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.
As public and private entities increasingly test the use of automated shuttles for passenger transportation, project sponsors need evaluation methods that measure the willingness of potential passengers to use these vehicles and to identify factors that may increase or decrease acceptability. Toward this end, many automated shuttle pilot sponsors h
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Machek, E. C., & Peirce, S. (2021). Survey Research for Automated Shuttle Pilots: Issues and Challenges (Report No. FTA Report No. 0193). United States. Federal Transit Administration. Office of Research, Demonstration, and Innovation. https://doi.org/10.21949/1520679
Machek, Elizabeth C. and Sean Peirce. Survey Research for Automated Shuttle Pilots: Issues and Challenges. Report no. FTA Report No. 0193. United States. Federal Transit Administration. Office of Research, Demonstration, and Innovation, 2021. https://doi.org/10.21949/1520679.
Machek, Elizabeth C., and Sean Peirce Survey Research for Automated Shuttle Pilots: Issues and Challenges. United States. Federal Transit Administration. Office of Research, Demonstration, and Innovation, 2021, Report no. FTA Report No. 0193, ROSA P. https://doi.org/10.21949/1520679.
The purpose of this report is to document a simulation-based case study completed by the project team to investigate the effectiveness of SAE J3016 Level 1 automation technology for mitigating or solving existing transportation problems related to congestion, fuel consumption, and emissions (SAE International 2016). The case study conducted simulat
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Ma, J., Guo, Y., & Huang, Z. (2021). Developing Analysis, Modeling, and Simulation Tools for Connected and Automated Vehicle Applications: A Case Study for I-66 in Virginia (Report No. FHWA-HRT-21-050). United States. Federal Highway Administration. https://rosap.ntl.bts.gov/view/dot/56284
Ma, Jiaqi, Yi Guo, and Zhitong Huang. Developing Analysis, Modeling, and Simulation Tools for Connected and Automated Vehicle Applications: A Case Study for I-66 in Virginia. Report no. FHWA-HRT-21-050. United States. Federal Highway Administration, 2021. https://rosap.ntl.bts.gov/view/dot/56284.
Ma, Jiaqi, et al. Developing Analysis, Modeling, and Simulation Tools for Connected and Automated Vehicle Applications: A Case Study for I-66 in Virginia. United States. Federal Highway Administration, 2021, Report no. FHWA-HRT-21-050, ROSA P. https://rosap.ntl.bts.gov/view/dot/56284.
This document describes the current test tools and capabilities established at NHTSA’s Vehicle Research and Test Center for researching the safety performance of advanced driver assistance systems (ADAS) and automated driving systems (ADS) in a closed-course setting. This paper focuses on immediate and future needs for ADAS and ADS closed-course te
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Albrecht, H., Barickman, F. S., & Schnelle, S. C. (2021). Advanced Test Tools for ADAS and ADS (Report No. DOT HS 813 083). United States. Department of Transportation. National Highway Traffic Safety Administration. https://doi.org/10.21949/1530219
Albrecht, Heath, Frank S. Barickman, and Scott C. Schnelle. Advanced Test Tools for ADAS and ADS. Report no. DOT HS 813 083. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021. https://doi.org/10.21949/1530219.
Albrecht, Heath, et al. Advanced Test Tools for ADAS and ADS. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021, Report no. DOT HS 813 083, ROSA P. https://doi.org/10.21949/1530219.
This report (a) summarizes the three test scenarios intended to emulate situations commonly encountered during real-world driving where a traffic jam assist (TJA) system may be expected to operate; (b) discusses the results from testing one light vehicle equipped with TJA on the test track; and (c) provides general assessments of revisions made to
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Fogle, E., Arquette, T. E., & Forkenbrock, G. J. (2021). Traffic Jam Assist Draft Test Procedure Performability Validation (Report No. DOT HS 812 987). United States. Department of Transportation. National Highway Traffic Safety Administration. https://doi.org/10.21949/1530212
Fogle, Erin, Tyler E. Arquette, and Garrick J. Forkenbrock. Traffic Jam Assist Draft Test Procedure Performability Validation. Report no. DOT HS 812 987. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021. https://doi.org/10.21949/1530212.
Fogle, Erin, et al. Traffic Jam Assist Draft Test Procedure Performability Validation. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021, Report no. DOT HS 812 987, ROSA P. https://doi.org/10.21949/1530212.
“Semi-controlled” crosswalks exist because of the desire for pedestrians to cross there and the use of stop signs or signals is not warranted. However, there is a sufficient amount of interaction between pedestrians and vehicles at “semi-controlled” crosswalks to be concerned about the time at which “negotiations” between pedestrians and human driv
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Zhang, Y., & Fricker, J. D. (2021). Smart Interaction Pedestrians and Vehicles in a CAV Environment. Purdue University. https://rosap.ntl.bts.gov/view/dot/66746
Zhang, Yunchang and Jon D. Fricker. Smart Interaction Pedestrians and Vehicles in a CAV Environment. Purdue University, 2021. https://rosap.ntl.bts.gov/view/dot/66746.
Zhang, Yunchang, and Jon D. Fricker Smart Interaction Pedestrians and Vehicles in a CAV Environment. Purdue University, 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/66746.
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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DatasetSupporting Files
McDonald, A. D., & Alambeigi, H. (2021). Modeling Driver Behavior during Automated Vehicle Platooning Failures [supporting datasets] (Report No. 03-036). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://doi.org/10.15787/VTT1/C76VBC
McDonald, Anthony D and Hananeh Alambeigi. Modeling Driver Behavior during Automated Vehicle Platooning Failures [supporting datasets]. Report no. 03-036. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021. https://doi.org/10.15787/VTT1/C76VBC.
McDonald, Anthony D, and Hananeh Alambeigi Modeling Driver Behavior during Automated Vehicle Platooning Failures [supporting datasets]. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021, Report no. 03-036, ROSA P. https://doi.org/10.15787/VTT1/C76VBC.
Parking infrastructure is suffering from congestion as the number of vehicles circulating in urban areas is growing and expansion is not a cost-effective solution. In parallel, developments in autonomous vehicle technology mean that driverless vehicles are predicted to be in circulation by the 2020s and makeup 40% of vehicle travel by the 2040s. Ex
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Al Faruque, M. A., Odema, M., & Chen, L. (2021). Software and Hardware Systems for Autonomous Smart Parking Accommodating Both Traditional and Autonomous Vehicles (Report No. PSR-19-30). Pacific Southwest Region University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/56844
Al Faruque, Mohammad Abdullah, Mohanad Odema, and Luke Chen. Software and Hardware Systems for Autonomous Smart Parking Accommodating Both Traditional and Autonomous Vehicles. Report no. PSR-19-30. Pacific Southwest Region University Transportation Center (UTC), 2021. https://rosap.ntl.bts.gov/view/dot/56844.
Al Faruque, Mohammad Abdullah, et al. Software and Hardware Systems for Autonomous Smart Parking Accommodating Both Traditional and Autonomous Vehicles. Pacific Southwest Region University Transportation Center (UTC), 2021, Report no. PSR-19-30, ROSA P. https://rosap.ntl.bts.gov/view/dot/56844.
Parking infrastructure is suffering from congestion as the number of vehicles circulating in urban areas is growing and expansion is not a cost-effective solution. In parallel, developments in autonomous vehicle technology mean that driverless vehicles are predicted to be in circulation by the 2020s and makeup 40% of vehicle travel by the 2040s. Ex
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Al Faruque, M. A. (2021). Software and Hardware Systems for Autonomous Smart Parking Accommodating Both Traditional and Autonomous Vehicles [Research Brief]. METRANS Transportation Center (Calif.). https://rosap.ntl.bts.gov/view/dot/67951
Al Faruque, Mohammad Abdullah. Software and Hardware Systems for Autonomous Smart Parking Accommodating Both Traditional and Autonomous Vehicles [Research Brief]. METRANS Transportation Center (Calif.), 2021. https://rosap.ntl.bts.gov/view/dot/67951.
Al Faruque, Mohammad Abdullah Software and Hardware Systems for Autonomous Smart Parking Accommodating Both Traditional and Autonomous Vehicles [Research Brief]. METRANS Transportation Center (Calif.), 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/67951.
This report summarizes test track validation of NHTSA’s September 2019 Opposing Traffic Safety Assist (OTSA) draft research test procedure. Three of the five test scenarios described in this draft procedure (Scenarios 1, 2, and 4) were used to objectively and effectively assess OTSA performance with the SV operating in automation levels 0 and 1. Si
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Manahan, T., & Forkenbrock, G. J. (2021). Opposing Traffic Safety Assist Draft Test Procedure Performability Validation (Report No. DOT HS 812 918). United States. Department of Transportation. National Highway Traffic Safety Administration. https://doi.org/10.21949/1530209
Manahan, Taylor and Garrick J. Forkenbrock. Opposing Traffic Safety Assist Draft Test Procedure Performability Validation. Report no. DOT HS 812 918. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021. https://doi.org/10.21949/1530209.
Manahan, Taylor, and Garrick J. Forkenbrock Opposing Traffic Safety Assist Draft Test Procedure Performability Validation. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021, Report no. DOT HS 812 918, ROSA P. https://doi.org/10.21949/1530209.
This report reviews existing pre-crash scenario typologies and various proposed behavioral competencies in literature that may be relevant to automated driving systems (ADSs) and selects five example scenarios to facilitate exploration of elements that may be helpful in characterizing them. The selected scenarios were: rear-end scenario, lead vehic
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Rao, S. J., Deosthale, E., Barickman, F. S., Elsasser, D., & Schnelle, S. C. (2021). An Approach for the Selection and Description of Elements Used to Define Driving Scenarios (Report No. DOT HS 813 073). United States. Department of Transportation. National Highway Traffic Safety Administration. https://doi.org/10.21949/1530208
Rao, Sughosh J., Eeshan Deosthale, Frank S. Barickman, Devin Elsasser, and Scott C. Schnelle. An Approach for the Selection and Description of Elements Used to Define Driving Scenarios. Report no. DOT HS 813 073. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021. https://doi.org/10.21949/1530208.
Rao, Sughosh J., et al. An Approach for the Selection and Description of Elements Used to Define Driving Scenarios. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021, Report no. DOT HS 813 073, ROSA P. https://doi.org/10.21949/1530208.
Autonomous truck-mounted attenuators (ATMAs) promise transformative changes in mobile work zone operations by eliminating the need for a worker to operate an impact protection vehicle. The goal of this project was to pilot a demonstration of the ATMA technology, conduct an evaluation and assessment of the operational and safety functions of ATMA ve
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Agarwal, N., Rahmani, R., & Kashayi, N. (2021). Florida ATMA Pilot Demonstration and Evaluation. Florida. Department of Transportation. Research Center. https://rosap.ntl.bts.gov/view/dot/61848
Agarwal, Nithin, Roozbeh Rahmani, and Nagaraju Kashayi. Florida ATMA Pilot Demonstration and Evaluation. Florida. Department of Transportation. Research Center, 2021. https://rosap.ntl.bts.gov/view/dot/61848.
Agarwal, Nithin, et al. Florida ATMA Pilot Demonstration and Evaluation. Florida. Department of Transportation. Research Center, 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/61848.
The goal of this project was to aid emergency management agencies and departments of transportation in preparing for a future hurricane-related mass evacuation where self-driving, autonomous vehicles (AVs) can be utilized to assist with evacuating critical transportation need households (CTNH). A survey was developed and administered to over 1000 r
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Padmanabhan, B., Shirley, T., Murray-Tuite, P., Huynh, N., Comert, G., Shen, J., Tadesse, H., & Wofford, T. (2021). Assessment of Autonomous Vehicle Sharing for Evacuation and Disaster Relief. Center for Connected Multimodal Mobility, Clemson University. https://rosap.ntl.bts.gov/view/dot/60927
Padmanabhan, Bhavya, Thomas Shirley, Pamela Murray-Tuite, Nathan Huynh, Gurcan Comert, Jiayun Shen, Hiwot Tadesse, and Treylon Wofford. Assessment of Autonomous Vehicle Sharing for Evacuation and Disaster Relief. Center for Connected Multimodal Mobility, Clemson University, 2021. https://rosap.ntl.bts.gov/view/dot/60927.
Padmanabhan, Bhavya, et al. Assessment of Autonomous Vehicle Sharing for Evacuation and Disaster Relief. Center for Connected Multimodal Mobility, Clemson University, 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/60927.
Transportation systems continue to face significant challenges and opportunities in the context of their social, technical, and economic outcomes (De Neufville and Scholtes, 2011; Kaewunren et al.; Sinha et al., 2017; De Martinis and Corman, 2018; Bongiovanni et al., 2019; Eker et al., 2019; Eker et al., 2020a; Barmpounakis and Geroliminis, 2020; B
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Labi, S., Anastasopoulos, P., Miralinaghi, M., Ong, G. P., & Zhu, F. (2021). Editorial: Advances in Planning for Emerging Transportation Technologies: Towards Automation, Connectivity, and Electric Propulsion. Center for Connected and Automated Transportation. Purdue University. https://doi.org/10.3389/fbuil.2021.666246
Labi, Samuel, Panagiotis Anastasopoulos, Mohammad Miralinaghi, Ghim Ping Ong, and Feng Zhu. Editorial: Advances in Planning for Emerging Transportation Technologies: Towards Automation, Connectivity, and Electric Propulsion. Center for Connected and Automated Transportation. Purdue University, 2021. https://doi.org/10.3389/fbuil.2021.666246.
Labi, Samuel, et al. Editorial: Advances in Planning for Emerging Transportation Technologies: Towards Automation, Connectivity, and Electric Propulsion. Center for Connected and Automated Transportation. Purdue University, 2021, ROSA P. https://doi.org/10.3389/fbuil.2021.666246.
The purpose of this report is to document a simulation-based case study investigating the effectiveness of SAE J3016 Level 1 automation technology for mitigating or solving existing transportation problems related to congestion, fuel consumption, and emissions.(1) This case study examined the impacts of cooperative adaptive cruise control (CACC) ve
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Liu, H., Lu, X. Y., Shladover, S. E., & Huang, Z. (2021). Developing Analysis, Modeling, and Simulation Tools for Connected Automated Vehicle Applications: A Case Study on SR 99 in California (Report No. FHWA-HRT-21-039). United States. Federal Highway Administration. https://rosap.ntl.bts.gov/view/dot/54795
Liu, Hao, Xiao-Yun Lu, Steven E. Shladover, and Zhitong Huang. Developing Analysis, Modeling, and Simulation Tools for Connected Automated Vehicle Applications: A Case Study on SR 99 in California. Report no. FHWA-HRT-21-039. United States. Federal Highway Administration, 2021. https://rosap.ntl.bts.gov/view/dot/54795.
Liu, Hao, et al. Developing Analysis, Modeling, and Simulation Tools for Connected Automated Vehicle Applications: A Case Study on SR 99 in California. United States. Federal Highway Administration, 2021, Report no. FHWA-HRT-21-039, ROSA P. https://rosap.ntl.bts.gov/view/dot/54795.
Autonomous vehicles (AVs) that utilize LiDAR (Light Detection and Ranging) and other sensing technologies are becoming more prevalent in the transportation industry. Concurrently, transportation agencies are increasingly challenged with asset management, traffic operations, and safety assessments. The affordability of LiDAR technology continues to
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Mekker, M. M., & Rahman, M. A. (2021). Uses and Challenges of Collecting LiDAR Data From a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices (Report No. MPC-577). Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/56629
Mekker, Michelle M and Md Ashikur Rahman. Uses and Challenges of Collecting LiDAR Data From a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices. Report no. MPC-577. Mountain-Plains Consortium, 2021. https://rosap.ntl.bts.gov/view/dot/56629.
Mekker, Michelle M, and Md Ashikur Rahman Uses and Challenges of Collecting LiDAR Data From a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices. Mountain-Plains Consortium, 2021, Report no. MPC-577, ROSA P. https://rosap.ntl.bts.gov/view/dot/56629.
This project studies methods to control both vehicle and traffic under limited penetration of low-level connected and autonomous vehicles (LCAVs). The investigation includes three major parts: (1) the Eco-Driving algorithm for a single CAV with low-level automation; (2) the vehicle in the loop (VIL) simulation platform; and (3) the integrated vehic
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Dataset
Ban, X. (., Guo, Q., Angah, O., & Liu, Z. (2021). Vehicle-Traffic Control with Limited-Capacity Connected/Automated Vehicles [supporting datasets]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART). https://doi.org/10.5281/zenodo.4527313
Ban, Xuegang (Jeff), Qiangqiang Guo, Ohay Angah, and Zhijun Liu. Vehicle-Traffic Control with Limited-Capacity Connected/Automated Vehicles [supporting datasets]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2021. https://doi.org/10.5281/zenodo.4527313.
Ban, Xuegang (Jeff), et al. Vehicle-Traffic Control with Limited-Capacity Connected/Automated Vehicles [supporting datasets]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2021, ROSA P. https://doi.org/10.5281/zenodo.4527313.
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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DatasetSupporting Files
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 [supporting datasets]. Safety Research Using Simulation (SAFER-SIM) University Transportation Center. https://doi.org/10.7910/DVN/LDO7PP
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 [supporting datasets]. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2021. https://doi.org/10.7910/DVN/LDO7PP.
Pradhan, Anuj K., et al. Driver’s Mental Models of Advanced Vehicle Technologies: A Proposed Framework for Identifying and Predicting Operator Errors [supporting datasets]. Safety Research Using Simulation (SAFER-SIM) University Transportation Center, 2021, ROSA P. https://doi.org/10.7910/DVN/LDO7PP.
This repository contains the results produced by CMU-UTC Project 294, titled "Integration of Autonomous Vehicles with Adaptive Signal Control to Enhance Mobility." The data contains comparative results of a pilot experiment designed to demonstrate the traffic flow efficiency can be improved by vehicle-to-infrastructure communication and use of vehi
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DatasetSupporting Files
Smith, S. F., & Hawkes, A. (2021). Integration of Automated Vehicle Sensing with Adaptive Signal Control for Enhanced Mobility [supporting datasets]. Mobility21, Carnegie Mellon University. https://doi.org/10.5281/zenodo.4500187
Smith, Stephen F. and Allen Hawkes. Integration of Automated Vehicle Sensing with Adaptive Signal Control for Enhanced Mobility [supporting datasets]. Mobility21, Carnegie Mellon University, 2021. https://doi.org/10.5281/zenodo.4500187.
Smith, Stephen F., and Allen Hawkes Integration of Automated Vehicle Sensing with Adaptive Signal Control for Enhanced Mobility [supporting datasets]. Mobility21, Carnegie Mellon University, 2021, ROSA P. https://doi.org/10.5281/zenodo.4500187.
The study examined whether advanced driver assistance systems (ADAS) can benefit the mobility and driving performance of senior drivers. Two groups of driving data, collected separately from two naturalistic driving projects, were examined. The Second Strategic Highway Research Program and the Examining Seniors’ Adaptation to Mixed Function Automat
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Liang, D., Antin, J. F., Lau, N. K., & Stulce, K. E. (2021). Examining Seniors’ Adaptation to Mixed Function Automated Vehicles: Analysis of Naturalistic Driving Data (Report No. 03-040). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/56928
Liang, Dan, Jonathan F Antin, Nathan K Lau, and Kelly E Stulce. Examining Seniors’ Adaptation to Mixed Function Automated Vehicles: Analysis of Naturalistic Driving Data. Report no. 03-040. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021. https://rosap.ntl.bts.gov/view/dot/56928.
Liang, Dan, et al. Examining Seniors’ Adaptation to Mixed Function Automated Vehicles: Analysis of Naturalistic Driving Data. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2021, Report no. 03-040, ROSA P. https://rosap.ntl.bts.gov/view/dot/56928.
Advocates of electric, shared, and automated vehicles (e-SAVs) envision a future in which people no longer need to drive their privately owned, petroleum-fueled vehicles. Instead, for daily travel they rely on fleets of electric, automated vehicles that offer travel services, including the option to share, or “pool,” rides with strangers. The desig
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Kurani, K. S. (2021). User Perceptions of the Risks of Electric, Shared, and Automated Vehicles Remain Largely Unexplored [Policy Brief]. National Center for Sustainable Transportation (NCST) (UTC). https://doi.org/10.7922/G21C1V5C
Kurani, Kenneth S.. User Perceptions of the Risks of Electric, Shared, and Automated Vehicles Remain Largely Unexplored [Policy Brief]. National Center for Sustainable Transportation (NCST) (UTC), 2021. https://doi.org/10.7922/G21C1V5C.
Kurani, Kenneth S. User Perceptions of the Risks of Electric, Shared, and Automated Vehicles Remain Largely Unexplored [Policy Brief]. National Center for Sustainable Transportation (NCST) (UTC), 2021, ROSA P. https://doi.org/10.7922/G21C1V5C.
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