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.
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.
As the demand for curb parking increases and new types of curb space users compete for space, the need to more efficiently manage how vehicles interact with the curb becomes more apparent. One solution is to allow curb space users to submit a reservation ahead of their arrival that can be centrally managed and scheduled if the resources are availab
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Dataset
Ninan, S., & Rathinam, S. (2023). Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas [supporting dataset] (Report No. 06-004). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://doi.org/10.15787/VTT1/ZGRYVU
Ninan, Stephen and Sivakumar Rathinam. Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas [supporting dataset]. Report no. 06-004. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://doi.org/10.15787/VTT1/ZGRYVU.
Ninan, Stephen, and Sivakumar Rathinam Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas [supporting dataset]. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. 06-004, ROSA P. https://doi.org/10.15787/VTT1/ZGRYVU.
Connected, automated, shared, and electric (CASE) technologies have invoked Mobility 4.0—a connected, digitized, multimodal, and autonomous system of systems. This project established a flexible and adaptable blueprint that would streamline multidisciplinary and multistakeholder efforts as well as leverage available resources to prepare the Illinoi
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Jayme, A., Usta, B., Hamad, N., Tahlyan, D., Johnson, B. L., Mahmassani, H., Al-Qadi, I. L., & Quandt, J. (2023). Smart Mobility Blueprint for Illinois (Report No. FHWA-ICT-23-006). Illinois Center for Transportation. https://doi.org/10.36501/0197-9191/23-007
Jayme, Angeli, Berkan Usta, Nadim Hamad, Divyakant Tahlyan, Breton L Johnson, Hani Mahmassani, Imad L Al-Qadi, and Jerry Quandt. Smart Mobility Blueprint for Illinois. Report no. FHWA-ICT-23-006. Illinois Center for Transportation, 2023. https://doi.org/10.36501/0197-9191/23-007.
Jayme, Angeli, et al. Smart Mobility Blueprint for Illinois. Illinois Center for Transportation, 2023, Report no. FHWA-ICT-23-006, ROSA P. https://doi.org/10.36501/0197-9191/23-007.
Autonomous, or self-driving, vehicles have the capability to either fully or partially replace a human driver in the navigation to a destination. To better understand how receptive society will be to these types of vehicles, this study focused on the perceived level of trust in autonomous vehicles (AVs) by rural drivers and passengers. An online su
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Chang, K., & Williams, J. (2023). The Perception of Autonomous Driving in Rural Communities (Report No. INE/CSET 23.03). University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET). https://rosap.ntl.bts.gov/view/dot/68792
Chang, Kevin and Jade Williams. The Perception of Autonomous Driving in Rural Communities. Report no. INE/CSET 23.03. University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET), 2023. https://rosap.ntl.bts.gov/view/dot/68792.
Chang, Kevin, and Jade Williams The Perception of Autonomous Driving in Rural Communities. University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET), 2023, Report no. INE/CSET 23.03, ROSA P. https://rosap.ntl.bts.gov/view/dot/68792.
Given the importance of mental models towards safe interaction with Advanced Driver Assistance Systems (ADAS) and the various human factors challenges regarding ADAS such as mis-calibrated trust and the effect on workload, it is important to understand how different types of driving experiences and exposures affect drivers’ mental models about ADAS
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Pradhan, A. K., Roberts, S. C., Pai, G., Zhang, F., & Horrey, W. J. (2023). Change in Mental Models of ADAS in Relation to Quantity and Quality of Exposure. AAA Foundation for Traffic Safety. https://rosap.ntl.bts.gov/view/dot/68467
Pradhan, Anuj K., Shannon C Roberts, Ganesh Pai, Fangda Zhang, and William J Horrey. Change in Mental Models of ADAS in Relation to Quantity and Quality of Exposure. AAA Foundation for Traffic Safety, 2023. https://rosap.ntl.bts.gov/view/dot/68467.
Pradhan, Anuj K., et al. Change in Mental Models of ADAS in Relation to Quantity and Quality of Exposure. AAA Foundation for Traffic Safety, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68467.
The consideration of cooperative driving automation (CDA) in the transportation system management and operations (TSM&O ) processes has the potential for improving system performance in terms of safety, mobility, environmental impacts, and user satisfaction and acceptance. The goal of this project is to provide recommendations regarding the incorpo
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Hadi, M., Mata, H., & Hunsanon, T. (2023). Environment for Testing and Assessing Infrastructure Support of Connected Vehicle and Cooperative Highway Automation Applications. Florida. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/80039
Hadi, Mohammed, Hector Mata, and Thodsapon Hunsanon. Environment for Testing and Assessing Infrastructure Support of Connected Vehicle and Cooperative Highway Automation Applications. Florida. Department of Transportation, 2023. https://rosap.ntl.bts.gov/view/dot/80039.
Hadi, Mohammed, et al. Environment for Testing and Assessing Infrastructure Support of Connected Vehicle and Cooperative Highway Automation Applications. Florida. Department of Transportation, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/80039.
At roadway ecosystems with frequent movement conflicts among vehicles, pedestrians, and other road users, a road user entering the location immediately triggers a vibrant exchange of informal or formal cues with other road users and the traffic environment to ensure safe and efficient movement for all the road users at that location and at that tim
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John, A. P., Sabu, J., Dong, J., Li, Y., Chen, S., & Labi, S. (2023). Using Virtual Reality Techniques to Investigating Interactions Between Fully Autonomous Vehicles and Vulnerable Road Users (Report No. Report No. 62). Center for Connected and Automated Transportation. Purdue University. https://rosap.ntl.bts.gov/view/dot/73994
John, Ajuna P, Jelin Sabu, Jiqian Dong, Yujie Li, Sikai Chen, and Samuel Labi. Using Virtual Reality Techniques to Investigating Interactions Between Fully Autonomous Vehicles and Vulnerable Road Users. Report no. Report No. 62. Center for Connected and Automated Transportation. Purdue University, 2023. https://rosap.ntl.bts.gov/view/dot/73994.
John, Ajuna P, et al. Using Virtual Reality Techniques to Investigating Interactions Between Fully Autonomous Vehicles and Vulnerable Road Users. Center for Connected and Automated Transportation. Purdue University, 2023, Report no. Report No. 62, ROSA P. https://rosap.ntl.bts.gov/view/dot/73994.
Advanced Driver Assistance Systems (ADAS) support drivers with some driving tasks. However, drivers may lack appropriate knowledge about ADAS (referred to as their mental model), which can translate to drivers misusing or mistrusting the technologies, especially in situations beyond the capability of the system (i.e., edge cases). Past research sug
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AAA Foundation for Traffic Safety, & Safety Research Using Simulation (SAFER-SIM) University Transportation Center (2023). Change in Mental Models of ADAS in Relation to Quantity and Quality of Exposure [Fact Sheet]. Safety Research Using Simulation (SAFER-SIM) University Transportation Center. https://rosap.ntl.bts.gov/view/dot/68468
AAA Foundation for Traffic Safety and Safety Research Using Simulation (SAFER-SIM) University Transportation Center. Change in Mental Models of ADAS in Relation to Quantity and Quality of Exposure [Fact Sheet]. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2023. https://rosap.ntl.bts.gov/view/dot/68468.
AAA Foundation for Traffic Safety, et al. Change in Mental Models of ADAS in Relation to Quantity and Quality of Exposure [Fact Sheet]. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/68468.
Automated vehicles (AVs) present significant potential; yet, their technological maturity and performance remain to be proven. With several deployments underway in the state, it is critical for the Texas Department of Transportation (TxDOT) to provide the public with assurances that the AVs are performing safely in their intended operational enviro
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Chin, K., Wang, J., Zhou, X., Ross, H., Gold, A., & Sample, M. (2023). 0-7033: Defining Operational Design Domains (ODDs) for the Safe Blending of Levels 0-4 Connected and Autonomous Vehicles (CAVs) in the Traffic Streams (Report No. 0-7033). Texas Department of Transportation. Research and Technology Implementation Office. https://rosap.ntl.bts.gov/view/dot/79800
Chin, Kristie, Junmin Wang, Xingyu Zhou, Heidi Ross, Andrea Gold, and Mikhaela Sample. 0-7033: Defining Operational Design Domains (ODDs) for the Safe Blending of Levels 0-4 Connected and Autonomous Vehicles (CAVs) in the Traffic Streams. Report no. 0-7033. Texas Department of Transportation. Research and Technology Implementation Office, 2023. https://rosap.ntl.bts.gov/view/dot/79800.
Chin, Kristie, et al. 0-7033: Defining Operational Design Domains (ODDs) for the Safe Blending of Levels 0-4 Connected and Autonomous Vehicles (CAVs) in the Traffic Streams. Texas Department of Transportation. Research and Technology Implementation Office, 2023, Report no. 0-7033, ROSA P. https://rosap.ntl.bts.gov/view/dot/79800.
Collision Risk Models (CRM) are used by regulatory safety agencies to determine the safe separation minima and monitor the air-to-air collision risk level of an airspace. CRMs estimate the expected number of aircraft collisions and "total" risk for a given air traffic concept-of-operation (e.g., parallel approaches). The fidelity of the models, and
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Sherry, L., Shortle, J., Payan, A. P., Harrison, E., Thapa, A. K., Melgar, A. C., & Auguste, Y. (2023). Review of Current State of Artificial Intelligence/Machine Learning and Other Advanced Techniques Related to Air-to-Air Collision Risk Models (CRM) in the Terminal Airspace (Report No. DOT/FAA/TC-23/37). United States. Department of Transportation. Federal Aviation Administration. William J. Hughes Technical Center. https://doi.org/10.21949/1528218
Sherry, Lance, John Shortle, Alexia P Payan, Evan Harrison, Ashim Kumar Thapa, Alberto Cardenas Melgar, and Yohan Auguste. Review of Current State of Artificial Intelligence/Machine Learning and Other Advanced Techniques Related to Air-to-Air Collision Risk Models (CRM) in the Terminal Airspace. Report no. DOT/FAA/TC-23/37. United States. Department of Transportation. Federal Aviation Administration. William J. Hughes Technical Center, 2023. https://doi.org/10.21949/1528218.
Sherry, Lance, et al. Review of Current State of Artificial Intelligence/Machine Learning and Other Advanced Techniques Related to Air-to-Air Collision Risk Models (CRM) in the Terminal Airspace. United States. Department of Transportation. Federal Aviation Administration. William J. Hughes Technical Center, 2023, Report no. DOT/FAA/TC-23/37, ROSA P. https://doi.org/10.21949/1528218.
In September 2015, the U.S. Department of Transportation (USDOT) Intelligent Transportation Systems (ITS) Joint Program Office (JPO) awarded three deployment sites under the Connected Vehicle (CV) Pilot Deployment Program to: the New York City Department of Transportation (NYCDOT); the Tampa Hillsborough Expressway Authority (THEA); and the Wyoming
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Hartman, K., Wunderlich, K., Vasudevan, M., Thompson, K., Staples, B., Asare, S., Chang, J., Anderson, J., & Ali, A. (2023). Advancing Interoperable Connectivity Deployment: Connected Vehicle Pilot Deployment Results and Findings (Report No. FHWA-JPO-23-990). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/68128
Hartman, Kate, Karl Wunderlich, Meenakshy Vasudevan, Kathy Thompson, Barbara Staples, Sampson Asare, James Chang, Justin Anderson, and Atizaz Ali. Advancing Interoperable Connectivity Deployment: Connected Vehicle Pilot Deployment Results and Findings. Report no. FHWA-JPO-23-990. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2023. https://rosap.ntl.bts.gov/view/dot/68128.
Hartman, Kate, et al. Advancing Interoperable Connectivity Deployment: Connected Vehicle Pilot Deployment Results and Findings. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2023, Report no. FHWA-JPO-23-990, ROSA P. https://rosap.ntl.bts.gov/view/dot/68128.
Automated vehicles (AVs) present significant potential; yet, their technological maturity and performance remain to be proven. With several deployments underway in the state, it is critical for the Texas Department of Transportation (TxDOT) to provide the public with assurances that the AVs are performing safely in their intended operational enviro
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Chin, K., Gold, A., Sample, M., Ross, H., Wang, J., & Zhou, X. (2023). Automated Vehicle Recommendations for Texas: A Study of Highway and Urban Operational Design Domains (Report No. FHWA/TX-23/0-7033-1). University of Texas at Austin. Center for Transportation Research. https://rosap.ntl.bts.gov/view/dot/79799
Chin, Kristie, Andrea Gold, Mikhaela Sample, Heidi Ross, Junmin Wang, and Xingyu Zhou. Automated Vehicle Recommendations for Texas: A Study of Highway and Urban Operational Design Domains. Report no. FHWA/TX-23/0-7033-1. University of Texas at Austin. Center for Transportation Research, 2023. https://rosap.ntl.bts.gov/view/dot/79799.
Chin, Kristie, et al. Automated Vehicle Recommendations for Texas: A Study of Highway and Urban Operational Design Domains. University of Texas at Austin. Center for Transportation Research, 2023, Report no. FHWA/TX-23/0-7033-1, ROSA P. https://rosap.ntl.bts.gov/view/dot/79799.
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