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 recent years, there has been a notable increase in the development of autonomous vehicle (AV) technologies aimed at improving safety in transportation systems. While AVs have been deployed in the real-world to some extent, a full-scale deployment requires AVs to robustly navigate through challenges like heavy rain, snow, low lighting, constructi
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Sural, S., Ramakrishnan, N., & Rajkumar, R. (. (2024). ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://doi.org/10.48550/arXiv.2409.00301
Sural, Shounak, Naren Ramakrishnan, and Ragunathan (Raj) Rajkumar. ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://doi.org/10.48550/arXiv.2409.00301.
Sural, Shounak, et al. ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, ROSA P. https://doi.org/10.48550/arXiv.2409.00301.
Connected autonomous vehicles (CAVs) are gradually advancing towards widespread deployments. CAVs promise to improve transportation safety by operating more efficiently and avoiding incidents like crashes due to human driver error. However, they may cause crashes or other safety incidents themselves, especially when interacting with humans. Our wor
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Joe-Wong, C., Yağan, O., & Lin, I. C. (2024). Evaluating Autonomous Vehicles' Safety Benefits in Mixed Autonomy Scenarios. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77493
Joe-Wong, Carlee, Osman Yağan, and I-Cheng Lin. Evaluating Autonomous Vehicles' Safety Benefits in Mixed Autonomy Scenarios. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77493.
Joe-Wong, Carlee, et al. Evaluating Autonomous Vehicles' Safety Benefits in Mixed Autonomy Scenarios. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/77493.
Fatal traffic crashes have increased significantly, largely due to human error, which automated vehicle technology aims to reduce. However, challenges such as driver fatigue and the need for quick intervention in case of system failures must be urgently addressed to ensure safety. This study aims to develop a driver fatigue monitoring system to det
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Deb, S., Kan, C., Pande, A., & Noyce, D. A. (2024). Development of a Monitoring System for Driver Readiness in Prolonged Automated Driving (Report No. CTEDD 022-04). Center for Transportation, Equity, Decisions and Dollars (CTEDD) (UTC). https://rosap.ntl.bts.gov/view/dot/78249
Deb, Shuchisnigdha, Chen Kan, Anurag Pande, and David A. Noyce. Development of a Monitoring System for Driver Readiness in Prolonged Automated Driving. Report no. CTEDD 022-04. Center for Transportation, Equity, Decisions and Dollars (CTEDD) (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/78249.
Deb, Shuchisnigdha, et al. Development of a Monitoring System for Driver Readiness in Prolonged Automated Driving. Center for Transportation, Equity, Decisions and Dollars (CTEDD) (UTC), 2024, Report no. CTEDD 022-04, ROSA P. https://rosap.ntl.bts.gov/view/dot/78249.
The safety of vulnerable road users (VRUs) at signalized intersections is a significant concern in urban traffic management. This study aims to enhance VRUs safety through the deployment of advanced communication and machine learning technologies. Using a Connected and Autonomous Vehicle (CAV) testbed, this research explores real-time communication
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Jeihani, M., Yang, D., & Ansariyar, A. (2024). Evaluation of Driver Behavior Influenced by Traffic Safety Messages From Roadside Units in Connected and Autonomous Systems (Report No. SM02). Morgan State University. Safety and Mobility Advancements Regional Transportation and Economics Research Center. https://rosap.ntl.bts.gov/view/dot/92681
Jeihani, Mansoureh, Di Yang, and Alireza Ansariyar. Evaluation of Driver Behavior Influenced by Traffic Safety Messages From Roadside Units in Connected and Autonomous Systems. Report no. SM02. Morgan State University. Safety and Mobility Advancements Regional Transportation and Economics Research Center, 2024. https://rosap.ntl.bts.gov/view/dot/92681.
Jeihani, Mansoureh, et al. Evaluation of Driver Behavior Influenced by Traffic Safety Messages From Roadside Units in Connected and Autonomous Systems. Morgan State University. Safety and Mobility Advancements Regional Transportation and Economics Research Center, 2024, Report no. SM02, ROSA P. https://rosap.ntl.bts.gov/view/dot/92681.
In this project, we proposed a CBF-inspired risk assessment toolbox, measuring the aggregated risk faced by individual agents due to multi-agent interactions, to help empower the ego robot with a comprehensive understanding of the dynamic environment it is in. We also demonstrate two complementary methodologies of embedding the resulting risk asses
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Dolan, J. M., Lyu, Y., & Schulte, P. (2024). Risk Aware Warning and Control for Interactive Traffic Safety (Report No. 428). Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77710
Dolan, John M, Yiwei Lyu, and Paul Schulte. Risk Aware Warning and Control for Interactive Traffic Safety. Report no. 428. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77710.
Dolan, John M, et al. Risk Aware Warning and Control for Interactive Traffic Safety. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, Report no. 428, ROSA P. https://rosap.ntl.bts.gov/view/dot/77710.
Most light-duty vehicle (LDV) crashes occur due to human error. The National Highway Safety Administration (NHTSA) reports that eight percent of fatal crashes in 2018 were distraction-affected crashes, while close to ninety-four percent of all crashes occur in part due to human error. Crash avoidance features could reduce both the frequency and sev
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Harper, C., Hendrickson, C., & Yang, H. (2024). Estimating the Effects of Vehicle Automation and Vehicle Weight and Size on Crash Frequency and Severity: Phase 1. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77504
Harper, Corey, Chris Hendrickson, and Haoming Yang. Estimating the Effects of Vehicle Automation and Vehicle Weight and Size on Crash Frequency and Severity: Phase 1. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77504.
Harper, Corey, et al. Estimating the Effects of Vehicle Automation and Vehicle Weight and Size on Crash Frequency and Severity: Phase 1. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/77504.
The current approach to connected and autonomous driving function development and evaluation uses model-in-the-loop (MIL) simulation, hardware-in-the-loop (HIL) simulation and limited proving ground use, followed by public road deployment of the beta version of software and technology. The rest of the road users are involuntarily forced into taking
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Chen, H., Cao, X., Aksun-Guvenc, B., & Guvenc, L. (2024). Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77506
Chen, Haochong, Xincheng Cao, Bilin Aksun-Guvenc, and Levent Guvenc. Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77506.
Chen, Haochong, et al. Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/77506.
Autonomous ground vehicles must safely operate in highly interactive environments with human uncertainties. Safe actions depend on context, interactions, and absolute (mathematical measures for safety) may differ from how humans perceive as safe and reliable behaviors. However, producing context-dependent and interaction-aware safe actions is non-t
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Nakahira, Y., & Hoshino, H. (2024). Safe Decision-Making in Interactive Environments (Report No. 471). Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77711
Nakahira, Yorie and Hiraku Hoshino. Safe Decision-Making in Interactive Environments. Report no. 471. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77711.
Nakahira, Yorie, and Hiraku Hoshino Safe Decision-Making in Interactive Environments. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, Report no. 471, ROSA P. https://rosap.ntl.bts.gov/view/dot/77711.
Developing an automated driving system capable of navigating complex traffic environments remains a formidable challenge. Unlike rule-based or supervised learning-based methods, Deep Reinforcement Learning (DRL) based controllers eliminate the need for domain-specific knowledge and datasets, thus providing adaptability to various scenarios. Nonethe
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Redmill, K., Zhang, Z., & Yurtsever, E. (2024). Hierarchical Decision Making and Control in RL-based Autonomous Driving for Improved Safety in Complex Traffic Scenarios - Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77138
Redmill, Keith, Zhihao Zhang, and Ekim Yurtsever. Hierarchical Decision Making and Control in RL-based Autonomous Driving for Improved Safety in Complex Traffic Scenarios - Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77138.
Redmill, Keith, et al. Hierarchical Decision Making and Control in RL-based Autonomous Driving for Improved Safety in Complex Traffic Scenarios - Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/77138.
Despite maturing road tests and limited commercial mobility services with autonomous vehicles (AVs), the existing behavioral research, surveys, and polls suggest that, to date, the public is largely reluctant or neutral to accept this emerging technology due to potential lurking failures and malfunctions in unexpected weather/road conditions and cy
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Nazari, F., & Noruzoliaee, M. (2024). On the Role of Perceived Safety Concerns on Public Acceptance Behavior of Autonomous Vehicles (Report No. 472). Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77511
Nazari, Fatemeh and Mohamadhossein Noruzoliaee. On the Role of Perceived Safety Concerns on Public Acceptance Behavior of Autonomous Vehicles. Report no. 472. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77511.
Nazari, Fatemeh, and Mohamadhossein Noruzoliaee On the Role of Perceived Safety Concerns on Public Acceptance Behavior of Autonomous Vehicles. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, Report no. 472, ROSA P. https://rosap.ntl.bts.gov/view/dot/77511.
Vehicle headway, defined as the time elapsed between two successive vehicles passing a roadway point, is a key mesoscopic-scale measure in traffic flow theory with safety-critical transportation applications, such as preemptive collision avoidance warning systems as well as connected and autonomous vehicle (CAV) platoon control. Hence, it is crucia
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Noruzoliaee, M., & Nazari, F. (2024). Enhancing Traffic Safety and Connectivity: A Data-Driven Multi-Step-Ahead Vehicle Headway Prediction Leveraging High-Resolution Vehicular Trajectories (Report No. 470). Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77505
Noruzoliaee, Mohamadhossein and Fatemeh Nazari. Enhancing Traffic Safety and Connectivity: A Data-Driven Multi-Step-Ahead Vehicle Headway Prediction Leveraging High-Resolution Vehicular Trajectories. Report no. 470. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77505.
Noruzoliaee, Mohamadhossein, and Fatemeh Nazari Enhancing Traffic Safety and Connectivity: A Data-Driven Multi-Step-Ahead Vehicle Headway Prediction Leveraging High-Resolution Vehicular Trajectories. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, Report no. 470, ROSA P. https://rosap.ntl.bts.gov/view/dot/77505.
Work zone safety has been a major concern for many stakeholders, including the state Departments of Transportation (DOTs). To prevent DOT workers, especially truck mounted attenuator (TMA) drivers, from injuries, the Autonomous Truck Mounted Attenuator (ATMA) technology was developed. To understand the current testing and deployment status of ATMA,
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Tian, C., Feng, Y., Chen, Y., & Zhang, J. (2024). Impacts of Autonomous Truck-Mounted Attenuator (ATMA) on INDOT Work Zone Safety, Mobility, and Worker Productivity (Report No. FHWA/IN/JTRP-2024/22). Purdue University. Joint Transportation Research Program. https://doi.org/10.5703/1288284317754
Tian, Chi, Yiheng Feng, Yunfeng Chen, and Jiansong Zhang. Impacts of Autonomous Truck-Mounted Attenuator (ATMA) on INDOT Work Zone Safety, Mobility, and Worker Productivity. Report no. FHWA/IN/JTRP-2024/22. Purdue University. Joint Transportation Research Program, 2024. https://doi.org/10.5703/1288284317754.
Tian, Chi, et al. Impacts of Autonomous Truck-Mounted Attenuator (ATMA) on INDOT Work Zone Safety, Mobility, and Worker Productivity. Purdue University. Joint Transportation Research Program, 2024, Report no. FHWA/IN/JTRP-2024/22, ROSA P. https://doi.org/10.5703/1288284317754.
At the state and national levels, there is continued interest in AV technologies and in providing a broad set of transportation options. Furthermore, technologies such as mobility on demand, ridesharing, and micromobility provide alternative options. Research is needed to understand how AV shuttles and other advanced technology-based options may of
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Elefteriadou, L., Yan, J., Manjunatha, P., Salehian, M., & Zorbas, V. (2024). Scan and Review of Autonomous Shuttle Operation and Other Personal Transportation Options Affecting Autonomous Transit Viability. Florida. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/88424
Elefteriadou, Lily, Jacob Yan, Pruthvi Manjunatha, Mohaddese Salehian, and Victoria Zorbas. Scan and Review of Autonomous Shuttle Operation and Other Personal Transportation Options Affecting Autonomous Transit Viability. Florida. Department of Transportation, 2024. https://rosap.ntl.bts.gov/view/dot/88424.
Elefteriadou, Lily, et al. Scan and Review of Autonomous Shuttle Operation and Other Personal Transportation Options Affecting Autonomous Transit Viability. Florida. Department of Transportation, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/88424.
In fall 2022, a first-of-its-kind connected and automated vehicle (CAV) pilot program called goMARTI (Minnesota’s Autonomous Rural Transit Initiative) was launched as a collaborative effort between numerous partners. The 18-month pilot offers free, on-demand rides to area residents and visitors using five autonomous shuttle vans (including three wh
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Douma, F., & Weiner, E. (2024). The Grand Iron Range CAV Initiative: History, Partnerships, and Community Engagement (Report No. MN 2024-20). Minnesota. Department of Transportation. Office of Research & Innovation. https://rosap.ntl.bts.gov/view/dot/78999
Douma, Frank and Evelyn Weiner. The Grand Iron Range CAV Initiative: History, Partnerships, and Community Engagement. Report no. MN 2024-20. Minnesota. Department of Transportation. Office of Research & Innovation, 2024. https://rosap.ntl.bts.gov/view/dot/78999.
Douma, Frank, and Evelyn Weiner The Grand Iron Range CAV Initiative: History, Partnerships, and Community Engagement. Minnesota. Department of Transportation. Office of Research & Innovation, 2024, Report no. MN 2024-20, ROSA P. https://rosap.ntl.bts.gov/view/dot/78999.
Pavement marking style and patterns have largely been designed based on human vision. MnDOT is completing a human factors study on pavement marking variations, A field study included data collected through various methods from test corridors in Minnesota and Texas during daytime and nighttime driving. A closed course at Texas A&M University allowed
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Peterson, E., & Pike, A. (2024). Pavement Markings To Support Automated Vehicles [Research Summary] (Report No. 2024-16). Minnesota. Department of Transportation. Office of Research & Innovation. https://rosap.ntl.bts.gov/view/dot/77436
Peterson, Ethan and Adam Pike. Pavement Markings To Support Automated Vehicles [Research Summary]. Report no. 2024-16. Minnesota. Department of Transportation. Office of Research & Innovation, 2024. https://rosap.ntl.bts.gov/view/dot/77436.
Peterson, Ethan, and Adam Pike Pavement Markings To Support Automated Vehicles [Research Summary]. Minnesota. Department of Transportation. Office of Research & Innovation, 2024, Report no. 2024-16, ROSA P. https://rosap.ntl.bts.gov/view/dot/77436.
The North Carolina Department of Transportation (NCDOT) partnered with the University of North Carolina at Charlotte (UNC Charlotte) and Beep, Inc. (Beep) to bring a novel-design, all-electric, low-speed automated shuttle to UNC Charlotte’s campus for a 23-week pilot through the Connected Autonomous Shuttle Supporting Innovation (CASSI) program. Be
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Searcy, S., & Lape, D. (2024). Connected Autonomous Shuttle Supporting Innovation (CASSI) at UNC Charlotte. North Carolina. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/78968
Searcy, Sarah and Doug Lape. Connected Autonomous Shuttle Supporting Innovation (CASSI) at UNC Charlotte. North Carolina. Department of Transportation, 2024. https://rosap.ntl.bts.gov/view/dot/78968.
Searcy, Sarah, and Doug Lape Connected Autonomous Shuttle Supporting Innovation (CASSI) at UNC Charlotte. North Carolina. Department of Transportation, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/78968.
New technologies are being added to vehicles at a growing rate to assist drivers and even fully take over the driving task in some situations. These technologies are generally camera-based and typically rely on pavement markings to maintain vehicle position and navigate the roadway. As drivers become more reliant on these systems, and for these sys
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Pike, A., Shirinzad, M., Nayak, A., & Rathinam, S. (2024). Assessing Pavement Markings for Automated Vehicle Readiness (Report No. MN 2024-16). Minnesota. Department of Transportation. Office of Research & Innovation. https://rosap.ntl.bts.gov/view/dot/77435
Pike, Adam, Maryam Shirinzad, Abhishek Nayak, and Sivakumar Rathinam. Assessing Pavement Markings for Automated Vehicle Readiness. Report no. MN 2024-16. Minnesota. Department of Transportation. Office of Research & Innovation, 2024. https://rosap.ntl.bts.gov/view/dot/77435.
Pike, Adam, et al. Assessing Pavement Markings for Automated Vehicle Readiness. Minnesota. Department of Transportation. Office of Research & Innovation, 2024, Report no. MN 2024-16, ROSA P. https://rosap.ntl.bts.gov/view/dot/77435.
Although the literature on autonomous vehicles (AVs) has been growing with a focus on adoption, expected changes in travel behavior, and travel demand and land use in the future, few studies have analyzed envisioned activities in AVs, which will affect all those outcomes at the micro level. To address this gap, this study examines preferred activit
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Kim, I., Lee, Y., Mokhtarian, P. L., & Circella, G. (2024). Heterogeneous Preferences for Activities While Traveling in Autonomous Vehicles: Relationships with Travel Contexts and Attitudes. Center for Teaching Old Models New Tricks (TOMNET). https://rosap.ntl.bts.gov/view/dot/77643
Kim, Ilsu, Yongsung Lee, Patricia L. Mokhtarian, and Giovanni Circella. Heterogeneous Preferences for Activities While Traveling in Autonomous Vehicles: Relationships with Travel Contexts and Attitudes. Center for Teaching Old Models New Tricks (TOMNET), 2024. https://rosap.ntl.bts.gov/view/dot/77643.
Kim, Ilsu, et al. Heterogeneous Preferences for Activities While Traveling in Autonomous Vehicles: Relationships with Travel Contexts and Attitudes. Center for Teaching Old Models New Tricks (TOMNET), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/77643.
This project convened a series of meetings and workshops to prioritize listening to multi-sector stakeholders from local government, advocacy, and industry in US cities where autonomous vehicles are operating. The objective was to listen and learn from all stakeholders, raise issues surrounding accessibility and equity, and to solicit responses. Ke
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D’Agostino, M. C., Cooper, E. M., & Venkataram, P. S. (2024). Experiences With Autonomous Vehicle in U.S. Cities (Report No. UC-iTS-RIMI-5F). University of California Institute of Transportation Studies. https://doi.org/10.7922/G2348HQ8
D’Agostino, Mollie Cohen, Elliott Michael Cooper, and Prashanth S Venkataram. Experiences With Autonomous Vehicle in U.S. Cities. Report no. UC-iTS-RIMI-5F. University of California Institute of Transportation Studies, 2024. https://doi.org/10.7922/G2348HQ8.
D’Agostino, Mollie Cohen, et al. Experiences With Autonomous Vehicle in U.S. Cities. University of California Institute of Transportation Studies, 2024, Report no. UC-iTS-RIMI-5F, ROSA P. https://doi.org/10.7922/G2348HQ8.
This study evaluated connected and autonomous vehicle (CAV) crash reporting practices across the United States, emphasizing the importance of standardized reporting and legislation for the safe deployment of CAVs on public roads. Through a survey of state transportation officials and a review of current practices and legislation, the study identifi
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Acharya, S., Rahman, M. A., & Mekker, M. (2024). State of the Practice of Crash Reporting in the U.S. and Implications for CAV Safety Assessment (Report No. MPC 24-521). Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/75299
Acharya, Sailesh, Md Ashikur Rahman, and Michelle Mekker. State of the Practice of Crash Reporting in the U.S. and Implications for CAV Safety Assessment. Report no. MPC 24-521. Mountain-Plains Consortium, 2024. https://rosap.ntl.bts.gov/view/dot/75299.
Acharya, Sailesh, et al. State of the Practice of Crash Reporting in the U.S. and Implications for CAV Safety Assessment. Mountain-Plains Consortium, 2024, Report no. MPC 24-521, ROSA P. https://rosap.ntl.bts.gov/view/dot/75299.
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