
Hejase, Bilal, et al. Effect of Pedestrian and Crowds on Vehicle Motion and Traffic Flow. Mobility21, Carnegie Mellon University, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68737.
As the population of older drivers continues to grow, there is an increasing need to enhance their awareness and understanding of Connected and Automated Vehicles (CAVs). This study utilized an education program aimed at improving the knowledge and awareness of older drivers about CAVs, thereby preparing them to utilize the safety features of these
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Yi, P., Marovic, C., & Whittenberger, R. (2023). Educating Older Drivers to Improve Acceptance and Utilization of CAV Technologies (Report No. UA-CETran 2023-04). University of Michigan. Center for Connected and Automated Transportation. https://rosap.ntl.bts.gov/view/dot/73844
Yi, Ping, Claudia Marovic, and Reneé Whittenberger. Educating Older Drivers to Improve Acceptance and Utilization of CAV Technologies. Report no. UA-CETran 2023-04. University of Michigan. Center for Connected and Automated Transportation, 2023. https://rosap.ntl.bts.gov/view/dot/73844.
Yi, Ping, et al. Educating Older Drivers to Improve Acceptance and Utilization of CAV Technologies. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. UA-CETran 2023-04, ROSA P. https://rosap.ntl.bts.gov/view/dot/73844.
To standardize definitions and guide the design, regulation, and policy related to automated transportation, the Society of Automotive Engineers (SAE) has established a taxonomy consisting of six levels of vehicle automation. The SAE taxonomy defines each level based on the capabilities of the automated system. It does not fully consider the infras
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Chen, S., Zong, S., Chen, T., Huang, Z., Chen, Y., & Labi, S. (2023). A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure. MDPI. https://doi.org/10.3390/su151411258
Chen, Sikai, Shuya Zong, Tiantian Chen, Zilin Huang, Yanshen Chen, and Samuel Labi. A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure. MDPI, 2023. https://doi.org/10.3390/su151411258.
Chen, Sikai, et al. A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure. MDPI, 2023, ROSA P. https://doi.org/10.3390/su151411258.
This project focuses on experimental tests of the performance characteristics of autonomous vehicles (AVs) on highways and local roads in Minnesota. The project provides detailed data characterizing AV performance, which in turn can be used to inform the transportation community on implications for infrastructure maintenance, winter road maintenanc
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Rajamani, R., & Lund, V. (2023). Impacts of Autonomous Vehicles on Minnesota Roads [Technical Summary]. Minnesota. Department of Transportation. Office of Research & Innovation. https://rosap.ntl.bts.gov/view/dot/73018
Rajamani, Rajesh and Victor Lund. Impacts of Autonomous Vehicles on Minnesota Roads [Technical Summary]. Minnesota. Department of Transportation. Office of Research & Innovation, 2023. https://rosap.ntl.bts.gov/view/dot/73018.
Rajamani, Rajesh, and Victor Lund Impacts of Autonomous Vehicles on Minnesota Roads [Technical Summary]. Minnesota. Department of Transportation. Office of Research & Innovation, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/73018.
The effectiveness of the human–machine interface (HMI) in a driving automation system during takeover situations is based, in part, on its design. Past research has indicated that modality, specificity, and timing of the HMI have an impact on driver behavior. The objective of this study was to examine the effectiveness of two HMIs, which vary by mo
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Wang, M., Parker, J., Wong, N., Mehrotra, S., Roberts, S. C., Kim, W., Romo, A., & Horrey, W. J. (2023). Human-Machine Interfaces and Vehicle Automation: The Effect of HMI Design on Driver Performance and Behavior. Safety Research Using Simulation (SAFER-SIM) University Transportation Center. https://rosap.ntl.bts.gov/view/dot/72287
Wang, Meng, Jah'inaya Parker, Nicholas Wong, Shashank Mehrotra, Shannon C Roberts, Woon Kim, Alicia Romo, and William J Horrey. Human-Machine Interfaces and Vehicle Automation: The Effect of HMI Design on Driver Performance and Behavior. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2023. https://rosap.ntl.bts.gov/view/dot/72287.
Wang, Meng, et al. Human-Machine Interfaces and Vehicle Automation: The Effect of HMI Design on Driver Performance and Behavior. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/72287.
Development of connected and automated vehicles (CAVs) holds promise for reducing traffic crashes and maintaining mobility among older adults. Challenges remain, however, in ensuring that CAVs are accessible, acceptable, affordable, and otherwise inclusive for older adults. The objective of this project was to increase graduate students’ awareness
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Molnar, L. J., Zhou, F., Eby, D. W., Zakrajsek, J., St. Louis, R. M., Zanier, N., & Yi, P. (2023). Promoting Inclusive Design and Deployment of Connected and Automated Vehicles for Older Adults Through Education of Engineering Students. University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.7302/7962
Molnar, Lisa J, Feng Zhou, David W Eby, Jennifer Zakrajsek, Renee M. St. Louis, Nicole Zanier, and Ping Yi. Promoting Inclusive Design and Deployment of Connected and Automated Vehicles for Older Adults Through Education of Engineering Students. University of Michigan. Center for Connected and Automated Transportation, 2023. https://doi.org/10.7302/7962.
Molnar, Lisa J, et al. Promoting Inclusive Design and Deployment of Connected and Automated Vehicles for Older Adults Through Education of Engineering Students. University of Michigan. Center for Connected and Automated Transportation, 2023, ROSA P. https://doi.org/10.7302/7962.
This funding augmented maintenance and operations of the Ann Arbor Connected Environment. The Ann Arbor Connected Environment is one of the largest operational, real-world deployment of DSRC connected vehicles and infrastructure in the world. In 2017, it was expanded to encompass the entire City of Ann Arbor – 29 square miles. It has 70 infrastruct
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Bezzina, D., & Buonarosa, M. L. (2023). CCAT Ann Arbor Connected Environment (AACE) Operations and Maintenance (Report No. UMTR-2023-12). University of Michigan. Center for Connected and Automated Transportation. https://dx.doi.org/10.7302/7996
Bezzina, Debby and Mary Lynn Buonarosa. CCAT Ann Arbor Connected Environment (AACE) Operations and Maintenance. Report no. UMTR-2023-12. University of Michigan. Center for Connected and Automated Transportation, 2023. https://dx.doi.org/10.7302/7996.
Bezzina, Debby, and Mary Lynn Buonarosa CCAT Ann Arbor Connected Environment (AACE) Operations and Maintenance. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. UMTR-2023-12, ROSA P. https://dx.doi.org/10.7302/7996.
Texas has become a major hub for autonomous trucking activity, with companies operating routes daily and continuing to expand operations onto new roadways. Equipped with high-definition cameras and sensor suites, autonomous trucks present a new data opportunity for the Texas Department of Transportation (TxDOT) to improve its routine maintenance op
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Chin, K., McAuley, A., Werner, M., Maples, H., & Gold, A. (2023). Tapping Into Autonomous Trucking Data: An Intelligent Routine Maintenance Framework for Texas (Report No. FHWA/TX-23/0-7129-1). University of Texas at Austin. Center for Transportation Research. https://rosap.ntl.bts.gov/view/dot/77786
Chin, Kristie, Anna McAuley, Mark Werner, Hunter Maples, and Andrea Gold. Tapping Into Autonomous Trucking Data: An Intelligent Routine Maintenance Framework for Texas. Report no. FHWA/TX-23/0-7129-1. University of Texas at Austin. Center for Transportation Research, 2023. https://rosap.ntl.bts.gov/view/dot/77786.
Chin, Kristie, et al. Tapping Into Autonomous Trucking Data: An Intelligent Routine Maintenance Framework for Texas. University of Texas at Austin. Center for Transportation Research, 2023, Report no. FHWA/TX-23/0-7129-1, ROSA P. https://rosap.ntl.bts.gov/view/dot/77786.
This paper proposes an alternative strategy that could meet the needs of both connected vehicles and Wi-Fi 6 by allowing them to share spectrum under an appropriate set of coexistence rules. This could be achieved through changes in spectrum regulations, modest changes in technology for those C-V2X devices that operate in the shared band, and no ch
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Peha, J. M. (2023). Bringing Connected Vehicle Communications to Unlicensed Spectrum. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/68189
Peha, Jon M. Bringing Connected Vehicle Communications to Unlicensed Spectrum. Mobility21, Carnegie Mellon University, 2023. https://rosap.ntl.bts.gov/view/dot/68189.
Peha, Jon M Bringing Connected Vehicle Communications to Unlicensed Spectrum. Mobility21, Carnegie Mellon University, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68189.
The accurate detection and prediction of actions by multiple traffic participants such as pedestrians, vehicles, cyclists and others is a critical prerequisite for enabling self-driving vehicles to make autonomous decisions. Current approaches to teach an autonomous vehicle how to drive use reinforcement learning which is essentially relies on alre
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Hauptmann, A., Yu, L., Liu, W., Qian, Y., Cheng, Z., & Gui, L. (2023). Robust Automatic Detection of Traffic Activity. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/68085
Hauptmann, Alexander, et al. Robust Automatic Detection of Traffic Activity. Mobility21, Carnegie Mellon University, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68085.
This project aimed to implement the advances in new technologies to develop a robotic-based autonomous inspection system for underground pipelines. The new technology is based on the combination of intelligent powerful portable software and the newly advancement in mapping techniques. The system can scan and reconstruct the 3D profile of a pipeline
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Hamoush, S., Yi, S., Megri, A., Seong, Y., ElSherif, H., Muktadir, M. A., Garfo, S., Li, X., Keshinro, B., Khoury, H., & Burke, M. (2023). Technology of Mapping and NDT for Pipes Inspection (Report No. NCDOT Project No. 2021-055). North Carolina Department of Transportation. Research and Development Unit. https://rosap.ntl.bts.gov/view/dot/72995
Hamoush, Sameer, Sun Yi, Ahmed Megri, Younho Seong, HossamEldin ElSherif, M A Muktadir, and Selorm Garfo, et al.. Technology of Mapping and NDT for Pipes Inspection. Report no. NCDOT Project No. 2021-055. North Carolina Department of Transportation. Research and Development Unit, 2023. https://rosap.ntl.bts.gov/view/dot/72995.
Hamoush, Sameer, et al. Technology of Mapping and NDT for Pipes Inspection. North Carolina Department of Transportation. Research and Development Unit, 2023, Report no. NCDOT Project No. 2021-055, ROSA P. https://rosap.ntl.bts.gov/view/dot/72995.
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