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.
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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Espindola, A., Alexander, L., & Rajamani, R. (2023). Influence of Autonomous and Partially Autonomous Vehicles on Minnesota Roads (Report No. MN 2023-23). Minnesota. Department of Transportation. Research Services & Library. https://rosap.ntl.bts.gov/view/dot/73017
Espindola, Andre, Lee Alexander, and Rajesh Rajamani. Influence of Autonomous and Partially Autonomous Vehicles on Minnesota Roads. Report no. MN 2023-23. Minnesota. Department of Transportation. Research Services & Library, 2023. https://rosap.ntl.bts.gov/view/dot/73017.
Espindola, Andre, et al. Influence of Autonomous and Partially Autonomous Vehicles on Minnesota Roads. Minnesota. Department of Transportation. Research Services & Library, 2023, Report no. MN 2023-23, ROSA P. https://rosap.ntl.bts.gov/view/dot/73017.
The key accomplishments of the project are summarized as follows. The team conducted a literature review on computer vision for smart cities with a focus on transportation and summarized a resource list that lists publicly available traffic camera systems in the U.S. The team also established an automatic pipeline for data acquisition and developed
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Gao, J., Xu, C., Zhang, D., Zuo, F., Yang, L., & Hammami, O. (2023). Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART). https://rosap.ntl.bts.gov/view/dot/72518
Gao, Jingqin, Chuan Xu, Daniel Zhang, Fan Zuo, Liu Yang, and Omar Hammami. Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2023. https://rosap.ntl.bts.gov/view/dot/72518.
Gao, Jingqin, et al. Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/72518.
Computer vision is reshaping the transportation industry and bringing its unique capabilities to the table to enable next generation smart transportation systems in many different ways. The state-of-the-art of IoT strategies and computer vision techniques is well-studied in the literature, some has already been tested and used for certain use cases
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Gao, J., Ozbay, K., Xu, C., Zhang, D., Zuo, F., Yang, L., & Hammami, O. (2023). Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems [Literature Review]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART). https://rosap.ntl.bts.gov/view/dot/72520
Gao, Jingqin, Kaan Ozbay, Chuan Xu, Daniel Zhang, Fan Zuo, Liu Yang, and Omar Hammami. Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems [Literature Review]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2023. https://rosap.ntl.bts.gov/view/dot/72520.
Gao, Jingqin, et al. Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems [Literature Review]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/72520.
Connected and automated vehicles (CAVs) represent a transformative technology that can revolutionize how people and goods move. The private sector is at the forefront of developing the technology, and many municipalities are attempting to prepare for a more connected and automated future. As such, both private and public sectors are in need of a sk
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Supporting Files
Masoud, N., Tafreshian, A., Lim, J., Liu, H., Carrel, A., Bao, S., Work, D. B., & Orosz, G. (2023). Autonomy in Transportation Education (Report No. CCAT Report No. 75). University of Michigan. Center for Connected and Automated Transportation. https://dx.doi.org/10.7302/8003
Masoud, Neda, Amirmahdi Tafreshian, Jisoon Lim, Henry Liu, Andre Carrel, Shan Bao, Daniel B. Work, and Gabor Orosz. Autonomy in Transportation Education. Report no. CCAT Report No. 75. University of Michigan. Center for Connected and Automated Transportation, 2023. https://dx.doi.org/10.7302/8003.
Masoud, Neda, et al. Autonomy in Transportation Education. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. CCAT Report No. 75, ROSA P. https://dx.doi.org/10.7302/8003.
This report introduces a robust green light optimal speed advisory (GLOSA) system for fixed and actuated traffic signals which considers a probability distribution. These distributions represent the domain of possible switching times from the signal phasing and timing (SPaT) messages. The system finds the least-cost (minimum fuel consumption) vehic
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Shafik, A., Eteifa, S., & Rakha, H. A. (2023). Optimal Trajectory Planning Algorithm for Connected and Autonomous Vehicles towards Uncertainty of Actuated Traffic Signals (Report No. UMEC-050). Morgan State University. Urban Mobility & Equity Center. https://rosap.ntl.bts.gov/view/dot/67184
Shafik, Amr, Seifeldeen Eteifa, and Hesham A. Rakha. Optimal Trajectory Planning Algorithm for Connected and Autonomous Vehicles towards Uncertainty of Actuated Traffic Signals. Report no. UMEC-050. Morgan State University. Urban Mobility & Equity Center, 2023. https://rosap.ntl.bts.gov/view/dot/67184.
Shafik, Amr, et al. Optimal Trajectory Planning Algorithm for Connected and Autonomous Vehicles towards Uncertainty of Actuated Traffic Signals. Morgan State University. Urban Mobility & Equity Center, 2023, Report no. UMEC-050, ROSA P. https://rosap.ntl.bts.gov/view/dot/67184.
This report presents the results of the Arlington Rideshare, Automation, and Payment Integration Demonstration (RAPID) project. This project integrates a shared, dynamically routed automated vehicle (AV) fleet into an existing public rideshare system in Arlington, Texas.
Foss, A. (2023). Arlington Rideshare, Automation, and Payment Integration Demonstration (RAPID) (Report No. FTA Report No. 0244). United States. Department of Transportation. Federal Transit Administration. https://doi.org/10.21949/1527657
Foss, Ann. Arlington Rideshare, Automation, and Payment Integration Demonstration (RAPID). Report no. FTA Report No. 0244. United States. Department of Transportation. Federal Transit Administration, 2023. https://doi.org/10.21949/1527657.
Foss, Ann Arlington Rideshare, Automation, and Payment Integration Demonstration (RAPID). United States. Department of Transportation. Federal Transit Administration, 2023, Report no. FTA Report No. 0244, ROSA P. https://doi.org/10.21949/1527657.
Reliable, lane-level, absolute position determination for connected and automated vehicles (CAV’s) is near at hand due to advances in sensor and computing technology. These capabilities in conjunction with high-definition maps enable lane determination, per lane queue determination, and enhanced performance in applications. This project investigate
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Dataset
Farrell, J. A., Wu, G., Hu, W., & Oswald, D. (2023). Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications [Supporting Dataset] (Report No. NCST-UCR-RR-23-13). National Center for Sustainable Transportation (NCST) (UTC). https://doi.org/10.6086/D11M43
Farrell, Jay A., Guoyuan Wu, Wang Hu, and David Oswald. Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications [Supporting Dataset]. Report no. NCST-UCR-RR-23-13. National Center for Sustainable Transportation (NCST) (UTC), 2023. https://doi.org/10.6086/D11M43.
Farrell, Jay A., et al. Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications [Supporting Dataset]. National Center for Sustainable Transportation (NCST) (UTC), 2023, Report no. NCST-UCR-RR-23-13, ROSA P. https://doi.org/10.6086/D11M43.
To establish a framework for considering these wraparound AV impacts in New York City, the NYU Rudin Center for Transportation Policy and Management led a multi-stakeholder initiative in conjunction with NYU’s C2SMART, USDOT University Transportation Center. The team hosted three workshops in December 2021 addressing issues and opportunities in sev
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Namdarpour, Farnoosh, Joseph Y. J. Chow, and BingQing Liu. Autonomous Vehicle Good Citizenry Standard [Supporting Dataset]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2023. http://doi.org/10.5281/zenodo.7733664 https://doi.org/10.5281/zenodo.7430184.
Driving intelligence test is critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would
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Liu, H., & Feng, Y. (2023). DeepScenario: City Scale Scenario Generation for Automated Driving System Testing & Evaluation (Report No. UMTRI-2023-3). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.7302/7018
Liu, Henry and Yiheng Feng. DeepScenario: City Scale Scenario Generation for Automated Driving System Testing & Evaluation. Report no. UMTRI-2023-3. University of Michigan. Center for Connected and Automated Transportation, 2023. https://doi.org/10.7302/7018.
Liu, Henry, and Yiheng Feng DeepScenario: City Scale Scenario Generation for Automated Driving System Testing & Evaluation. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. UMTRI-2023-3, ROSA P. https://doi.org/10.7302/7018.
Testing and evaluation is a critical step in the development and deployment of connected and automated vehicle (CAV) technology. Testing standards for human-driven vehicles, such as Federal Motor Vehicle Safety Standards (FMVSS), were established a longtime ago. However, current standards cannot be applied to CAVs, because they often assume the pre
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Feng, Y., Bao, S., & Liu, H. (2023). Connected and Automated Vehicle (CAV) Testing Scenario Design and Implementation Using Naturalistic Driving Data and Augmented Reality (Report No. UMTRI-2023-6). University of Michigan. Center for Connected and Automated Transportation. https://rosap.ntl.bts.gov/view/dot/73486
Feng, Yiheng, Shan Bao, and Henry Liu. Connected and Automated Vehicle (CAV) Testing Scenario Design and Implementation Using Naturalistic Driving Data and Augmented Reality. Report no. UMTRI-2023-6. University of Michigan. Center for Connected and Automated Transportation, 2023. https://rosap.ntl.bts.gov/view/dot/73486.
Feng, Yiheng, et al. Connected and Automated Vehicle (CAV) Testing Scenario Design and Implementation Using Naturalistic Driving Data and Augmented Reality. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. UMTRI-2023-6, ROSA P. https://rosap.ntl.bts.gov/view/dot/73486.
Reliable, lane-level, absolute position determination for connected and automated vehicles (CAV’s) is near at hand due to advances in sensor and computing technology. These capabilities in conjunction with high-definition maps enable lane determination, per lane queue determination, and enhanced performance in applications. This project investigate
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Farrell, J. A., Wu, G., Hu, W., Oswald, D., & Hao, P. (2023). Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications (Report No. NCST-UCR-RR-23-13). National Center for Sustainable Transportation (NCST) (UTC). https://doi.org/10.7922/G25T3HSS
Farrell, Jay A., Guoyuan Wu, Wang Hu, David Oswald, and Peng Hao. Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications. Report no. NCST-UCR-RR-23-13. National Center for Sustainable Transportation (NCST) (UTC), 2023. https://doi.org/10.7922/G25T3HSS.
Farrell, Jay A., et al. Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications. National Center for Sustainable Transportation (NCST) (UTC), 2023, Report no. NCST-UCR-RR-23-13, ROSA P. https://doi.org/10.7922/G25T3HSS.
Automated, connected, electric, and shared (ACES) technologies are rapidly evolving and will continue to impact the development of vehicles, infrastructure, communities, commerce, and the economy. Based on the Florida ACES transportation system roadmap for Florida developed in Phase I, this project aimed to engage with private industries to leverag
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Lin, P. S., Wang, Z., Li, Q., Lyu, H., Jackman, J., & Sipiora, A. M. (2023). Toward a Florida Automated, Connected, Electric, and Shared (ACES) Transportation System Roadmap: Phase II. Florida Department of Transportation. https://rosap.ntl.bts.gov/view/dot/74635
Lin, Pei-Sung, Zhenyu Wang, Qianwen Li, Huiqing Lyu, Jason Jackman, and Austin Marie Sipiora. Toward a Florida Automated, Connected, Electric, and Shared (ACES) Transportation System Roadmap: Phase II. Florida Department of Transportation, 2023. https://rosap.ntl.bts.gov/view/dot/74635.
Lin, Pei-Sung, et al. Toward a Florida Automated, Connected, Electric, and Shared (ACES) Transportation System Roadmap: Phase II. Florida Department of Transportation, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/74635.
NEC developed a Video Analytics implementation for traffic intersections using 5G technology. This implementation included both hardware infrastructure and software applications supporting 5G communications, which allows low latency and secure communications. The Virginia Tech Transportation Institute (VTTI) worked with NEC to facilitate the usage
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Vilela, J. P. T., Mollenhauer, M. A., White, E. E., & Miller, M. (2023). Private 5G Technology and Implementation Testing (Report No. VTTI-06-006). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/67588
Vilela, Jean Paul Talledo, Michael A Mollenhauer, Elizabeth E White, and Marty Miller. Private 5G Technology and Implementation Testing. Report no. VTTI-06-006. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/67588.
Vilela, Jean Paul Talledo, et al. Private 5G Technology and Implementation Testing. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. VTTI-06-006, ROSA P. https://rosap.ntl.bts.gov/view/dot/67588.
In this project, we developed an integrated solution for autonomous vehicle testing, in which the naturalistic driving environment (NDE) is combined with the augmented reality (AR) testing system. The integrated solution is implemented at American Center for Mobility (ACM). With the NDE, realistic virtual traffic flow can be generated in the testin
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Liu, H. (2023). Development of an Integrated Augmented Reality Testing Environment and Implementation at the American Center for Mobility (Report No. UMTRI-2023-2). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.7302/7019
Liu, Henry. Development of an Integrated Augmented Reality Testing Environment and Implementation at the American Center for Mobility. Report no. UMTRI-2023-2. University of Michigan. Center for Connected and Automated Transportation, 2023. https://doi.org/10.7302/7019.
Liu, Henry Development of an Integrated Augmented Reality Testing Environment and Implementation at the American Center for Mobility. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. UMTRI-2023-2, ROSA P. https://doi.org/10.7302/7019.
The objective of this methodology is to refine the preliminary results from previous work (11% fuel savings for one vehicle, one intersection) to an entire corridor of SPaT signals, with different CV market penetration, and with driver awareness of fuel savings benefits. The research will proceed in three parts. First, several vehicles will be inst
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Levin, M. W., Sun, Z., Wang, S., Sun, W., He, S., Suh, B., Zhao, G., Margolis, J., & Zamanpour, M. (2023). Cost/Benefit Analysis of Fuel-Efficient Speed Control Using Signal Phasing and Timing (SPaT) Data: Evaluation for Future Connected Corridor Deployment (Report No. MN 2023-06). Minnesota. Department of Transportation. Office of Research & Innovation. https://rosap.ntl.bts.gov/view/dot/67145
Levin, Michael W., Zongxuan Sun, Shi’an Wang, Wenbo Sun, Suiyi He, Bohoon Suh, Gaonan Zhao, Jacob Margolis, and Maziar Zamanpour. Cost/Benefit Analysis of Fuel-Efficient Speed Control Using Signal Phasing and Timing (SPaT) Data: Evaluation for Future Connected Corridor Deployment. Report no. MN 2023-06. Minnesota. Department of Transportation. Office of Research & Innovation, 2023. https://rosap.ntl.bts.gov/view/dot/67145.
Levin, Michael W., et al. Cost/Benefit Analysis of Fuel-Efficient Speed Control Using Signal Phasing and Timing (SPaT) Data: Evaluation for Future Connected Corridor Deployment. Minnesota. Department of Transportation. Office of Research & Innovation, 2023, Report no. MN 2023-06, ROSA P. https://rosap.ntl.bts.gov/view/dot/67145.
The goal of this project was to explore the fuel efficiencies of SPaT broadcasts interacting with CVs to control speed along connected corridors. The findings from this investigation could then be used to inform possible future SPaT deployments.
Rowe, D., & Levin, M. W. (2023). Using Signal Phasing and Timing Data to Reduce Fuel Use [Technical Summary] (Report No. 2023-06). Minnesota. Department of Transportation. Office of Research & Innovation. https://rosap.ntl.bts.gov/view/dot/67167
Rowe, Daniel and Michael W. Levin. Using Signal Phasing and Timing Data to Reduce Fuel Use [Technical Summary]. Report no. 2023-06. Minnesota. Department of Transportation. Office of Research & Innovation, 2023. https://rosap.ntl.bts.gov/view/dot/67167.
Rowe, Daniel, and Michael W. Levin Using Signal Phasing and Timing Data to Reduce Fuel Use [Technical Summary]. Minnesota. Department of Transportation. Office of Research & Innovation, 2023, Report no. 2023-06, ROSA P. https://rosap.ntl.bts.gov/view/dot/67167.
Firstly, to guarantee stability and robustness in the face of parametric uncertainties, non-linearities, and modeling errors, we have proposed a data-driven optimal control algorithm to solve the lane-changing problem of AVs which is inspired by reinforcement learning and adaptive dynamic programming. Secondly, we have developed a lane change decis
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Jiang, Z. P., Ozbay, K., Chakraborty, S., & Cui, L. (2023). Automated Lane Change and Robust Safety. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART). https://rosap.ntl.bts.gov/view/dot/67943
Jiang, Zhong-Ping, Kaan Ozbay, Sayantan Chakraborty, and Leilei Cui. Automated Lane Change and Robust Safety. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2023. https://rosap.ntl.bts.gov/view/dot/67943.
Jiang, Zhong-Ping, et al. Automated Lane Change and Robust Safety. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/67943.
Surface transportation systems (e.g., arterial roadways with signalized intersections) are inherently inefficient, particularly at higher traffic volumes. In general, both the infrastructure (e.g., traffic signals) and the vehicles operate independently, with little coordination between them. Previous research has shown that implementing strategies
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Hao, P., Oswald, D., Wu, G., & Barth, M. J. (2023). Eco-friendly Cooperative Traffic Optimization at Signalized Intersections (Report No. NCST-UCR-RR-23-09). University of California, Riverside. Center for Environmental Research and Technology. https://rosap.ntl.bts.gov/view/dot/66750
Hao, Peng, David Oswald, Guoyuan Wu, and Matthew J. Barth. Eco-friendly Cooperative Traffic Optimization at Signalized Intersections. Report no. NCST-UCR-RR-23-09. University of California, Riverside. Center for Environmental Research and Technology, 2023. https://rosap.ntl.bts.gov/view/dot/66750.
Hao, Peng, et al. Eco-friendly Cooperative Traffic Optimization at Signalized Intersections. University of California, Riverside. Center for Environmental Research and Technology, 2023, Report no. NCST-UCR-RR-23-09, ROSA P. https://rosap.ntl.bts.gov/view/dot/66750.
Age-related macular degeneration is a leading cause of blindness worldwide and is one of many limitations to independent driving among old adults. Highly autonomous vehicles present a prospective solution for those who are no longer capable of driving due to low vision. However, accessibility issues must be addressed to create a safe and pleasant e
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Bynum, L., Parker, J., Lee, K., Nitschke, N., LaFlam, M., Marcussen, J., Taleb, J., Dogan, A., Molnar, L. J., & Zhou, F. (2023). Navigating in the Dark – Designing Autonomous Driving Features to Assist Old Adults with Visual Impairments. Association for Computing Machinery. https://doi.org/10.48550/arXiv.2302.00499
Bynum, Lashawnda, Jay Parker, Kristy Lee, Nia Nitschke, Melanie LaFlam, Jennifer Marcussen, Jana Taleb, Aleyna Dogan, Lisa J Molnar, and Feng Zhou. Navigating in the Dark – Designing Autonomous Driving Features to Assist Old Adults with Visual Impairments. Association for Computing Machinery, 2023. https://doi.org/10.48550/arXiv.2302.00499.
Bynum, Lashawnda, et al. Navigating in the Dark – Designing Autonomous Driving Features to Assist Old Adults with Visual Impairments. Association for Computing Machinery, 2023, ROSA P. https://doi.org/10.48550/arXiv.2302.00499.
The many potentially transformative changes to the transportation system, such as automated vehicles, electric vehicle adoption, increased telework, and new travel modes, are creating increasing uncertainties for the future. These uncertainties call for fast, flexible models. System dynamics (SD) is emerging as a research modeling focus area for ch
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Smith, S., Rakoff, H., Eilbert, A., Shaw, J., & Berg, I. (2023). System Dynamics Models of Automated Vehicle Impacts (Report No. FHWA-JPO-22-985;DOT-VNTSC-FHWA-23-02). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/66972
Smith, Scott, Hannah Rakoff, Andrew Eilbert, Jingsi Shaw, and Ian Berg. System Dynamics Models of Automated Vehicle Impacts. Report no. FHWA-JPO-22-985;DOT-VNTSC-FHWA-23-02. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2023. https://rosap.ntl.bts.gov/view/dot/66972.
Smith, Scott, et al. System Dynamics Models of Automated Vehicle Impacts. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2023, Report no. FHWA-JPO-22-985;DOT-VNTSC-FHWA-23-02, ROSA P. https://rosap.ntl.bts.gov/view/dot/66972.
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