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.
Autonomous vehicles (AVs) are expected to transform transportation systems and reshape workforce needs across engineering and related fields. Although existing AV workforce development efforts often emphasize electrical engineering, computer science, and mechanical engineering, transportation engineering remains under represented despite its import
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Abadi, M. G., & Moghadam, R. (. (2026). Preparing Today's Workforce for Tomorrow's Autonomous Transportation: Bridging Electrical and Civil Engineering Disciplines (Report No. 26-17). San Jose State University. College of Business. Mineta Transportation Institute. https://rosap.ntl.bts.gov/view/dot/92922
Abadi, Masoud Ghodrat and Rohollah (Roham) Moghadam. Preparing Today's Workforce for Tomorrow's Autonomous Transportation: Bridging Electrical and Civil Engineering Disciplines. Report no. 26-17. San Jose State University. College of Business. Mineta Transportation Institute, 2026. https://rosap.ntl.bts.gov/view/dot/92922.
Abadi, Masoud Ghodrat, and Rohollah (Roham) Moghadam Preparing Today's Workforce for Tomorrow's Autonomous Transportation: Bridging Electrical and Civil Engineering Disciplines. San Jose State University. College of Business. Mineta Transportation Institute, 2026, Report no. 26-17, ROSA P. https://rosap.ntl.bts.gov/view/dot/92922.
Although significant progress has been made in automated driving technologies, technical challenges still exist, especially for complex Operational Design Domains (ODDs). A low-cost roadside device system, the Connected Reference Marker (CRM) System, has been developed to support CAVs in those ODDs. The CRM system can facilitate CAV localization by
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Supporting Files
Cheng, Y., Parker, S. T., Ran, B., Fu, S., Liu, J., Gan, R., & You, J. (2026). Prototyping a Low-Cost Roadside Device System for Cooperative Automated Driving: Integrated CRM/UWB Localization, Work Zone Geometry Reconstruction, and WZDx-Ready Navigation Refinement (Report No. 118). University of Michigan. Center for Connected and Automated Transportation. https://rosap.ntl.bts.gov/view/dot/93284
Cheng, Yang, Steven T. Parker, Bin Ran, Sicheng Fu, Jiaxi Liu, Rui Gan, and Junwei You. Prototyping a Low-Cost Roadside Device System for Cooperative Automated Driving: Integrated CRM/UWB Localization, Work Zone Geometry Reconstruction, and WZDx-Ready Navigation Refinement. Report no. 118. University of Michigan. Center for Connected and Automated Transportation, 2026. https://rosap.ntl.bts.gov/view/dot/93284.
Cheng, Yang, et al. Prototyping a Low-Cost Roadside Device System for Cooperative Automated Driving: Integrated CRM/UWB Localization, Work Zone Geometry Reconstruction, and WZDx-Ready Navigation Refinement. University of Michigan. Center for Connected and Automated Transportation, 2026, Report no. 118, ROSA P. https://rosap.ntl.bts.gov/view/dot/93284.
Autonomous and connected vehicle perception capabilities can be enhanced with roadside sensing, by increasing the spatial range of traffic events for all road users and revealing 'blind spots' where onboard vehicle sensors cannot 'see'. A multi-lane roundabout intersection can manifest such scenarios, that introduce perception and control challenge
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Morris, T. L., Shenoy, V. G., Shepard, B., & Morellas, V. (2026). Roundabout Connected/Automated Vehicle Active Inference Control Strategies to Improve Safety for All Users: Phase-I (Report No. CTS 26-08). University of Michigan. Center for Connected and Automated Transportation. https://hdl.handle.net/11299/281279
Morris, Ted L., Varadaraya G. Shenoy, Ben Shepard, and Vassilios Morellas. Roundabout Connected/Automated Vehicle Active Inference Control Strategies to Improve Safety for All Users: Phase-I. Report no. CTS 26-08. University of Michigan. Center for Connected and Automated Transportation, 2026. https://hdl.handle.net/11299/281279.
Morris, Ted L., et al. Roundabout Connected/Automated Vehicle Active Inference Control Strategies to Improve Safety for All Users: Phase-I. University of Michigan. Center for Connected and Automated Transportation, 2026, Report no. CTS 26-08, ROSA P. https://hdl.handle.net/11299/281279.
United States. Department of Transportation. Federal Highway Administration
2026-06-01
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CDA enables automated vehicles, infrastructure, and other road users to cooperate through vehicle-to-everything (V2X) communication, with the goal of improving the safety and mobility of transportation systems. To realize these benefits, CDA systems need to perform reliably not only under typical operating conditions but also in edge cases that do
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United States. Department of Transportation. Federal Highway Administration (2026). Scenario Development for Cooperative Driving Automation (CDA) Prototypes [Fact Sheet] (Report No. FHWA-HRT-26-060). United States. Department of Transportation. Federal Highway Administration. https://doi.org/10.21949/jz0h-7102
United States. Department of Transportation. Federal Highway Administration. Scenario Development for Cooperative Driving Automation (CDA) Prototypes [Fact Sheet]. Report no. FHWA-HRT-26-060. United States. Department of Transportation. Federal Highway Administration, 2026. https://doi.org/10.21949/jz0h-7102.
United States. Department of Transportation. Federal Highway Administration Scenario Development for Cooperative Driving Automation (CDA) Prototypes [Fact Sheet]. United States. Department of Transportation. Federal Highway Administration, 2026, Report no. FHWA-HRT-26-060, ROSA P. https://doi.org/10.21949/jz0h-7102.
The Autonomous Truck-Mounted Attenuator (ATMA) system is gaining traction for its potential to increase worker safety in short-term mobile operations. Despite its safety advantages, ATMA is still a relatively new technology, and decision-makers like the Department of Transportation (DOTs) face operational and financial viability issues. There are f
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Aziz, H. A., & Roes, C. (2026). ATMA Deployment Toolkit: Cost-Benefit Analysis and Planning Guide for State DOTs (Report No. CDOT-2026-03). Colorado. Dept. of Transportation. Applied Research and Innovation Branch. https://rosap.ntl.bts.gov/view/dot/92469
Aziz, HM Abdul and Chelsea Roes. ATMA Deployment Toolkit: Cost-Benefit Analysis and Planning Guide for State DOTs. Report no. CDOT-2026-03. Colorado. Dept. of Transportation. Applied Research and Innovation Branch, 2026. https://rosap.ntl.bts.gov/view/dot/92469.
Aziz, HM Abdul, and Chelsea Roes ATMA Deployment Toolkit: Cost-Benefit Analysis and Planning Guide for State DOTs. Colorado. Dept. of Transportation. Applied Research and Innovation Branch, 2026, Report no. CDOT-2026-03, ROSA P. https://rosap.ntl.bts.gov/view/dot/92469.
Driverless racecar competitions are often perceived as recreational. However, their academic, industry, and research benefits are potentially tremendous, as several significant lessons can be learned from these competitions to advance autonomous mobility. This study was motivated (and its conduction facilitated) by the active involvement of the aut
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Tsunokowa, K., & Labi, S. (2026). High-Speed Driverless Racing: Lessons for Autonomous Mobility (Report No. 115). Center for Connected and Automated Transportation. Purdue University. http://dx.doi.org/10.5703/1288284318631
Tsunokowa, Koji and Samuel Labi. High-Speed Driverless Racing: Lessons for Autonomous Mobility. Report no. 115. Center for Connected and Automated Transportation. Purdue University, 2026. http://dx.doi.org/10.5703/1288284318631.
Tsunokowa, Koji, and Samuel Labi High-Speed Driverless Racing: Lessons for Autonomous Mobility. Center for Connected and Automated Transportation. Purdue University, 2026, Report no. 115, ROSA P. http://dx.doi.org/10.5703/1288284318631.
The goal of this work was to explore whether the complexity of different rural intersections influenced driver trust and comfort in a conditionally automated vehicle (i.e., Level 3 SAE) navigating the intersection for them. In an online survey platform (UMN Qualtrics), 271 participants watched five brief curated videos of a simulated automated vehi
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Supporting Files
Morris, N. L., Schwieters, K. R., Drahos, B. A., & Easterlund, P. (2026). Examining Driver Takeover Decisions and Trust of AVs at Rural Intersections (Report No. CTS 26-05). University of Michigan. Center for Connected and Automated Transportation. https://rosap.ntl.bts.gov/view/dot/89830
Morris, Nichole L., Katelyn R. Schwieters, Bradley A. Drahos, and Peter Easterlund. Examining Driver Takeover Decisions and Trust of AVs at Rural Intersections. Report no. CTS 26-05. University of Michigan. Center for Connected and Automated Transportation, 2026. https://rosap.ntl.bts.gov/view/dot/89830.
Morris, Nichole L., et al. Examining Driver Takeover Decisions and Trust of AVs at Rural Intersections. University of Michigan. Center for Connected and Automated Transportation, 2026, Report no. CTS 26-05, ROSA P. https://rosap.ntl.bts.gov/view/dot/89830.
The goal of this work was to explore whether the complexity of different rural intersections influenced driver trust and comfort in a conditionally automated vehicle (i.e., Level 3 SAE) navigating the intersection for them. In an online survey platform (UMN Qualtrics), 271 participants watched five brief curated videos of a simulated automated vehi
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DatasetSupporting Files
Morris, N. L., Schwieters, K. R., Drahos, B. A., & Easterlund, P. (2026). Examining Driver Takeover Decisions and Trust of AVs at Rural Intersections [supporting dataset]. University of Michigan. Center for Connected and Automated Transportation. https://hdl.handle.net/11299/278984
Morris, Nichole L., Katelyn R. Schwieters, Bradley A. Drahos, and Peter Easterlund. Examining Driver Takeover Decisions and Trust of AVs at Rural Intersections [supporting dataset]. University of Michigan. Center for Connected and Automated Transportation, 2026. https://hdl.handle.net/11299/278984.
Morris, Nichole L., et al. Examining Driver Takeover Decisions and Trust of AVs at Rural Intersections [supporting dataset]. University of Michigan. Center for Connected and Automated Transportation, 2026, ROSA P. https://hdl.handle.net/11299/278984.
This study uses connected vehicle data to analyze speeding behavior on residential roads. A scalable pipeline processes trajectory data and supplements missing speed limits to generate summaries at OpenStreetMap's way ID level. The findings reveal a highly skewed distribution of both aggressive and reckless speeding. Based on a case study of Charlo
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Feng, S., Park, B. B., & Mondschein, A. (2026). Analyzing Residential Speeding Using Connected Vehicle Data: A Case Study in the Charlottesville, VA Area (Report No. SM43). Morgan State University.Safety and Mobility Advancement Regional Transportation and Economics Research Center (SMARTER) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/93348
Feng, Shi, B. Brian Park, and Andrew Mondschein. Analyzing Residential Speeding Using Connected Vehicle Data: A Case Study in the Charlottesville, VA Area. Report no. SM43. Morgan State University.Safety and Mobility Advancement Regional Transportation and Economics Research Center (SMARTER) University Transportation Center (UTC), 2026. https://rosap.ntl.bts.gov/view/dot/93348.
Feng, Shi, et al. Analyzing Residential Speeding Using Connected Vehicle Data: A Case Study in the Charlottesville, VA Area. Morgan State University.Safety and Mobility Advancement Regional Transportation and Economics Research Center (SMARTER) University Transportation Center (UTC), 2026, Report no. SM43, ROSA P. https://rosap.ntl.bts.gov/view/dot/93348.
This study investigated the effectiveness of a Human-in-the-Loop Cooperative Cruise Control (hC3) system in improving vehicle driving performance. Twenty participants experienced both manual and hC3 driving modes in realistic traffic situations by using a virtual reality (VR) based simulation framework. The study measured and evaluated different dr
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Park, B. B., Beigi, S. A., & Mu, Z. (2026). A Virtual Reality-Based Evaluation of Human-in-the-Loop Connected Cruise Control (hC3) (Report No. SM26). Morgan State University.Safety and Mobility Advancement Regional Transportation and Economics Research Center (SMARTER) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/93349
Park, B. Brian, S. Austin Beigi, and Zeyu Mu. A Virtual Reality-Based Evaluation of Human-in-the-Loop Connected Cruise Control (hC3). Report no. SM26. Morgan State University.Safety and Mobility Advancement Regional Transportation and Economics Research Center (SMARTER) University Transportation Center (UTC), 2026. https://rosap.ntl.bts.gov/view/dot/93349.
Park, B. Brian, et al. A Virtual Reality-Based Evaluation of Human-in-the-Loop Connected Cruise Control (hC3). Morgan State University.Safety and Mobility Advancement Regional Transportation and Economics Research Center (SMARTER) University Transportation Center (UTC), 2026, Report no. SM26, ROSA P. https://rosap.ntl.bts.gov/view/dot/93349.
This study provides INDOT with critical insights into major OEMs’ connected and autonomous vehicle (CAV) development plans and related research and demonstration activities in several surrounding states, guiding infrastructure investment and policy decisions. The report delivers three outputs that guide the CAV technologies implementation in Indian
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Cui, C., Lu, J., Zhou, Y., Mathew, A., Zhou, J., Wang, Z., & Chen, Y. (2026). Feasibility Study on the Development of Indiana’s Connected and Automated Vehicle Roadmap and Strategic Plan (Report No. FHWA/IN/JTRP-2026/03). Purdue University. Joint Transportation Research Program. https://doi.org/10.5703/1288284318611
Cui, Can, Juanwu Lu, Yupeng Zhou, Abin Mathew, Jue Zhou, Ziran Wang, and Yaobin Chen. Feasibility Study on the Development of Indiana’s Connected and Automated Vehicle Roadmap and Strategic Plan. Report no. FHWA/IN/JTRP-2026/03. Purdue University. Joint Transportation Research Program, 2026. https://doi.org/10.5703/1288284318611.
Cui, Can, et al. Feasibility Study on the Development of Indiana’s Connected and Automated Vehicle Roadmap and Strategic Plan. Purdue University. Joint Transportation Research Program, 2026, Report no. FHWA/IN/JTRP-2026/03, ROSA P. https://doi.org/10.5703/1288284318611.
This repository stores corresponding video and output data for the project,"Roundabout Connected/Automated Vehicle Active Inference Control Strategies to Improve Safety for All Users: Phase-I". The video is from a high resolution, 2992x2992 180-degree elevated fisheye camera deployed at the center of an urban arterial 2-lane Roundabout intersection
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DatasetSupporting Files
Morris, T. L., Shenoy, V. G., Shepard, B., & Morellas, V. (2026). Roundabout Connected/Automated Vehicle Active Inference Control Strategies to Improve Safety for All Users: Phase-1 [supporting dataset]. University of Michigan. Center for Connected and Automated Transportation. https://hdl.handle.net/11299/282868
Morris, Ted L., Varadaraya G. Shenoy, Ben Shepard, and Vassilios Morellas. Roundabout Connected/Automated Vehicle Active Inference Control Strategies to Improve Safety for All Users: Phase-1 [supporting dataset]. University of Michigan. Center for Connected and Automated Transportation, 2026. https://hdl.handle.net/11299/282868.
Morris, Ted L., et al. Roundabout Connected/Automated Vehicle Active Inference Control Strategies to Improve Safety for All Users: Phase-1 [supporting dataset]. University of Michigan. Center for Connected and Automated Transportation, 2026, ROSA P. https://hdl.handle.net/11299/282868.
This paper presents a comprehensive comparative analysis of the primary perception technologies, LiDAR, Radar, Camera, and Sonar, that underpin modern intelligent transportation systems and autonomous vehicles. While numerous studies have examined individual sensor technologies, this paper's primary contribution lies in its holistic, cross-modal an
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Soltanirad, M., & Baghersad, M. (2025). Perception Technologies for Autonomous Transportation: A Comparative Analysis of LiDAR, Radar, Camera, and Sonar. Computational Research Progress in Applied Science & Engineering (CRPASE). https://doi.org/10.82042/crpase.11.4.2967
Soltanirad, Mohammad and Mahdi Baghersad. Perception Technologies for Autonomous Transportation: A Comparative Analysis of LiDAR, Radar, Camera, and Sonar. Computational Research Progress in Applied Science & Engineering (CRPASE), 2025. https://doi.org/10.82042/crpase.11.4.2967.
Soltanirad, Mohammad, and Mahdi Baghersad Perception Technologies for Autonomous Transportation: A Comparative Analysis of LiDAR, Radar, Camera, and Sonar. Computational Research Progress in Applied Science & Engineering (CRPASE), 2025, ROSA P. https://doi.org/10.82042/crpase.11.4.2967.
This report, the fourth in the series, documents continuing work identifying unnecessary/unintended regulatory barriers to self-certification and compliance verification of innovative vehicle designs with automated driving systems that lack manually operated driving controls (steering wheel, brake pedal, etc.). It gives technical translation option
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Supporting Files
Stowe, L., Kizyma, D., Krum, A., McNeil, J., Kefauver, K., Haley, P., Weinstein, K., Hardy, W. N., Bedwell, K., Trimble, T. E., & Chaka, M. (2025). FMVSS Considerations for Vehicles with Automated Driving Systems: Volume 4 (Report No. DOT HS 813 755). United States. Department of Transportation. National Highway Traffic Safety Administration. https://doi.org/10.21949/hq4a-6m74
Stowe, Loren, David Kizyma, Andrew Krum, Joshua McNeil, Kevin Kefauver, Patrick Haley, and Kenneth Weinstein, et al.. FMVSS Considerations for Vehicles with Automated Driving Systems: Volume 4. Report no. DOT HS 813 755. United States. Department of Transportation. National Highway Traffic Safety Administration, 2025. https://doi.org/10.21949/hq4a-6m74.
Stowe, Loren, et al. FMVSS Considerations for Vehicles with Automated Driving Systems: Volume 4. United States. Department of Transportation. National Highway Traffic Safety Administration, 2025, Report no. DOT HS 813 755, ROSA P. https://doi.org/10.21949/hq4a-6m74.
As the number of vehicles equipped with automated driving systems (ADS) that are using cooperative driving automation (CDA) technology to communicate safety messages are anticipated to increase in the future, maintaining the safety and efficient operation of roadways is important. ADS-equipped vehicles and CDA technology need to operate smoothly an
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Calvo, J., Svancara, A., Chao, S. F., Lee, Y. C., Jannat, M., & Arnold, M. (2025). Comparing Merging Behaviors of Drivers with Vehicles Equipped with Level 3 Automation and Connected Messaging when Merging in a Mixed Vehicle Fleet Environment of Various Traffic Densities (Report No. FHWA-HRT-25-051). United States. Federal Highway Administration. Office of Safety and Operations Research and Development. https://rosap.ntl.bts.gov/view/dot/88184
Calvo, Jose, Austin Svancara, Szu-Fu Chao, Yi-Ching Lee, Mafruhatul Jannat, and Michelle Arnold. Comparing Merging Behaviors of Drivers with Vehicles Equipped with Level 3 Automation and Connected Messaging when Merging in a Mixed Vehicle Fleet Environment of Various Traffic Densities. Report no. FHWA-HRT-25-051. United States. Federal Highway Administration. Office of Safety and Operations Research and Development, 2025. https://rosap.ntl.bts.gov/view/dot/88184.
Calvo, Jose, et al. Comparing Merging Behaviors of Drivers with Vehicles Equipped with Level 3 Automation and Connected Messaging when Merging in a Mixed Vehicle Fleet Environment of Various Traffic Densities. United States. Federal Highway Administration. Office of Safety and Operations Research and Development, 2025, Report no. FHWA-HRT-25-051, ROSA P. https://rosap.ntl.bts.gov/view/dot/88184.
Low volume rural roads present special challenges for AVs, since they can be narrow, often will not have right-side lane markers, may not even have center lines, and may not be plowed for snow removal. Rural traffic intersections can have missing delineation and signage that are normally provided on higher volume roadways. All of these issues pose
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Rajamani, R., Alai, H., Sharma, G., Kyong, H., & Pushpalayam, N. (2025). Autonomous Vehicle Challenges for the US Rural Midwest (Report No. CTS 25-14). University of Michigan. Center for Connected and Automated Transportation. https://rosap.ntl.bts.gov/view/dot/88289
Rajamani, Rajesh, Hamidreza Alai, Gaurav Sharma, Hongjoon Kyong, and Navaneeth Pushpalayam. Autonomous Vehicle Challenges for the US Rural Midwest. Report no. CTS 25-14. University of Michigan. Center for Connected and Automated Transportation, 2025. https://rosap.ntl.bts.gov/view/dot/88289.
Rajamani, Rajesh, et al. Autonomous Vehicle Challenges for the US Rural Midwest. University of Michigan. Center for Connected and Automated Transportation, 2025, Report no. CTS 25-14, ROSA P. https://rosap.ntl.bts.gov/view/dot/88289.
Pedestrian safety remains a major challenge in urban transportation, especially when connected and automated vehicles (CAVs) must operate under occlusion, limited line-of-sight, and unpredictable pedestrian behavior. Pedestrians hidden by parked vehicles, roadside obstacles, or complex roadway geometry may not be detected in time by onboard sensors
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Yang, D., Taherpour, A., Jeihani, M., Cheng, L., & Yang, T. (2025). Development of a Pedestrian Collision Avoidance System for Connected and Autonomous Vehicles With Cooperative Perception (Report No. SM21). Morgan State University. Sustainable Mobility and Accessibility Regional Transportation Equity Research Center (SMARTER) Region 3 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/92777
Yang, Di, Abolfazl Taherpour, Mansoureh Jeihani, Lei Cheng, and Terry Yang. Development of a Pedestrian Collision Avoidance System for Connected and Autonomous Vehicles With Cooperative Perception. Report no. SM21. Morgan State University. Sustainable Mobility and Accessibility Regional Transportation Equity Research Center (SMARTER) Region 3 University Transportation Center (UTC), 2025. https://rosap.ntl.bts.gov/view/dot/92777.
Yang, Di, et al. Development of a Pedestrian Collision Avoidance System for Connected and Autonomous Vehicles With Cooperative Perception. Morgan State University. Sustainable Mobility and Accessibility Regional Transportation Equity Research Center (SMARTER) Region 3 University Transportation Center (UTC), 2025, Report no. SM21, ROSA P. https://rosap.ntl.bts.gov/view/dot/92777.
This research examines how Connected and Autonomous Vehicle (CAV) deployments can be made compatible with Complete Streets objectives through strategic infrastructure design and systematic interaction management to optimize urban space utilization. Urban transportation systems face increasing pressure to accommodate both autonomous vehicle technolo
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Mahmassani, H. S., Hegde, S. J., Raymer, M., & Khakpour, A. (2025). Making CAV Deployments Compatible with Complete Streets Objectives for Safe and Efficient Operation Phase I (Report No. CCAT-NU-2025-3). Center for Connected and Automated Transportation. Northwestern University. https://rosap.ntl.bts.gov/view/dot/87580
Mahmassani, Hani S., Sharika J. Hegde, Meredith Raymer, and Amirmohammad Khakpour. Making CAV Deployments Compatible with Complete Streets Objectives for Safe and Efficient Operation Phase I. Report no. CCAT-NU-2025-3. Center for Connected and Automated Transportation. Northwestern University, 2025. https://rosap.ntl.bts.gov/view/dot/87580.
Mahmassani, Hani S., et al. Making CAV Deployments Compatible with Complete Streets Objectives for Safe and Efficient Operation Phase I. Center for Connected and Automated Transportation. Northwestern University, 2025, Report no. CCAT-NU-2025-3, ROSA P. https://rosap.ntl.bts.gov/view/dot/87580.
Accurate estimation of the tire-road friction coefficient (TRFC) is essential for ensuring safe vehicle control, particularly under adverse road conditions. Conventional approaches typically rely on naturalistic driving data from regular vehicles, which operate under mild acceleration and braking and therefore provide limited slip excitation and in
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Li, X. (., Ouyang, Y., Liang, Z., Beojone, C. V., & Huang, H. (2025). Roadway Friction Screening and Measurement With Automated Vehicle Telematics and Control (Report No. AWD-001401). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.21231/8trv-9w17
Li, Xiaopeng (Shaw), Yanfeng Ouyang, Zhaohui Liang, Caio V. Beojone, and Heye Huang. Roadway Friction Screening and Measurement With Automated Vehicle Telematics and Control. Report no. AWD-001401. University of Michigan. Center for Connected and Automated Transportation, 2025. https://doi.org/10.21231/8trv-9w17.
Li, Xiaopeng (Shaw), et al. Roadway Friction Screening and Measurement With Automated Vehicle Telematics and Control. University of Michigan. Center for Connected and Automated Transportation, 2025, Report no. AWD-001401, ROSA P. https://doi.org/10.21231/8trv-9w17.
This report summarizes progress made on two problems of central importance to achieving increased safety in autonomous urban driving: (1) the development of end-to-end frameworks for cooperative perception, tracking and planning, and (2) the development of real-time strategies for collision avoidance when collisions are predicted. With respect to t
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Smith, S. F., Dolan, J. M., Chiu, H. K., & Lyu, Y. (2025). Cooperative Sensing of Vulnerable Road Users and Real-Time Response to Potential Collisions via Vehicle and Infrastructure Communication (Report No. 511). Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/86527
Smith, Stephen F., John M Dolan, Hsu-Kuang Chiu, and Yiwei Lyu. Cooperative Sensing of Vulnerable Road Users and Real-Time Response to Potential Collisions via Vehicle and Infrastructure Communication. Report no. 511. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2025. https://rosap.ntl.bts.gov/view/dot/86527.
Smith, Stephen F., et al. Cooperative Sensing of Vulnerable Road Users and Real-Time Response to Potential Collisions via Vehicle and Infrastructure Communication. Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC), 2025, Report no. 511, ROSA P. https://rosap.ntl.bts.gov/view/dot/86527.
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