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.
The objective of this project was to identify the user perspective about autonomous ride-sharing services for adults 50 years of age and older in Lake Nona, Port St. Lucie, and The Villages, and to solicit responses pertaining to the adoption and acceptance of these services.
Holley, G. M., & Classen, S. (2024). Assessing Safety and Mobility Benefits of Autonomous Ride Sharing Services [Summary]. Florida Department of Transportation. https://rosap.ntl.bts.gov/view/dot/74784
Holley, Gail M. and Sherrilene Classen. Assessing Safety and Mobility Benefits of Autonomous Ride Sharing Services [Summary]. Florida Department of Transportation, 2024. https://rosap.ntl.bts.gov/view/dot/74784.
Holley, Gail M., and Sherrilene Classen Assessing Safety and Mobility Benefits of Autonomous Ride Sharing Services [Summary]. Florida Department of Transportation, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/74784.
This study collected and processed accurate trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments. Multiple methods were utilized to collect data: fixed location aerial videography, moving aerial videography, and infrastructure-based videography. Fixed lo
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Talebpour, A., Mahmassani, H. S., & Hamdar, S. H. (2024). Third Generation Simulation Data (TGSIM): A Closer Look at The Impacts of Automated Driving Systems on Human Behavior (Report No. FHWA-JPO-24-133). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/74647
Talebpour, Alireza, Hani S. Mahmassani, and Samer H Hamdar. Third Generation Simulation Data (TGSIM): A Closer Look at The Impacts of Automated Driving Systems on Human Behavior. Report no. FHWA-JPO-24-133. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2024. https://rosap.ntl.bts.gov/view/dot/74647.
Talebpour, Alireza, et al. Third Generation Simulation Data (TGSIM): A Closer Look at The Impacts of Automated Driving Systems on Human Behavior. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2024, Report no. FHWA-JPO-24-133, ROSA P. https://rosap.ntl.bts.gov/view/dot/74647.
This research focuses on evaluating current CAV crash reporting practices across the United States, identifying inconsistencies and gaps, and proposing recommendations for standardized reporting and legislation to ensure safe and effective deployment of CAVs on public roads. Researchers assessed CAV crash reporting practices across the United State
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Mekker, M., Acharya, S., & Rahman, A. (2024). State of the Practice of Crash Reporting in the U.S. and Implications for CAV Safety Assessment [Research Brief] (Report No. MPC 24-521). Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/76537
Mekker, Michelle, Sailesh Acharya, and Ashikur Rahman. State of the Practice of Crash Reporting in the U.S. and Implications for CAV Safety Assessment [Research Brief]. Report no. MPC 24-521. Mountain-Plains Consortium, 2024. https://rosap.ntl.bts.gov/view/dot/76537.
Mekker, Michelle, et al. State of the Practice of Crash Reporting in the U.S. and Implications for CAV Safety Assessment [Research Brief]. Mountain-Plains Consortium, 2024, Report no. MPC 24-521, ROSA P. https://rosap.ntl.bts.gov/view/dot/76537.
Traditional road safety assessment methodologies rely heavily on AADT (Annual Average Daily Traffic) data estimates to account for variability in traffic operations. Unfortunately, this approach does not consider the driving environment's fast-changing dynamics, which can influence contextual complexity and risk. This research report presents a met
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Ogle, J. H., Bendigeri, V., Zou, F., Ghafari, A. Z., & Comert, G. (2024). Assessment of Contextual Complexity and Risk Using Unsupervised Clustering Approaches with Dynamic Traffic Condition Data Obtained from Autonomous Vehicles. Center for Connected Multimodal Mobility, Clemson University. https://rosap.ntl.bts.gov/view/dot/75190
Ogle, Jennifer H, Vijay Bendigeri, Fengjiao Zou, Ahmad Zaki Ghafari, and Gurcan Comert. Assessment of Contextual Complexity and Risk Using Unsupervised Clustering Approaches with Dynamic Traffic Condition Data Obtained from Autonomous Vehicles. Center for Connected Multimodal Mobility, Clemson University, 2024. https://rosap.ntl.bts.gov/view/dot/75190.
Ogle, Jennifer H, et al. Assessment of Contextual Complexity and Risk Using Unsupervised Clustering Approaches with Dynamic Traffic Condition Data Obtained from Autonomous Vehicles. Center for Connected Multimodal Mobility, Clemson University, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/75190.
Traditional road safety assessment methodologies rely heavily on AADT (Annual Average Daily Traffic) data estimates to account for variability in traffic operations. Unfortunately, this approach does not consider the driving environment's fast-changing dynamics, which can influence contextual complexity and risk. This research report presents a met
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Ogle, J. H., Bendigeri, V., Zou, F., Ghafari, A. Z., & Comert, G. (2024). A Statistical and Machine Learning Approach to Assess Contextual Complexity of the Driving Environment Using Autonomous Vehicle Data- Technology Transfer Activities. Center for Connected Multimodal Mobility, Clemson University. https://rosap.ntl.bts.gov/view/dot/75092
Ogle, Jennifer H, Vijay Bendigeri, Fengjiao Zou, Ahmad Zaki Ghafari, and Gurcan Comert. A Statistical and Machine Learning Approach to Assess Contextual Complexity of the Driving Environment Using Autonomous Vehicle Data- Technology Transfer Activities. Center for Connected Multimodal Mobility, Clemson University, 2024. https://rosap.ntl.bts.gov/view/dot/75092.
Ogle, Jennifer H, et al. A Statistical and Machine Learning Approach to Assess Contextual Complexity of the Driving Environment Using Autonomous Vehicle Data- Technology Transfer Activities. Center for Connected Multimodal Mobility, Clemson University, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/75092.
The Cooperative Driving Automation (CDA) Annual Report highlights the achievements of the CDA program and discusses the use cases that were created and researched to further overall CDA program objectives to enable a cooperative, safe, efficient, and sustainable transportation system for all users. This report also details engagement activities wit
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Balse, A., Bellamkonda, M., Cooper, Z. R., Ghiasi, A., Huang, Z., Laqui, R., Leslie, E., Lou, Y., Marwadi, S., Moyer, S., Nieto, F. P., Osman, O. A., Rashid, K. M., Racha, S., Soleimaniamiri, S., Tyagi, A., & Urban, Z. W. (2024). 2022 CDA Annual Report (Report No. FHWA-HRT-24-089). United States. Department of Transportation. Federal Highway Administration. https://rosap.ntl.bts.gov/view/dot/76958
Balse, Animesh, Mitali Bellamkonda, Zachary R. Cooper, Amir Ghiasi, Zhitong Huang, Robin Laqui, and Ed Leslie, et al.. 2022 CDA Annual Report. Report no. FHWA-HRT-24-089. United States. Department of Transportation. Federal Highway Administration, 2024. https://rosap.ntl.bts.gov/view/dot/76958.
Balse, Animesh, et al. 2022 CDA Annual Report. United States. Department of Transportation. Federal Highway Administration, 2024, Report no. FHWA-HRT-24-089, ROSA P. https://rosap.ntl.bts.gov/view/dot/76958.
Workforce education is crucial to ensuring the safety and equality of the work environment. Immersive virtual reality (VR) training is revolutionizing workforce education, providing practical on-the-job training within a safe simulated environment. The project aimed to develop an immersive training platform using state-of-the-art VR technologies an
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Wang, Z., Lin, P. S., Alqasemi, R. M., Kolla, R. D. T. N., & Aguila, A. L. (2024). Development of an Immersive Training Platform for Roadway Construction Workers using Virtual Reality Technologies [Policy Brief]. Center for Transportation, Equity, Decisions and Dollars (CTEDD) (UTC). https://rosap.ntl.bts.gov/view/dot/78250
Wang, Zhenyu, Pei-Sung Lin, Redwan M Alqasemi, Rama Durga Tammayya Naidu Kolla, and Alvaro Lazaro Aguila. Development of an Immersive Training Platform for Roadway Construction Workers using Virtual Reality Technologies [Policy Brief]. Center for Transportation, Equity, Decisions and Dollars (CTEDD) (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/78250.
Wang, Zhenyu, et al. Development of an Immersive Training Platform for Roadway Construction Workers using Virtual Reality Technologies [Policy Brief]. Center for Transportation, Equity, Decisions and Dollars (CTEDD) (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/78250.
This paper investigates how global navigation satellite systems (GNSSs) and inertial navigation systems (INSs), when appropriately augmented by ranging from local landmarks, can safely navigate vehicles through a real-world urban environment. We begin by considering safety requirements for driverless vehicles under fault-free assumptions and develo
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Nagai, K., Spenko, M., Henderson, R., & Pervan, B. (2024). Fault-Free Integrity of Urban Driverless Vehicle Navigation with Multi-Sensor Integration: A Case Study in Downtown Chicago. Institute of Navigation. https://doi.org/10.33012/navi.631
Nagai, Kana, Matthew Spenko, Ron Henderson, and Boris Pervan. Fault-Free Integrity of Urban Driverless Vehicle Navigation with Multi-Sensor Integration: A Case Study in Downtown Chicago. Institute of Navigation, 2024. https://doi.org/10.33012/navi.631.
Nagai, Kana, et al. Fault-Free Integrity of Urban Driverless Vehicle Navigation with Multi-Sensor Integration: A Case Study in Downtown Chicago. Institute of Navigation, 2024, ROSA P. https://doi.org/10.33012/navi.631.
Public transportation provides a safe, convenient, affordable, and environmentally friendly mobility service. However, due to its fixed routes and limited network coverage, it is sometimes difficult or impossible for passengers to walk from a transit stop to their destination. This inaccessibility problem is also known as the “transit last-mile con
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Khani, A., Aalipour, A., & Kumar, P. (2024). Designing an Autonomous Service to Cover Transit’s Last Mile in Low-Density Areas (Report No. MN 2024-06). Minnesota. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/75512
Khani, Alireza, Ali Aalipour, and Pramesh Kumar. Designing an Autonomous Service to Cover Transit’s Last Mile in Low-Density Areas. Report no. MN 2024-06. Minnesota. Department of Transportation, 2024. https://rosap.ntl.bts.gov/view/dot/75512.
Khani, Alireza, et al. Designing an Autonomous Service to Cover Transit’s Last Mile in Low-Density Areas. Minnesota. Department of Transportation, 2024, Report no. MN 2024-06, ROSA P. https://rosap.ntl.bts.gov/view/dot/75512.
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., & Ponnaluri, R. (2024). Toward a Florida Automated, Connected, Electric, and Shared (ACES) Transportation System Roadmap: Phase II [Summary]. Florida Department of Transportation. https://rosap.ntl.bts.gov/view/dot/74636
Lin, Pei-Sung and Raj Ponnaluri. Toward a Florida Automated, Connected, Electric, and Shared (ACES) Transportation System Roadmap: Phase II [Summary]. Florida Department of Transportation, 2024. https://rosap.ntl.bts.gov/view/dot/74636.
Lin, Pei-Sung, and Raj Ponnaluri Toward a Florida Automated, Connected, Electric, and Shared (ACES) Transportation System Roadmap: Phase II [Summary]. Florida Department of Transportation, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/74636.
This research studied how variations in an occupantless delivery vehicle’s (ODV) design can affect the occupants in an occupied crash partner vehicle, across a range of expected operational design domains. In full frontal and frontal oblique impact configurations, improved ODV compatibility correlated well with less severe LPV crash pulses, lower o
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Kan, S., Reichert, R., & Park, C. (2024). Crash Compatibility for Occupantless Delivery Vehicles (Report No. DOT HS 813 332). United States. Department of Transportation. National Highway Traffic Safety Administration. https://rosap.ntl.bts.gov/view/dot/73791
Kan, Steve, Rudolf Reichert, and CK Park. Crash Compatibility for Occupantless Delivery Vehicles. Report no. DOT HS 813 332. United States. Department of Transportation. National Highway Traffic Safety Administration, 2024. https://rosap.ntl.bts.gov/view/dot/73791.
Kan, Steve, et al. Crash Compatibility for Occupantless Delivery Vehicles. United States. Department of Transportation. National Highway Traffic Safety Administration, 2024, Report no. DOT HS 813 332, ROSA P. https://rosap.ntl.bts.gov/view/dot/73791.
Intelligent mobile robots, including autonomous agents, highly rely on the correctness of surrounding environment perception. Recently, Deep Learning-based perception models have been shown to be vulnerable to adversarial attacks through one kind of well-designed input called adversarial examples. Existing defenses include mainly adversarial traini
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Pisu, P., Comert, G., Zhao, C., & Begashaw, N. (2024). Securing Deep Learning Against Adversarial Attacks for Connected and Automated Vehicles. Center for Connected Multimodal Mobility, Clemson University. https://rosap.ntl.bts.gov/view/dot/75046
Pisu, Pierluigi, Gurcan Comert, Chunheng Zhao, and Negash Begashaw. Securing Deep Learning Against Adversarial Attacks for Connected and Automated Vehicles. Center for Connected Multimodal Mobility, Clemson University, 2024. https://rosap.ntl.bts.gov/view/dot/75046.
Pisu, Pierluigi, et al. Securing Deep Learning Against Adversarial Attacks for Connected and Automated Vehicles. Center for Connected Multimodal Mobility, Clemson University, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/75046.
We examined the user perspective about autonomous ride sharing services among older adults (50+ years of age) in three different geographic areas in Florida (Lake Nona, Port St. Lucie, and The Villages). We utilized the Autonomous Ride Sharing Services Survey and participants’ lived experiences before and after exposure to the autonomous shuttle. O
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Classen, S., VandeWeerd, C., Stetten, N., Hwangbo, S. W., Winter, S., & Li, Y. (2024). Assessing Safety and Mobility Benefits of Autonomous Ride Sharing Services. Florida Department of Transportation. https://rosap.ntl.bts.gov/view/dot/74844
Classen, Sherrilene, Carla VandeWeerd, Nichole Stetten, Seung Woo Hwangbo, Sandra Winter, and Yuan Li. Assessing Safety and Mobility Benefits of Autonomous Ride Sharing Services. Florida Department of Transportation, 2024. https://rosap.ntl.bts.gov/view/dot/74844.
Classen, Sherrilene, et al. Assessing Safety and Mobility Benefits of Autonomous Ride Sharing Services. Florida Department of Transportation, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/74844.
Driving under the influence is a significant concern for public safety, leading to many tragic accidents. Alcohol impairs a driver’s ability to process information at every stage, affecting their judgment and reactions on the road. Our research explores the correlation between driving under the effects of alcohol and its consequences while focusing
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Dong, M., Lee, Y., Cha, J., & Huang, G. (2024). Investigating the Effects of Alcohol Consumption on Manual and Automated Driving: A Systematic Review [Brief] (Report No. Project 2302). San Jose State University. College of Business. Mineta Transportation Institute. https://rosap.ntl.bts.gov/view/dot/84666
Dong, Miaomiao, Yuni Lee, Jackie Cha, and Gaojian Huang. Investigating the Effects of Alcohol Consumption on Manual and Automated Driving: A Systematic Review [Brief]. Report no. Project 2302. San Jose State University. College of Business. Mineta Transportation Institute, 2024. https://rosap.ntl.bts.gov/view/dot/84666.
Dong, Miaomiao, et al. Investigating the Effects of Alcohol Consumption on Manual and Automated Driving: A Systematic Review [Brief]. San Jose State University. College of Business. Mineta Transportation Institute, 2024, Report no. Project 2302, ROSA P. https://rosap.ntl.bts.gov/view/dot/84666.
There are direct correlations between drunk driving and car-related injuries, disabilities, and death. Autonomous vehicles (AVs) may provide useful driver support systems in order to prevent or reduce road accidents. However, AVs are not yet fully automated and require human drivers to take over the vehicle at times. Therefore, understanding how al
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Dong, M., Lee, Y., Cha, J., & Huang, G. (2024). Investigating the Effects of Alcohol Consumption on Manual and Automated Driving: A Systematic Review (Report No. 23-42). San Jose State University. College of Business. Mineta Transportation Institute. https://doi.org/10.31979/mti.2024.2302
Dong, Miaomiao, Yuni Lee, Jackie Cha, and Gaojian Huang. Investigating the Effects of Alcohol Consumption on Manual and Automated Driving: A Systematic Review. Report no. 23-42. San Jose State University. College of Business. Mineta Transportation Institute, 2024. https://doi.org/10.31979/mti.2024.2302.
Dong, Miaomiao, et al. Investigating the Effects of Alcohol Consumption on Manual and Automated Driving: A Systematic Review. San Jose State University. College of Business. Mineta Transportation Institute, 2024, Report no. 23-42, ROSA P. https://doi.org/10.31979/mti.2024.2302.
The objectives of this project were to 1) examine the AV-based microtransit service in the Lake Nona neighborhood of Orlando, Florida called Move Nona and 2) develop a framework for examining various aspects of the system, including policy and government support, infrastructure and technology, service and management, financial sustainability, and r
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Schoelzel, P., & Peng, Z. R. (2024). Examining Data Needs and Implementation Process of AV-Based Microtransit Service: A Case Study in Lake Nona [Summary]. Florida Department of Transportation. https://rosap.ntl.bts.gov/view/dot/74785
Schoelzel, Paul and Zhong-Ren Peng. Examining Data Needs and Implementation Process of AV-Based Microtransit Service: A Case Study in Lake Nona [Summary]. Florida Department of Transportation, 2024. https://rosap.ntl.bts.gov/view/dot/74785.
Schoelzel, Paul, and Zhong-Ren Peng Examining Data Needs and Implementation Process of AV-Based Microtransit Service: A Case Study in Lake Nona [Summary]. Florida Department of Transportation, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/74785.
The National Highway Traffic Safety Administration (NHTSA) calls for fundamental research on “the driver performance profile over time in sustained and short-cycle automation … and driver-vehicle interface to allow safe operation and transition between automated and nonautomated vehicle operation.” The emerging level 3 autonomous vehicle (AV) has t
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Deng, M., Gluck, A., Menassa, C., Kamat, V., Li, D., & Brinkley, J. (2024). Predicting Driver Takeover Performance in Conditional Automation (Level 3) through Physiological Sensing (Report No. CCAT Final Report #57). University of Michigan. Center for Connected and Automated Transportation. https://rosap.ntl.bts.gov/view/dot/73069
Deng, Min, Aaron Gluck, Carol Menassa, Vineet Kamat, Da Li, and Julian Brinkley. Predicting Driver Takeover Performance in Conditional Automation (Level 3) through Physiological Sensing. Report no. CCAT Final Report #57. University of Michigan. Center for Connected and Automated Transportation, 2024. https://rosap.ntl.bts.gov/view/dot/73069.
Deng, Min, et al. Predicting Driver Takeover Performance in Conditional Automation (Level 3) through Physiological Sensing. University of Michigan. Center for Connected and Automated Transportation, 2024, Report no. CCAT Final Report #57, ROSA P. https://rosap.ntl.bts.gov/view/dot/73069.
The National Highway Traffic Safety Administration (NHTSA) calls for fundamental research on “the driver performance profile over time in sustained and short-cycle automation … and driver-vehicle interface to allow safe operation and transition between automated and nonautomated vehicle operation.” The emerging level 3 autonomous vehicle (AV) has t
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Dataset
Deng, M., Gluck, A., Menassa, C., Kamat, V., Li, D., & Brinkley, J. (2024). Predicting Driver Takeover Performance in Conditional Automation (Level 3) Through Physiological Sensing [supporting dataset] (Report No. CCAT Final Report #57). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.7302/b312-3t56
Deng, Min, Aaron Gluck, Carol Menassa, Vineet Kamat, Da Li, and Julian Brinkley. Predicting Driver Takeover Performance in Conditional Automation (Level 3) Through Physiological Sensing [supporting dataset]. Report no. CCAT Final Report #57. University of Michigan. Center for Connected and Automated Transportation, 2024. https://doi.org/10.7302/b312-3t56.
Deng, Min, et al. Predicting Driver Takeover Performance in Conditional Automation (Level 3) Through Physiological Sensing [supporting dataset]. University of Michigan. Center for Connected and Automated Transportation, 2024, Report no. CCAT Final Report #57, ROSA P. https://doi.org/10.7302/b312-3t56.
High precision road maps are a crucial component to facilitating autonomous driving techniques. Although current Autonomous vehicles (AVs) rely on vehicular sensing techniques (e.g., camera, light detection and ranging [LiDAR], radar), studies have suggested that creating high-quality road maps with traffic control infrastructures (TCIs) (e.g., tra
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Wu, J. (., Miller, M., Stoeltje, G., Le, M., Hwang, W., Huang, T., Hu, N., Zalila-Wenkstern, R., Torabi, B., & Li, X. (2024). Digitizing Traffic Control Infrastructure for Autonomous Vehicles (AV): Technical Report (Report No. FHWA/TX-23/0-7128-R1). Texas A&M Transportation Institute. https://rosap.ntl.bts.gov/view/dot/73234
Wu, Jason (Dayong), Matthew Miller, Gretchen Stoeltje, Minh Le, William Hwang, Tianchen Huang, Nanzhou Hu, Rym Zalila-Wenkstern, Behnam Torabi, and Xiao Li. Digitizing Traffic Control Infrastructure for Autonomous Vehicles (AV): Technical Report. Report no. FHWA/TX-23/0-7128-R1. Texas A&M Transportation Institute, 2024. https://rosap.ntl.bts.gov/view/dot/73234.
Wu, Jason (Dayong), et al. Digitizing Traffic Control Infrastructure for Autonomous Vehicles (AV): Technical Report. Texas A&M Transportation Institute, 2024, Report no. FHWA/TX-23/0-7128-R1, ROSA P. https://rosap.ntl.bts.gov/view/dot/73234.
Connected and autonomous vehicles (C/AV) present the opportunity for momentous and positive changes to most aspects of modern life. When described, the impact to mobility is imagined as safer and more efficient. This comes from the chance to build a self-driving and connected network of vehicles, infrastructure, and supporting data exchanges.... As
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Blewett, B., Barham, M., Davis, R., Kuhr, J., Hwang, W., Stoeltje, G., Hansen, T., Geiselbrecht, T., Rodriguez, G., Shah, P., & Thompson, A. (2024). Tort Liability for TxDOT from C/AV Technologies (Report No. 0-7130-P7). Texas A&M Transportation Institute. https://rosap.ntl.bts.gov/view/dot/76671
Blewett, Becky, Misty Barham, Robert Davis, James Kuhr, William Hwang, Gretchen Stoeltje, and Todd Hansen, et al.. Tort Liability for TxDOT from C/AV Technologies. Report no. 0-7130-P7. Texas A&M Transportation Institute, 2024. https://rosap.ntl.bts.gov/view/dot/76671.
Blewett, Becky, et al. Tort Liability for TxDOT from C/AV Technologies. Texas A&M Transportation Institute, 2024, Report no. 0-7130-P7, ROSA P. https://rosap.ntl.bts.gov/view/dot/76671.
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