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.
In this research, we draw on our ongoing partnerships with the Amalgamated Transit Union, the Transport Workers Union, and the AFL-CIO Technology Institute to understand the role of bus operators within the context of increased transit automation. Our early work together involved a group interview study of working bus operators across the United St
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Fox, S., & Martelaro, N. (2024). Co-Designing Safety-Enhancing ADAS with Transit Operators. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/78034
Fox, Sarah and Nikolas Martelaro. Co-Designing Safety-Enhancing ADAS with Transit Operators. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/78034.
Fox, Sarah, and Nikolas Martelaro Co-Designing Safety-Enhancing ADAS with Transit Operators. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/78034.
Technology transfer is a vital component of the U.S. DOT University Transportation Center (UTC) program, ensuring that research findings are communicated to practitioners, policymakers, educators, and the general public. By translating technical insights into accessible and actionable information, technology transfer supports the implementation of
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Michalaka, D., Brown, K. T., & Davis, W. J. (2024). Safety and Health Impacts of Mobility Alternatives Technology Transfer. Center for Connected Multimodal Mobility, Clemson University. https://rosap.ntl.bts.gov/view/dot/83898
Michalaka, Dimitra, Kweku T Brown, and William J. Davis. Safety and Health Impacts of Mobility Alternatives Technology Transfer. Center for Connected Multimodal Mobility, Clemson University, 2024. https://rosap.ntl.bts.gov/view/dot/83898.
Michalaka, Dimitra, et al. Safety and Health Impacts of Mobility Alternatives Technology Transfer. Center for Connected Multimodal Mobility, Clemson University, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/83898.
This report aims to investigate and expand on five essential research areas related to connected and autonomous vehicle (CAV) testbeds and their contribution to enhancing road safety, especially for vulnerable road users. These areas include validating LiDAR data with CCTV systems, investigating CAV testbeds across the country, real-time communicat
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Jeihani, M., Yang, D., Ardeshiri, A., & Ansariyar, A. (2024). Smart, Green, Equitable, Safe, Complete Streets for All - Phase I: Development of a CAV Testbed-enhanced Smart Campus at Morgan State University (Report No. SM02). Morgan State University. Sustainable Mobility and Accessibility Regional Transportation Equity Research Center (SMARTER) Region 3 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/79008
Jeihani, Mansoureh, Di Yang, Anam Ardeshiri, and Alireza Ansariyar. Smart, Green, Equitable, Safe, Complete Streets for All - Phase I: Development of a CAV Testbed-enhanced Smart Campus at Morgan State University. Report no. SM02. Morgan State University. Sustainable Mobility and Accessibility Regional Transportation Equity Research Center (SMARTER) Region 3 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/79008.
Jeihani, Mansoureh, et al. Smart, Green, Equitable, Safe, Complete Streets for All - Phase I: Development of a CAV Testbed-enhanced Smart Campus at Morgan State University. Morgan State University. Sustainable Mobility and Accessibility Regional Transportation Equity Research Center (SMARTER) Region 3 University Transportation Center (UTC), 2024, Report no. SM02, ROSA P. https://rosap.ntl.bts.gov/view/dot/79008.
Connected autonomous vehicle (CAV) technology has the potential to enable significant gains in energy economy (EE). Much research attention has been focused on autonomous eco-driving control enabled by various methods. In this study, the state of the literature on autonomous eco-driving control is reviewed, an overall system’s description of eco-dr
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Rabinowitz, A. I., Ang, C. C., Mahmoud, Y. H., Araghi, F. M., Meyer, R. T., Kolmanovsky, I., Asher, Z. D., & Bradley, T. (2024). Real-Time Implementation Comparison of Urban Eco-Driving Controls (Report No. MPC-570). Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/78894
Rabinowitz, Aaron I, Chon Chia Ang, Yara Hazem Mahmoud, Farhang Motallebi Araghi, Richard T Meyer, Ilya Kolmanovsky, Zachary D. Asher, and Thomas Bradley. Real-Time Implementation Comparison of Urban Eco-Driving Controls. Report no. MPC-570. Mountain-Plains Consortium, 2024. https://rosap.ntl.bts.gov/view/dot/78894.
Rabinowitz, Aaron I, et al. Real-Time Implementation Comparison of Urban Eco-Driving Controls. Mountain-Plains Consortium, 2024, Report no. MPC-570, ROSA P. https://rosap.ntl.bts.gov/view/dot/78894.
In the vicinity of weaving areas, freeway congestion is nearly unavoidable due to their negative effects on the continuous freeway mainline flow. The adverse impacts include increased collision risks, extended travel time, and excessive emissions and fuel consumption. Dynamic Speed Harmonization (DSH), which is also known as Variable Speed Limit (V
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Fan, W., & Hua, C. (2024). Dynamic Coordinated Speed Control and Synergistic Performance Evaluation in Connected and Automated Vehicle Environment (Report No. 2022 Project 16). University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education. https://rosap.ntl.bts.gov/view/dot/77809
Fan, Wei and Chengying Hua. Dynamic Coordinated Speed Control and Synergistic Performance Evaluation in Connected and Automated Vehicle Environment. Report no. 2022 Project 16. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2024. https://rosap.ntl.bts.gov/view/dot/77809.
Fan, Wei, and Chengying Hua Dynamic Coordinated Speed Control and Synergistic Performance Evaluation in Connected and Automated Vehicle Environment. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2024, Report no. 2022 Project 16, ROSA P. https://rosap.ntl.bts.gov/view/dot/77809.
This study delves into the energy and emissions impacts of Shared Autonomous and Electric Vehicles (SAEVs) on disadvantaged communities in California. It explores the intersection of evolving transportation technologies—electric, autonomous, and shared mobility—and their implications for equity, energy consumption, and emissions. Through high-resol
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Li, X., & Jenn, A. (2024). Mobility, Energy, and Emissions Impacts of SAEVs to Disadvantaged Communities in California (Report No. NCST-UCD-RR-24-21). National Center for Sustainable Transportation (NCST) (UTC). https://doi.org/10.7922/G28050XS
Li, Xinwei and Alan Jenn. Mobility, Energy, and Emissions Impacts of SAEVs to Disadvantaged Communities in California. Report no. NCST-UCD-RR-24-21. National Center for Sustainable Transportation (NCST) (UTC), 2024. https://doi.org/10.7922/G28050XS.
Li, Xinwei, and Alan Jenn Mobility, Energy, and Emissions Impacts of SAEVs to Disadvantaged Communities in California. National Center for Sustainable Transportation (NCST) (UTC), 2024, Report no. NCST-UCD-RR-24-21, ROSA P. https://doi.org/10.7922/G28050XS.
Connected autonomous vehicle (CAV) technology has the potential to enable significant gains in energy economy (EE). Much research attention has been focused on autonomous eco-driving control enabled by various methods. In this study, the state of the literature on autonomous eco-driving control is reviewed, an overall system’s description of eco-dr
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Bradley, T. (2024). Real-Time Implementation Comparison of Urban Eco-Driving Controls [Brief] (Report No. MPC 24-558 (project 570)). Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/78895
In this work, we introduce a dataset called DrivingContexts for the detection of relevant driving contexts for autonomous driving. Additionally, we propose the use of vision language models such as LLaVa and ViLT with zero-shot and few-shot approaches, to solve the problem of detecting such contexts. With our approach, we reduce the need for fully
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Dataset
Rajkumar, R. (., Sahu, N., & Sural, S. (2024). ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models [IEEE ITSC 2024] [supporting dataset]. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/87498
Rajkumar, Ragunathan (Raj), Nishad Sahu, and Shounak Sural. ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models [IEEE ITSC 2024] [supporting dataset]. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/87498.
Rajkumar, Ragunathan (Raj), et al. ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models [IEEE ITSC 2024] [supporting dataset]. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/87498.
Based on the traffic light prediction and trajectory optimization techniques for a stream of CAVs, the proposed congestion-reducing scheme can increase the throughput of the transportation network by attenuating the deceleration and acceleration of vehicles before the signalized intersections, accompanied by decreased fuel consumption and emissions
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DatasetSupporting Files
Jiang, Z. P., Ozbay, K., & Li, X. (2024). Traffic Light Prediction and Trajectory Planning for Congestion Reduction [supporting dataset]. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/86351
Jiang, Zhong-Ping, Kaan Ozbay, and Xianning Li. Traffic Light Prediction and Trajectory Planning for Congestion Reduction [supporting dataset]. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/86351.
Jiang, Zhong-Ping, et al. Traffic Light Prediction and Trajectory Planning for Congestion Reduction [supporting dataset]. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/86351.
Based on the traffic light prediction and trajectory optimization techniques for a stream of CAVs, the proposed congestion-reducing scheme can increase the throughput of the transportation network by attenuating the deceleration and acceleration of vehicles before the signalized intersections, accompanied by decreased fuel consumption and emissions
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DatasetSupporting Files
Li, X. (2024). Data-Driven Combined Longitudinal and Lateral Control for the Car Following Problem [supporting dataset]. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/86350
Li, Xianning. Data-Driven Combined Longitudinal and Lateral Control for the Car Following Problem [supporting dataset]. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/86350.
Li, Xianning Data-Driven Combined Longitudinal and Lateral Control for the Car Following Problem [supporting dataset]. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/86350.
Based on the traffic light prediction and trajectory optimization techniques for a stream of CAVs, the proposed congestion-reducing scheme can increase the throughput of the transportation network by attenuating the deceleration and acceleration of vehicles before the signalized intersections, accompanied by decreased fuel consumption and emissions
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Jiang, Z. P., Ozbay, K., & Li, X. (2024). Control of Connected and Autonomous Vehicles for Congestion Reduction in Mixed Traffic: A Learning-Based Approach. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/86345
Jiang, Zhong-Ping, Kaan Ozbay, and Xianning Li. Control of Connected and Autonomous Vehicles for Congestion Reduction in Mixed Traffic: A Learning-Based Approach. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/86345.
Jiang, Zhong-Ping, et al. Control of Connected and Autonomous Vehicles for Congestion Reduction in Mixed Traffic: A Learning-Based Approach. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/86345.
Based on the traffic light prediction and trajectory optimization techniques for a stream of CAVs, the proposed congestion-reducing scheme can increase the throughput of the transportation network by attenuating the deceleration and acceleration of vehicles before the signalized intersections, accompanied by decreased fuel consumption and emissions
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DatasetSupporting Files
Jiang, Z. P., Ozbay, K., & Li, X. (2024). SUMO Simulation, Validation Using Real-World Trajectory Data [supporting datasets]. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/86352
Jiang, Zhong-Ping, Kaan Ozbay, and Xianning Li. SUMO Simulation, Validation Using Real-World Trajectory Data [supporting datasets]. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/86352.
Jiang, Zhong-Ping, et al. SUMO Simulation, Validation Using Real-World Trajectory Data [supporting datasets]. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/86352.
This study investigates the impact of semi-automated vehicle (SAV) systems, specifically Adaptive Cruise Control (ACC), on driver behavior and control transitions (CT). The variations and adaptations in driver performance due to mental workload, influenced by factors such as task difficulty and driver awareness, are critical, especially under diffe
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Kondyli, A., Lane, B. W., & Masud, S. S. B. (2024). Investigation of Driver Adaptations in a Mixed Traffic Environment (Report No. 25-1121-0005-153-4). Mid-America Transportation Center for Transportation Safety and Equity (MATC-TSE) Region 7 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77889
Kondyli, Alexandra, Bradley W Lane, and Saumik Sakib Bin Masud. Investigation of Driver Adaptations in a Mixed Traffic Environment. Report no. 25-1121-0005-153-4. Mid-America Transportation Center for Transportation Safety and Equity (MATC-TSE) Region 7 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77889.
Kondyli, Alexandra, et al. Investigation of Driver Adaptations in a Mixed Traffic Environment. Mid-America Transportation Center for Transportation Safety and Equity (MATC-TSE) Region 7 University Transportation Center (UTC), 2024, Report no. 25-1121-0005-153-4, ROSA P. https://rosap.ntl.bts.gov/view/dot/77889.
Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service. However, this control strategy generally causes adverse effects on other traffic, which limits its widespread adoption. The development of Connected Vehicle (CV) technology enables the real-time acquisit
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Fan, W., & Yang, T. (2024). Transit Signal Priority Control With Connected Vehicle Technology: Deep Reinforcement Learning Approach (Report No. 2022 Project 17). University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education. https://rosap.ntl.bts.gov/view/dot/77807
Fan, Wei and Tianjia Yang. Transit Signal Priority Control With Connected Vehicle Technology: Deep Reinforcement Learning Approach. Report no. 2022 Project 17. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2024. https://rosap.ntl.bts.gov/view/dot/77807.
Fan, Wei, and Tianjia Yang Transit Signal Priority Control With Connected Vehicle Technology: Deep Reinforcement Learning Approach. University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2024, Report no. 2022 Project 17, ROSA P. https://rosap.ntl.bts.gov/view/dot/77807.
Bridge collapses, road closures, disruptions in the public transportation system, and major issues caused by autonomous vehicles (AVs) are everyday realities of our transportation infrastructure that not only cause inconvenience to the public but also constitute a major safety concern. When a particular component of the transportation system fails
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Yagan, O., Joe-Wong, C., & Lin, I. C. (2024). Mitigating Cascading Failures for Safety in Transportation Networks in the Era of Autonomous Vehicles. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77510
Yagan, Osman, Carlee Joe-Wong, and I-Cheng Lin. Mitigating Cascading Failures for Safety in Transportation Networks in the Era of Autonomous Vehicles. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77510.
Yagan, Osman, et al. Mitigating Cascading Failures for Safety in Transportation Networks in the Era of Autonomous Vehicles. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/77510.
The Pennsylvania Safety Transportation and Research Track (PennSTART) and the Connected Deployment Corridor aim to revolutionize the testing and deployment of emerging transportation technologies, such as autonomous (AVs) and electric vehicles (EVs). As we navigate this transformative period in transportation, the safety of these technologies remai
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Jiang, E., Krishnamurthy, H. M., Nguyen, H., Hao, H., Miao, Y., & Zhang, P. (2024). The PennSTART Safety Standards Project: Current Safety Standards and Test Track Designs for Connected and Autonomous Vehicles (Report No. 467). Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77712
Jiang, Elaine, Haarish Mummidi Krishnamurthy, Hannah Nguyen, Hao Hao, Yidi Miao, and Peter Zhang. The PennSTART Safety Standards Project: Current Safety Standards and Test Track Designs for Connected and Autonomous Vehicles. Report no. 467. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77712.
Jiang, Elaine, et al. The PennSTART Safety Standards Project: Current Safety Standards and Test Track Designs for Connected and Autonomous Vehicles. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, Report no. 467, ROSA P. https://rosap.ntl.bts.gov/view/dot/77712.
There is considerable concern about the induced demand implications of the advent of automated vehicles. In an automated vehicle future, drivers and passengers are relieved of the driving task, thus rendering car travel more convenient and less onerous. As such, there is the possibility that people will undertake more trips in an automated vehicle
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Batur, I., & Pendyala, R. M. (2024). The Induced Demand Implications of Alternative Adoption Modalities of Automated Vehicles. Center for Teaching Old Models New Tricks (TOMNET). https://rosap.ntl.bts.gov/view/dot/77641
Batur, Irfan and Ram M. Pendyala. The Induced Demand Implications of Alternative Adoption Modalities of Automated Vehicles. Center for Teaching Old Models New Tricks (TOMNET), 2024. https://rosap.ntl.bts.gov/view/dot/77641.
Batur, Irfan, and Ram M. Pendyala The Induced Demand Implications of Alternative Adoption Modalities of Automated Vehicles. Center for Teaching Old Models New Tricks (TOMNET), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/77641.
This report aims to investigate and expand on five essential research areas related to connected and autonomous vehicle (CAV) testbeds and their contribution to enhancing road safety, especially for vulnerable road users. These areas include validating LiDAR data with CCTV systems, investigating CAV testbeds across the country, real-time communicat
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Jeihani, M., Yang, D., Ardeshiri, A., & Ansariyar, A. (2024). Development of a CAV Testbed-enhanced Smart Campus at Morgan State University – Phase 1 (Report No. SM02). Morgan State University.Safety and Mobility Advancement Regional Transportation and Economics Research Center (SMARTER) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/86277
Jeihani, Mansoureh, Di Yang, Anam Ardeshiri, and Alireza Ansariyar. Development of a CAV Testbed-enhanced Smart Campus at Morgan State University – Phase 1. Report no. SM02. Morgan State University.Safety and Mobility Advancement Regional Transportation and Economics Research Center (SMARTER) University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/86277.
Jeihani, Mansoureh, et al. Development of a CAV Testbed-enhanced Smart Campus at Morgan State University – Phase 1. Morgan State University.Safety and Mobility Advancement Regional Transportation and Economics Research Center (SMARTER) University Transportation Center (UTC), 2024, Report no. SM02, ROSA P. https://rosap.ntl.bts.gov/view/dot/86277.
Connected preceding vehicle identification is crucial for establishing cooperative platooning. This paper presents the development of a prototype preceding vehicle identification system (PVIS) and its field evaluation for the assessment of commercial viability. We designed and assembled a prototype consisting of a processing unit (Jetson Nano board
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Dataset
Park, B. B., Mu, Z., Tu, G., Shi, A., Yang, K., Sun, Y., & Shen, C. (2024). Connected Vehicle Identification System for Cooperative Control of Connected Automated Vehicles [supporting dataset] (Report No. 69A3552348303). Morgan State University. Sustainable Mobility and Accessibility Regional Transportation Equity Research Center (SMARTER) Region 3 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/86573
Park, B. Brian, Zeyu Mu, Guancheng Tu, Austin Shi, Kun Yang, Yixin Sun, and Cong Shen. Connected Vehicle Identification System for Cooperative Control of Connected Automated Vehicles [supporting dataset]. Report no. 69A3552348303. Morgan State University. Sustainable Mobility and Accessibility Regional Transportation Equity Research Center (SMARTER) Region 3 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/86573.
Park, B. Brian, et al. Connected Vehicle Identification System for Cooperative Control of Connected Automated Vehicles [supporting dataset]. Morgan State University. Sustainable Mobility and Accessibility Regional Transportation Equity Research Center (SMARTER) Region 3 University Transportation Center (UTC), 2024, Report no. 69A3552348303, ROSA P. https://rosap.ntl.bts.gov/view/dot/86573.
Privately owned and shared autonomous vehicles (AVs and SAVs) and automated trucks (ATrucks) are coming to the US and Texas. This project updated TxDOT’s Statewide Analysis Model (SAM) to integrate AVs, SAVs, and ATrucks as added transportation modes. For passenger trips over 50 miles (one way), the nested logit model was modified to include househ
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Kockelman, K., Vellimana, M., Paithankar, P., & Mori, K. (2024). Implementation of Understanding the Impact of Autonomous Vehicles on Long-Distance Travel Mode and Destination Choice in Texas (Report No. FHWA/TX-24/5-7081-01-1). University of Texas at Austin. Center for Transportation Research. https://rosap.ntl.bts.gov/view/dot/76663
Kockelman, Kara, Maithreyi Vellimana, Priyanka Paithankar, and Kentaro Mori. Implementation of Understanding the Impact of Autonomous Vehicles on Long-Distance Travel Mode and Destination Choice in Texas. Report no. FHWA/TX-24/5-7081-01-1. University of Texas at Austin. Center for Transportation Research, 2024. https://rosap.ntl.bts.gov/view/dot/76663.
Kockelman, Kara, et al. Implementation of Understanding the Impact of Autonomous Vehicles on Long-Distance Travel Mode and Destination Choice in Texas. University of Texas at Austin. Center for Transportation Research, 2024, Report no. FHWA/TX-24/5-7081-01-1, ROSA P. https://rosap.ntl.bts.gov/view/dot/76663.
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