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 paper, a physics-informed machine learning approach is proposed for the lane changing of autonomous vehicles. Combining vehicle longitudinal and lateral control, the lane changing problem is formulated as a nonlinear motion planning and control optimization problem based on model predictive control (MPC). To address the computational challe
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Li, X., Ozbay, K., & Jiang, Z. P. (2025). Physics-informed Machine Learning for Autonomous Vehicle Control. 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/86347
Li, Xianning, Kaan Ozbay, and Zhong-Ping Jiang. Physics-informed Machine Learning for Autonomous Vehicle Control. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC), 2025. https://rosap.ntl.bts.gov/view/dot/86347.
Li, Xianning, et al. Physics-informed Machine Learning for Autonomous Vehicle Control. Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC), 2025, ROSA P. https://rosap.ntl.bts.gov/view/dot/86347.
As we enter the next era of autonomous driving, robo-vehicles (which serve as low-cost and fully compliant drivers) are replacing conventional chauffeured services in the mobility market. During just the last few years, companies like Waymo Inc. and Cruise Inc. have already offered fully driverless robo-taxi services to the general public in cities
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Shen, S., Zhai, Y., & Ouyang, Y. (2024). Planning and Dynamic Management of Autonomous Modular Mobility Services (Report No. ICT-24-029). Illinois Center for Transportation. https://doi.org/10.36501/0197-9191/24-029
Shen, Shiyu, Yuhui Zhai, and Yanfeng Ouyang. Planning and Dynamic Management of Autonomous Modular Mobility Services. Report no. ICT-24-029. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-029.
Shen, Shiyu, et al. Planning and Dynamic Management of Autonomous Modular Mobility Services. Illinois Center for Transportation, 2024, Report no. ICT-24-029, ROSA P. https://doi.org/10.36501/0197-9191/24-029.
Cost-effective collection and distribution of intersection data are needed to facilitate traffic operations at intersections in the HDV era and particularly, in the prospective era of CAVs. Existing methods are time consuming and costly. Phase I of this research (executed under CCAT Project Nr. 71) developed a cost-effective intersection data colle
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Gowda, M., Fehr, W., Balmos, A., Ajagu, R., Hong, D., Krogmeier, J. V., Abbas, M. M., & Labi, S. (2024). Economical Acquisition of Intersection Data to Facilitate Cav Operations: Phase II (Implementation) (Report No. 86). Center for Connected and Automated Transportation. Purdue University. http://dx.doi.org/10.5703/1288284317851
Gowda, Manish, Walt Fehr, Andrew Balmos, Richard Ajagu, David Hong, James V. Krogmeier, Montasir M. Abbas, and Samuel Labi. Economical Acquisition of Intersection Data to Facilitate Cav Operations: Phase II (Implementation). Report no. 86. Center for Connected and Automated Transportation. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317851.
Gowda, Manish, et al. Economical Acquisition of Intersection Data to Facilitate Cav Operations: Phase II (Implementation). Center for Connected and Automated Transportation. Purdue University, 2024, Report no. 86, ROSA P. http://dx.doi.org/10.5703/1288284317851.
Automated vehicle (AV) functionality depends on perceiving and understanding the surrounding roadway environment and infrastructure elements. SAE International® Level 2™ driving automation systems use sensors to perceive the environment and maintain a set headway with lead vehicles while remaining centered in the lane (SAE International, 2020). Alt
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Sanchez, R., Ahmed, A., Chao, S. F., Weaver, S., Eisert, J., & Cobb, D. P. (2024). Effects of Vehicle Automation and Cooperative Driving Messaging on Driver Behavior When Passing a Bicyclist on a Shared Roadway (Report No. FHWA-HRT-25-019). United States. Federal Highway Administration. Office of Safety and Operations Research and Development. https://doi.org/10.21949/1521536
Sanchez, Robert, Ananna Ahmed, Szu-Fu Chao, Starla Weaver, Jesse Eisert, and Douglas P Cobb. Effects of Vehicle Automation and Cooperative Driving Messaging on Driver Behavior When Passing a Bicyclist on a Shared Roadway. Report no. FHWA-HRT-25-019. United States. Federal Highway Administration. Office of Safety and Operations Research and Development, 2024. https://doi.org/10.21949/1521536.
Sanchez, Robert, et al. Effects of Vehicle Automation and Cooperative Driving Messaging on Driver Behavior When Passing a Bicyclist on a Shared Roadway. United States. Federal Highway Administration. Office of Safety and Operations Research and Development, 2024, Report no. FHWA-HRT-25-019, ROSA P. https://doi.org/10.21949/1521536.
This study explored new vehicle to infrastructure (V2I) technology in construction work zones (CWZ), where speeding, unsafe driving behaviors, and drivers' failure to obey traffic signs contribute significantly to elevated accident rates and fatalities. The objective of this research to advance CWZ safety by evaluating the potential of 3-axis magne
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Sakulneya, A., & Roesler, J. R. (2024). Smart Construction Work-Zone Safety with V2I Passive Material Sensing (Report No. ICT-24-027). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.36501/0197-9191/24-027
Sakulneya, Apidej and Jeffery R. Roesler. Smart Construction Work-Zone Safety with V2I Passive Material Sensing. Report no. ICT-24-027. University of Michigan. Center for Connected and Automated Transportation, 2024. https://doi.org/10.36501/0197-9191/24-027.
Sakulneya, Apidej, and Jeffery R. Roesler Smart Construction Work-Zone Safety with V2I Passive Material Sensing. University of Michigan. Center for Connected and Automated Transportation, 2024, Report no. ICT-24-027, ROSA P. https://doi.org/10.36501/0197-9191/24-027.
Vehicle-to-Everything (V2X) technology, the ability of vehicles to communicate with each other, with the roadside infrastructure, and with other travelers such as pedestrians, presents the opportunity to prevent crashes, save lives, and to improve the efficiency and sustainability of our transportation network. Across the U.S., State and local agen
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Hatcher, S. G., McManus, I., Kuruvilla, E., Chang, J., Bare, K., & English, T. (2024). Vehicle-to-Everything (V2X) Deployer Resource (Report No. FHWA-JPO-24-149). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/79215
Hatcher, S. Gregory, Ian McManus, Eapen Kuruvilla, James Chang, Kelly Bare, and Tony English. Vehicle-to-Everything (V2X) Deployer Resource. Report no. FHWA-JPO-24-149. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2024. https://rosap.ntl.bts.gov/view/dot/79215.
Hatcher, S. Gregory, et al. Vehicle-to-Everything (V2X) Deployer Resource. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2024, Report no. FHWA-JPO-24-149, ROSA P. https://rosap.ntl.bts.gov/view/dot/79215.
Truck platooning can potentially increase the operational efficiency of freight movement on U.S. corridors, improving commercial productivity and economic vibrancy. Predicting the trajectory of each leading vehicle in an autonomous truck platoon using Artificial Intelligence (AI) can enhance platoon efficiency during unavailability of the real-time
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Biswas, P. K., Salek, M. S., Chowdhury, M., Comert, G., Michalaka, D., Mwakalonge, J. L., Huynh, N., Charles, F., Ahmed, F., & Tine, J. M. (2024). Quantum Artificial Intelligence-Supported Trajectory Prediction for an Autonomous Truck Platoon (Report No. Final Report (2021 - 2024)). Center for Connected Multimodal Mobility, Clemson University. https://rosap.ntl.bts.gov/view/dot/80354
Biswas, Pronab Kumar, M Sabbir Salek, Mashrur Chowdhury, Gurcan Comert, Dimitra Michalaka, Judith L. Mwakalonge, Nathan Huynh, Frank Charles, Fahim Ahmed, and Jean Michel Tine. Quantum Artificial Intelligence-Supported Trajectory Prediction for an Autonomous Truck Platoon. Report no. Final Report (2021 - 2024). Center for Connected Multimodal Mobility, Clemson University, 2024. https://rosap.ntl.bts.gov/view/dot/80354.
Biswas, Pronab Kumar, et al. Quantum Artificial Intelligence-Supported Trajectory Prediction for an Autonomous Truck Platoon. Center for Connected Multimodal Mobility, Clemson University, 2024, Report no. Final Report (2021 - 2024), ROSA P. https://rosap.ntl.bts.gov/view/dot/80354.
Minnesota. Department of Transportation. Research Services & Library
2024-11-20
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The MnDOT Autonomous Bus Pilot project consisted of deploying a Level 4 shuttle provided by EasyMile on the MnROAD facility. The bus was used for several rounds of public demonstrations as well as testing at the MnROAD facility during winter conditions. The bus could hold up to 12 people and had a range of typical driving speeds from 2 to 11 miles
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Minnesota. Department of Transportation. Research Services & Library (2024). Project Summary: MnDOT Autonomous Bus Pilot. Minnesota. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/81966
Minnesota. Department of Transportation. Research Services & Library. Project Summary: MnDOT Autonomous Bus Pilot. Minnesota. Department of Transportation, 2024. https://rosap.ntl.bts.gov/view/dot/81966.
Minnesota. Department of Transportation. Research Services & Library Project Summary: MnDOT Autonomous Bus Pilot. Minnesota. Department of Transportation, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/81966.
The Bear Tracks Automated Shuttle Pilot was a research project that included the 12-month operation (August 2022-July 2023) of a Level 3 Automated Vehicle (AV) Shuttle along a 1.5-mile-long route in the city of White Bear Lake. The shuttle itself was a self-driving, electric, multi-passenger vehicle that drove at a speed between 10-12 miles per hou
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Minnesota. Department of Transportation, AECOM, Newtrax, Navya, & City of White Bear Lake (2024). Project Summary: White Bear Lake Automated Shuttle Pilot: Bear Tracks. Minnesota. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/82266
Minnesota. Department of Transportation, AECOM, Newtrax, Navya, and City of White Bear Lake. Project Summary: White Bear Lake Automated Shuttle Pilot: Bear Tracks. Minnesota. Department of Transportation, 2024. https://rosap.ntl.bts.gov/view/dot/82266.
Minnesota. Department of Transportation, et al. Project Summary: White Bear Lake Automated Shuttle Pilot: Bear Tracks. Minnesota. Department of Transportation, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/82266.
This project focuses on the technological transfer of a robust perception algorithm previously developed to mitigate adversarial attacks, transforming it into a practical software tool with an intuitive interface. The initiative builds upon the prior project, Securing Deep Learning against Adversarial Attacks for Connected and Automated Vehicles, w
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Pisu, P., Comert, G., Begashaw, N., Zhao, C., & Vadnerkar, K. (2024). A Software Tool for Securing Deep Learning Against Adversarial Attacks for CAVs. Center for Connected Multimodal Mobility, Clemson University. https://rosap.ntl.bts.gov/view/dot/83806
Pisu, Pierluigi, Gurcan Comert, Negash Begashaw, Chunheng Zhao, and Kalpit Vadnerkar. A Software Tool for Securing Deep Learning Against Adversarial Attacks for CAVs. Center for Connected Multimodal Mobility, Clemson University, 2024. https://rosap.ntl.bts.gov/view/dot/83806.
Pisu, Pierluigi, et al. A Software Tool for Securing Deep Learning Against Adversarial Attacks for CAVs. Center for Connected Multimodal Mobility, Clemson University, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/83806.
Contribution: An autonomous vehicle hardware platform, called F1TENTH, is developed for teaching autonomous systems hands-on. This project will design and develop the education modules and software stack for teaching at various educational levels with the theme of ``racing" and competitions that replace exams. Background: College-level robotics cou
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Mangharam, R. (2024). F1Tenth Autonomous Racing Course & Competition (Report No. 445). Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/78691
Mangharam, Rahul. F1Tenth Autonomous Racing Course & Competition. Report no. 445. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/78691.
Mangharam, Rahul F1Tenth Autonomous Racing Course & Competition. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, Report no. 445, ROSA P. https://rosap.ntl.bts.gov/view/dot/78691.
Over the past decade, self-driving capability for all variants of on-street vehicles have promised safer and more efficient transportation. This remains “work in progress” with large unfilled gaps in addressing user-acceptance, safety, ethics, regulation, technology and the business model. Our goal is to develop the Open-source Autonomous Vehicle (
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Loeb, H. (2024). Safety Through Agility: Using Mixed Reality to Tune Shared Autonomy Systems (Report No. 446). Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/78708
Loeb, Helen. Safety Through Agility: Using Mixed Reality to Tune Shared Autonomy Systems. Report no. 446. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/78708.
Loeb, Helen Safety Through Agility: Using Mixed Reality to Tune Shared Autonomy Systems. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, Report no. 446, ROSA P. https://rosap.ntl.bts.gov/view/dot/78708.
Over the past decade, self-driving capability for all variants of on-street vehicles have promised safer and more efficient transportation. This remains “work in progress” with large unfilled gaps in addressing user-acceptance, safety, ethics, regulation, technology and the business model. Our goal is to develop the Open-source Autonomous Vehicle (
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Mangharam, R. (2024). AV4EV - Open-Source Autonomous Vehicle Software for Open-Standard Electric Vehicle Platforms (Report No. 443). Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/78707
Mangharam, Rahul. AV4EV - Open-Source Autonomous Vehicle Software for Open-Standard Electric Vehicle Platforms. Report no. 443. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/78707.
Mangharam, Rahul AV4EV - Open-Source Autonomous Vehicle Software for Open-Standard Electric Vehicle Platforms. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, Report no. 443, ROSA P. https://rosap.ntl.bts.gov/view/dot/78707.
Robot localization is the problem of finding a robot's pose using a map and sensor measurements, like LiDAR scans or camera images. It is crucial for any moving autonomous vehicle to interact with the physical world correctly. However, finding injective mappings between measurements and poses is difficult because sensor measurements from multiple d
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Mangharam, R. (2024). Low-cost Real-Time Learning-based Localization for Autonomous Systems (Report No. 441). Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/78719
Mangharam, Rahul. Low-cost Real-Time Learning-based Localization for Autonomous Systems. Report no. 441. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/78719.
Mangharam, Rahul Low-cost Real-Time Learning-based Localization for Autonomous Systems. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, Report no. 441, ROSA P. https://rosap.ntl.bts.gov/view/dot/78719.
Automated Vehicles (AV’s) can intermingle with pedestrians and cyclists when they are driving slowly in so- called “shared spaces”. In our previous work, we studied AVs' energy consumption and safety when occluded pedestrians appear suddenly in front of the AV. We will continue investigating and developing our "value of information" based approach
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Saim, E. M., Al-Shareeda, S., Redmill, K., & Ozgiiner, U. (2024). Control of Automated Vehicles in Vehicle-Pedestrian Environment. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/78212
Saim, E Muhammad, Sarah Al-Shareeda, Keith Redmill, and Umit Ozgiiner. Control of Automated Vehicles in Vehicle-Pedestrian Environment. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/78212.
Saim, E Muhammad, et al. Control of Automated Vehicles in Vehicle-Pedestrian Environment. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/78212.
Next generation connected transportation networks will require extensive communication capabilities with low-latency and high-reliability. Further, these systems rely on significant sensing capabilities in order to function, for example for autonomous routing. Joint communications and sensing is clearly going to be a pillar of 6G in order to facili
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Welling, T., & Yener, A. (2024). Secure Integrated Sensing and Communication with Transmitter Actions for Vehicular Communication. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77819
Welling, Truman and Aylin Yener. Secure Integrated Sensing and Communication with Transmitter Actions for Vehicular Communication. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC), 2024. https://rosap.ntl.bts.gov/view/dot/77819.
Welling, Truman, and Aylin Yener Secure Integrated Sensing and Communication with Transmitter Actions for Vehicular Communication. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/77819.
With advances in communication, computation, and control technologies, fleets of Connected Autonomous Vehicles (CAV) can be deployed to flexibly provide on-demand transportation services in the near future. Therefore, it is important to start the development of CAV testbeds and piloted projects for the deployment of CAVs to complement some of the o
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Karimoddini, A. (2024). Developing and Operationalizing a Testbed of Connected Automated Shuttles to Test and Develop CAV Applications in North Carolina (Report No. FHWA/NC/2022-16). North Carolina. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/84581
Karimoddini, Ali. Developing and Operationalizing a Testbed of Connected Automated Shuttles to Test and Develop CAV Applications in North Carolina. Report no. FHWA/NC/2022-16. North Carolina. Department of Transportation, 2024. https://rosap.ntl.bts.gov/view/dot/84581.
Karimoddini, Ali Developing and Operationalizing a Testbed of Connected Automated Shuttles to Test and Develop CAV Applications in North Carolina. North Carolina. Department of Transportation, 2024, Report no. FHWA/NC/2022-16, ROSA P. https://rosap.ntl.bts.gov/view/dot/84581.
The bipartite matching problem is widely applied in the field of transportation, e.g., to find optimal matches between supply and demand over time and space. Recent efforts have been made on developing analytical formulas to estimate the expected matching distance in bipartite matching with randomly distributed vertices in two- or higher-dimensiona
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Zhai, Y., Shen, S., & Ouyang, Y. (2024). Average Distance of Random Bipartite Matching in Discrete Networks (Report No. 161117). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.48550/arXiv.2409.18292
Zhai, Yuhui, Shiyu Shen, and Yanfeng Ouyang. Average Distance of Random Bipartite Matching in Discrete Networks. Report no. 161117. University of Michigan. Center for Connected and Automated Transportation, 2024. https://doi.org/10.48550/arXiv.2409.18292.
Zhai, Yuhui, et al. Average Distance of Random Bipartite Matching in Discrete Networks. University of Michigan. Center for Connected and Automated Transportation, 2024, Report no. 161117, ROSA P. https://doi.org/10.48550/arXiv.2409.18292.
While some congestion is expected at rural attractions such as national parks, theme parks, special sporting events, scenic points and the like, there are locations along the rural highway network that nearby attractions cause substantial congestion and/or unusually elevated traffic safety risk. This paper presents the case of two very popular tour
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Chen, R. B., Tallman, C., Garcia, P., Rajaure, T., Prevedouros, P. D., & Barros, R. D. M. (2024). Effects of Tourism on Rural Roads & Rural Delivery With CAV (Report No. INE/CSET 24.17). University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET). https://rosap.ntl.bts.gov/view/dot/78280
Chen, Roger B., Cody Tallman, Preston Garcia, Tribikram Rajaure, Panos D Prevedouros, and Rafaela De Melo Barros. Effects of Tourism on Rural Roads & Rural Delivery With CAV. Report no. INE/CSET 24.17. University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET), 2024. https://rosap.ntl.bts.gov/view/dot/78280.
Chen, Roger B., et al. Effects of Tourism on Rural Roads & Rural Delivery With CAV. University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET), 2024, Report no. INE/CSET 24.17, ROSA P. https://rosap.ntl.bts.gov/view/dot/78280.
The goal of this project is to study the critical scenario generation, design a testing framework, and use the generated scenarios to help the design of autonomous vehicle proving ground infrastructure aiming at the connected and autonomous vehicles evaluation. These scenarios should reflect the critical factors and risky driving conditions in the
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Zhao, D. (2024). Generating Safety-Critical Driving Scenarios for the Design of the CAV Proving-Ground - Using Domain Knowledge, Causality, and Large Language Models. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/78030
Zhao, Ding. Generating Safety-Critical Driving Scenarios for the Design of the CAV Proving-Ground - Using Domain Knowledge, Causality, and Large Language Models. Mobility21, Carnegie Mellon University, 2024. https://rosap.ntl.bts.gov/view/dot/78030.
Zhao, Ding Generating Safety-Critical Driving Scenarios for the Design of the CAV Proving-Ground - Using Domain Knowledge, Causality, and Large Language Models. Mobility21, Carnegie Mellon University, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/78030.
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