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.
United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology
2021-02-01
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This TechBrief provides a summary of a study the Federal Highway Administration (FHWA) conducted to evaluate how using three-dimensional (3D) engineered models in conjunction with Automated Machine Guidance (AMG) technology affects initial pavement smoothness. The assessment approach included a comprehensive literature review, engagement with State
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United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology (2021). Determination of Improved Pavement Smoothness When Using 3D Modeling and Automatic Machine Guidance [techbrief] (Report No. FHWA-HRT-21-021). United States. Federal Highway Administration. Office of Research, Development, and Technology. https://rosap.ntl.bts.gov/view/dot/54590
United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology. Determination of Improved Pavement Smoothness When Using 3D Modeling and Automatic Machine Guidance [techbrief]. Report no. FHWA-HRT-21-021. United States. Federal Highway Administration. Office of Research, Development, and Technology, 2021. https://rosap.ntl.bts.gov/view/dot/54590.
United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology Determination of Improved Pavement Smoothness When Using 3D Modeling and Automatic Machine Guidance [techbrief]. United States. Federal Highway Administration. Office of Research, Development, and Technology, 2021, Report no. FHWA-HRT-21-021, ROSA P. https://rosap.ntl.bts.gov/view/dot/54590.
Driving intelligence tests are 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 wou
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Feng, S., Yan, X., Sun, H., Feng, Y., & Liu, H. X. (2021). Intelligent Driving Intelligence Test for Autonomous Vehicles with Naturalistic and Adversarial Environment. Springer Nature. https://doi.org/10.1038/s41467-021-21007-8
Feng, Shuo, Xintao Yan, Haowei Sun, Yiheng Feng, and Henry X. Liu. Intelligent Driving Intelligence Test for Autonomous Vehicles with Naturalistic and Adversarial Environment. Springer Nature, 2021. https://doi.org/10.1038/s41467-021-21007-8.
Feng, Shuo, et al. Intelligent Driving Intelligence Test for Autonomous Vehicles with Naturalistic and Adversarial Environment. Springer Nature, 2021, ROSA P. https://doi.org/10.1038/s41467-021-21007-8.
Advanced vehicle technologies are increasingly more accessible and available in vehicles. These current and future systems, despite promising added safety, convenience, and efficiency to drivers and road users, have an inherently higher level of complexity than the driving systems that most drivers are used to operating. In order to maximize the pr
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Pradhan, A. K., Pai, G., Knodler, M. A., Fitzpatrick, C., & Horrey, W. J. (2021). Driver’s Mental Models of Advanced Vehicle Technologies: A Proposed Framework for Identifying and Predicting Operator Errors. Safety Research Using Simulation (SAFER-SIM) University Transportation Center. https://rosap.ntl.bts.gov/view/dot/60329
Pradhan, Anuj K., Ganesh Pai, Michael A. Knodler, Cole Fitzpatrick, and William J Horrey. Driver’s Mental Models of Advanced Vehicle Technologies: A Proposed Framework for Identifying and Predicting Operator Errors. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2021. https://rosap.ntl.bts.gov/view/dot/60329.
Pradhan, Anuj K., et al. Driver’s Mental Models of Advanced Vehicle Technologies: A Proposed Framework for Identifying and Predicting Operator Errors. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/60329.
This study fills the gap in the limited research on the effect of emerging Automated Vehicle (AV) technology on infrastructure standards. The main objective of this research is to evaluate implications of an innovative infrastructure solution, exclusive AV lanes, for safe and efficient integration of AVs into an existing transportation system. Exam
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Machiani, S. G., Jahangiri, A., Melendez, B., Katthe, A., Hasani, M., Ahmadi, A., & Musial, W. B. (2021). Safety Impact Evaluation of a Narrow-Automated Vehicle-Exclusive Reversible Lane on an Existing Smart Freeway (Report No. 04-101). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/56136
Machiani, Sahar Ghanipoor, Arash Jahangiri, Benjamin Melendez, Anagha Katthe, Mahdie Hasani, Alidad Ahmadi, and Walter B Musial. Safety Impact Evaluation of a Narrow-Automated Vehicle-Exclusive Reversible Lane on an Existing Smart Freeway. Report no. 04-101. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021. https://rosap.ntl.bts.gov/view/dot/56136.
Machiani, Sahar Ghanipoor, et al. Safety Impact Evaluation of a Narrow-Automated Vehicle-Exclusive Reversible Lane on an Existing Smart Freeway. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021, Report no. 04-101, ROSA P. https://rosap.ntl.bts.gov/view/dot/56136.
This report explores how older adults may be affected by the introduction of automated vehicles (AVs). It focuses on highly automated Level 4 AVs, self-driving vehicles that require no human supervision as long as certain conditions are met. It is based on information gathered from a literature review, interviews with subject matter experts, and a
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Fraade-Blanar, L., Larco, N., Best, R., Swift, T., & Blumenthal, M. S. (2021). Older Adults, New Mobility, and Automated Vehicles (Report No. AARP Research Report 2021-28). University of Oregon, Urbanism Next Center. https://rosap.ntl.bts.gov/view/dot/60303
Fraade-Blanar, Laura, Nico Larco, Ryan Best, Tiffany Swift, and Marjory S Blumenthal. Older Adults, New Mobility, and Automated Vehicles. Report no. AARP Research Report 2021-28. University of Oregon, Urbanism Next Center, 2021. https://rosap.ntl.bts.gov/view/dot/60303.
Fraade-Blanar, Laura, et al. Older Adults, New Mobility, and Automated Vehicles. University of Oregon, Urbanism Next Center, 2021, Report no. AARP Research Report 2021-28, ROSA P. https://rosap.ntl.bts.gov/view/dot/60303.
Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. In the context of AVs, prior studies have observed that major news events such as crashes and
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McDonald, T., Huang, B., Wei, R., Alambeigi, H., Arachie, C., Smith, A., & Jefferson, J. (2021). Data Mining Twitter To Improve Automated Vehicle Safety (Report No. 04-098). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/56364
McDonald, Tony, Bert Huang, Ran Wei, Hananeh Alambeigi, Chidubem Arachie, Alec Smith, and Jacelyn Jefferson. Data Mining Twitter To Improve Automated Vehicle Safety. Report no. 04-098. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021. https://rosap.ntl.bts.gov/view/dot/56364.
McDonald, Tony, et al. Data Mining Twitter To Improve Automated Vehicle Safety. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021, Report no. 04-098, ROSA P. https://rosap.ntl.bts.gov/view/dot/56364.
This report presents an analysis of the potential macroeconomic impacts resulting from the adoption of higher level automated driving systems (ADS) of the long-haul trucking industry in the United States. The analysis uses USAGE-Hwy, a computable general equilibrium (CGE) model of the U.S. economy that includes detail on transportation related indu
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Waschik, R., Friedman, D., Taylor, C., & Boatner, J. (2021). Macroeconomic Impacts of Automated Driving Systems in Long-Haul Trucking (Report No. DOT-VNTSC-FHWA-20-16;FHWA-JPO-21-847). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/54596
Waschik, Robert, Daniel Friedman, Catherine Taylor, and Jasmine Boatner. Macroeconomic Impacts of Automated Driving Systems in Long-Haul Trucking. Report no. DOT-VNTSC-FHWA-20-16;FHWA-JPO-21-847. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2021. https://rosap.ntl.bts.gov/view/dot/54596.
Waschik, Robert, et al. Macroeconomic Impacts of Automated Driving Systems in Long-Haul Trucking. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2021, Report no. DOT-VNTSC-FHWA-20-16;FHWA-JPO-21-847, ROSA P. https://rosap.ntl.bts.gov/view/dot/54596.
Commuting congestion increases alongside the prosperity of urban cities. With the rapid development of ride sourcing services and the advances of the connected and automated vehicles (CAV), researchers are seeking innovative approaches to alleviate commuting congestion by integrating CAV-based ride sourcing and transit services. We propose a genera
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DatasetSupporting Files
Fan, R., MacCabe, D., & Ban, X. (. (2021). A General Equilibrium Model for Integrated CAV Ridesourcing and Transit Services for the Morning Commute [supporting datasets]. University of Washington. Department of Civil and Environmental Engineering. https://doi.org/10.5281/zenodo.6377177
Fan, Rong, Dan MacCabe, and Xuegang (Jeff) Ban. A General Equilibrium Model for Integrated CAV Ridesourcing and Transit Services for the Morning Commute [supporting datasets]. University of Washington. Department of Civil and Environmental Engineering, 2021. https://doi.org/10.5281/zenodo.6377177.
Fan, Rong, et al. A General Equilibrium Model for Integrated CAV Ridesourcing and Transit Services for the Morning Commute [supporting datasets]. University of Washington. Department of Civil and Environmental Engineering, 2021, ROSA P. https://doi.org/10.5281/zenodo.6377177.
This report summarizes the results of a one-year project aimed at exploiting vehicle-to-infrastructure (V2I) communication to enhance the effectiveness of real-time adaptive traffic signal control systems. As originally formulated, the project’s goal was to explore the potential of using the sensing capabilities of connected autonomous vehicles (CA
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Smith, S. F., & Hawkes, A. (2021). Integration of Automated Vehicle Sensing with Adaptive Signal Control for Enhanced Mobility. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/58658
Smith, Stephen F. and Allen Hawkes. Integration of Automated Vehicle Sensing with Adaptive Signal Control for Enhanced Mobility. Mobility21, Carnegie Mellon University, 2021. https://rosap.ntl.bts.gov/view/dot/58658.
Smith, Stephen F., and Allen Hawkes Integration of Automated Vehicle Sensing with Adaptive Signal Control for Enhanced Mobility. Mobility21, Carnegie Mellon University, 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/58658.
Included in this second volume are 18 Federal Motor Vehicle Safety Standards (FMVSS) research findings, including the performance requirements and test procedures, in terms of options regarding technical translations, based on potential regulatory barriers identified for compliance verification of innovative new vehicle designs that may appear in v
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Chaka, M., Blanco, M., Stowe, L., McNeil, J., Kefauver, K., Fitchett, V., Fitzgerald, K. E., Trimble, T. E., Kizyma, D., Neurauter, L., Hardy, W. N., Anderson, G. T., Schultz, J., Thorn, E., Harper, C., & Weinstein, K. (2021). FMVSS Considerations for Vehicles with Automated Driving Systems: Volume 2 (Report No. DOT HS 813 024). United States. Department of Transportation. National Highway Traffic Safety Administration. https://doi.org/10.21949/1530204
Chaka, Michelle, Myra Blanco, L. Stowe, Joshua McNeil, Kevin Kefauver, Vikki Fitchett, and Kaitlyn E Fitzgerald, et al.. FMVSS Considerations for Vehicles with Automated Driving Systems: Volume 2. Report no. DOT HS 813 024. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021. https://doi.org/10.21949/1530204.
Chaka, Michelle, et al. FMVSS Considerations for Vehicles with Automated Driving Systems: Volume 2. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021, Report no. DOT HS 813 024, ROSA P. https://doi.org/10.21949/1530204.
Adaptive cruise control (ACC) is a longitudinal control system through which a vehicle can automatically maintain a driver-selected speed and, through the use of radar or light detection and ranging sensors, a preselected gap between itself and a slower-moving vehicle ahead.(1) ACC is marketed as a convenience system that reduces stress and workloa
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Weaver, S., Roldan, S. M., & Gonzalez, T. B. (2021). The Effects of Vehicle Automation on Driver Engagement: The Case of Adaptive Cruise Control and Mind Wandering [techbrief] (Report No. FHWA-HRT-21-017). United States. Federal Highway Administration. Office of Research, Development, and Technology. https://rosap.ntl.bts.gov/view/dot/54589
Weaver, Starla, Stephanie M. Roldan, and Tracy B. Gonzalez. The Effects of Vehicle Automation on Driver Engagement: The Case of Adaptive Cruise Control and Mind Wandering [techbrief]. Report no. FHWA-HRT-21-017. United States. Federal Highway Administration. Office of Research, Development, and Technology, 2021. https://rosap.ntl.bts.gov/view/dot/54589.
Weaver, Starla, et al. The Effects of Vehicle Automation on Driver Engagement: The Case of Adaptive Cruise Control and Mind Wandering [techbrief]. United States. Federal Highway Administration. Office of Research, Development, and Technology, 2021, Report no. FHWA-HRT-21-017, ROSA P. https://rosap.ntl.bts.gov/view/dot/54589.
This report focuses on how cities can use climate action plans (CAPs) to ensure that on-demand mobility and autonomous vehicles (AVs) help reduce, rather than increase, green-house gas (GHG) emissions and inequitable impacts from the transportation system. The authors employed a three-pronged research strategy involving: (1) an analysis of the curr
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Alexander, S., Agrawal, A. W., & Clark, B. Y. (2021). Local Climate Action Planning as a Tool to Harness the Greenhouse Gas Emissions Mitigation and Equity Potential of Autonomous Vehicles and On-Demand Mobility (Report No. 20-41, CA-MTI-1818). Mineta Transportation Institute. https://doi.org/10.31979/mti.2020.1818
Alexander, Serena, Asha Weinstein Agrawal, and Benjamin Y Clark. Local Climate Action Planning as a Tool to Harness the Greenhouse Gas Emissions Mitigation and Equity Potential of Autonomous Vehicles and On-Demand Mobility. Report no. 20-41, CA-MTI-1818. Mineta Transportation Institute, 2021. https://doi.org/10.31979/mti.2020.1818.
Alexander, Serena, et al. Local Climate Action Planning as a Tool to Harness the Greenhouse Gas Emissions Mitigation and Equity Potential of Autonomous Vehicles and On-Demand Mobility. Mineta Transportation Institute, 2021, Report no. 20-41, CA-MTI-1818, ROSA P. https://doi.org/10.31979/mti.2020.1818.
The main objective of this study is to evaluate the safety and operational impacts of an innovative infrastructure solution for safe and efficient integration of Automated Vehicle (AV) as an emerging technology into an existing transportation system. Filling the gap in the limited research on the effect of AV technology on infrastructure standards,
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Machiani, S. G., Ahmadi, A., Musial, W. B., Katthe, A., Melendez, B., & Jahangiri, A. (2021). Implications of a Narrow Automated Vehicle-Exclusive Lane on Interstate 15 Express Lanes. Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/60215
Machiani, Sahar Ghanipoor, Alidad Ahmadi, Walter B Musial, Anagha Katthe, Benjamin Melendez, and Arash Jahangiri. Implications of a Narrow Automated Vehicle-Exclusive Lane on Interstate 15 Express Lanes. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021. https://rosap.ntl.bts.gov/view/dot/60215.
Machiani, Sahar Ghanipoor, et al. Implications of a Narrow Automated Vehicle-Exclusive Lane on Interstate 15 Express Lanes. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/60215.
Despite growing interest in low-speed automated shuttles, pilot deployments have only just begun in a few places in the U.S., and there is a lack of studies that estimate the impacts of a widespread deployment of automated shuttles designed to supplement existing transit networks. This project estimated the potential impacts of automated shuttles b
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Hsueh, G., Czerwinski, D., Poliziani, C., Becker, T., Hughes, A., Chen, P., & Benn, M. (2021). Using Beam Software To Simulate the Introduction of On-Demand, Automated, and Electric Shuttles for Last Mile Connectivity in Santa Clara County (Report No. 20-47). Mineta Transportation Institute. https://doi.org/10.31979/mti.2021.1822
Hsueh, Gary, David Czerwinski, Cristian Poliziani, Terris Becker, Alexandre Hughes, Peter Chen, and Melissa Benn. Using Beam Software To Simulate the Introduction of On-Demand, Automated, and Electric Shuttles for Last Mile Connectivity in Santa Clara County. Report no. 20-47. Mineta Transportation Institute, 2021. https://doi.org/10.31979/mti.2021.1822.
Hsueh, Gary, et al. Using Beam Software To Simulate the Introduction of On-Demand, Automated, and Electric Shuttles for Last Mile Connectivity in Santa Clara County. Mineta Transportation Institute, 2021, Report no. 20-47, ROSA P. https://doi.org/10.31979/mti.2021.1822.
Data communication links are essential components of the ecosystem of intelligent vehicles, enabling autonomous and semi-autonomous driving. Maintaining a reliable, low latency communication link has been a topic of interest in research and product development. The driving force of these developments is a better awareness of the wider surroundings
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Molisch, A. F. (2021). Measurement and Modeling of Broadband Millimeter-Wave Signal Propagation Between Intelligent Vehicles [Research Brief] (Report No. PSR-18-08). Pacific Southwest Region 9 UTC, University of Southern California. https://rosap.ntl.bts.gov/view/dot/68067
Molisch, Andreas F. Measurement and Modeling of Broadband Millimeter-Wave Signal Propagation Between Intelligent Vehicles [Research Brief]. Report no. PSR-18-08. Pacific Southwest Region 9 UTC, University of Southern California, 2021. https://rosap.ntl.bts.gov/view/dot/68067.
Molisch, Andreas F Measurement and Modeling of Broadband Millimeter-Wave Signal Propagation Between Intelligent Vehicles [Research Brief]. Pacific Southwest Region 9 UTC, University of Southern California, 2021, Report no. PSR-18-08, ROSA P. https://rosap.ntl.bts.gov/view/dot/68067.
Communications between vehicles is one of the most important prerequisites for inter-vehicle coordination, a key feature needed to enable autonomous and semi-autonomous driving, from an efficiency, convenience and safety perspective. Such communication will have to be done with very low latency, which lead the industry into looking at mm-wave bands
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Molisch, A. F., Hammoud, H., & Zhang, Y. (2021). Measurement and Modeling of Broadband Millimeter-Wave Signal Propagation Between Intelligent Vehicles (Report No. PSR-18-08). METRANS Transportation Center (Calif.). https://rosap.ntl.bts.gov/view/dot/56363
Molisch, Andreas F, Hussein Hammoud, and Yuning Zhang. Measurement and Modeling of Broadband Millimeter-Wave Signal Propagation Between Intelligent Vehicles. Report no. PSR-18-08. METRANS Transportation Center (Calif.), 2021. https://rosap.ntl.bts.gov/view/dot/56363.
Molisch, Andreas F, et al. Measurement and Modeling of Broadband Millimeter-Wave Signal Propagation Between Intelligent Vehicles. METRANS Transportation Center (Calif.), 2021, Report no. PSR-18-08, ROSA P. https://rosap.ntl.bts.gov/view/dot/56363.
Automated vehicles (AVs) promise to revolutionize driving safety. Driver models can aid in achieving this promise by providing a tool for designers to ensure safe interactions between human drivers and AVs. In this project, we performed a literature review to identify important factors for AV takeover safety and promising models to capture these fa
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McDonald, A. D., Sarkar, A., Hickman, J. S., Alambeigi, H., Markkula, G., & Vogelpohl, T. (2021). Modeling Driver Behavior during Automated Vehicle Platooning Failures (Report No. 03-036). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/59881
McDonald, Anthony D, Abhijit Sarkar, Jeffrey S. Hickman, Hananeh Alambeigi, Gustav Markkula, and Tobias Vogelpohl. Modeling Driver Behavior during Automated Vehicle Platooning Failures. Report no. 03-036. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021. https://rosap.ntl.bts.gov/view/dot/59881.
McDonald, Anthony D, et al. Modeling Driver Behavior during Automated Vehicle Platooning Failures. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021, Report no. 03-036, ROSA P. https://rosap.ntl.bts.gov/view/dot/59881.
Using experience from working on the Knight Autonomous Vehicle (AV) Initiative, Urbanism Next created this white paper to provide a foundation for public sector agencies to approach autonomous vehicle deployment and policy with a focus on equity. This report outlines ways that public agencies can identify community needs and shape deployment to ens
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Steckler, B., Howell, A., & Larco, N. (2021). A Framework for Shaping the Deployment of Autonomous Vehicles and Advancing Equity Outcomes. University of Oregon, Urbanism Next Center. https://rosap.ntl.bts.gov/view/dot/60206
Steckler, Becky, Amanda Howell, and Nico Larco. A Framework for Shaping the Deployment of Autonomous Vehicles and Advancing Equity Outcomes. University of Oregon, Urbanism Next Center, 2021. https://rosap.ntl.bts.gov/view/dot/60206.
Steckler, Becky, et al. A Framework for Shaping the Deployment of Autonomous Vehicles and Advancing Equity Outcomes. University of Oregon, Urbanism Next Center, 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/60206.
Monthly reports on Arlington RAPID project which integrates a shared, dynamically routed automated vehicle (AV) fleet into an existing public rideshare system in Arlington, Texas.
The development of automated trucking technology is progressing rapidly, and increasing numbers of on-road pilots suggest that full-scale commercial deployment of partially automated truck platoons on public roads is forthcoming.(1) In the United States, platoons typically consist of two to four trucks equipped with cooperative adaptive cruise cont
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Roldan, S. M., & Gonzalez, T. B. (2021). Effective Indicators of Partially Automated Truck Platooning [techbrief] (Report No. FHWA-HRT-21-016). United States. Federal Highway Administration. Office of Research, Development, and Technology. https://rosap.ntl.bts.gov/view/dot/54588
Roldan, Stephanie M. and Tracy B. Gonzalez. Effective Indicators of Partially Automated Truck Platooning [techbrief]. Report no. FHWA-HRT-21-016. United States. Federal Highway Administration. Office of Research, Development, and Technology, 2021. https://rosap.ntl.bts.gov/view/dot/54588.
Roldan, Stephanie M., and Tracy B. Gonzalez Effective Indicators of Partially Automated Truck Platooning [techbrief]. United States. Federal Highway Administration. Office of Research, Development, and Technology, 2021, Report no. FHWA-HRT-21-016, ROSA P. https://rosap.ntl.bts.gov/view/dot/54588.
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