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, camera-based 3D object detection has gained widespread attention for its ability to achieve high performance with low computational cost. However, the robustness of these methods to adversarial attacks has not been thoroughly examined, especially when considering their deployment in safety-critical domains like autonomous driving.
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Xie, S., Li, Z., Wang, Z., & Xie, C. (2024). On the Adversarial Robustness of Camera-Based 3D Object Detection. University of California, Irvine. https://rosap.ntl.bts.gov/view/dot/73429
Xie, Shaoyuan, Zichao Li, Zeyu Wang, and Cihang Xie. On the Adversarial Robustness of Camera-Based 3D Object Detection. University of California, Irvine, 2024. https://rosap.ntl.bts.gov/view/dot/73429.
Xie, Shaoyuan, et al. On the Adversarial Robustness of Camera-Based 3D Object Detection. University of California, Irvine, 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/73429.
Intelligent road infrastructure consisting of sensors and communications is needed to deploy connected and automated vehicles (CAVs) on real highways. Such infrastructure can support the operation of CAVs (e.g., maneuver coordination and onboard energy management), and bridge the connectivity gap resulting from the currently low penetration of conn
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Wang, H., & Orosz, G. (2024). Improving the Efficiency of Trucks via CV2X Connectivity on Highways (Report No. CCAT Final Report No. 20). University of Michigan. Center for Connected and Automated Transportation. https://dx.doi.org/10.7302/21946
Wang, Hao and Gabor Orosz. Improving the Efficiency of Trucks via CV2X Connectivity on Highways. Report no. CCAT Final Report No. 20. University of Michigan. Center for Connected and Automated Transportation, 2024. https://dx.doi.org/10.7302/21946.
Wang, Hao, and Gabor Orosz Improving the Efficiency of Trucks via CV2X Connectivity on Highways. University of Michigan. Center for Connected and Automated Transportation, 2024, Report no. CCAT Final Report No. 20, ROSA P. https://dx.doi.org/10.7302/21946.
This project is concerned with adapting land use and transportation infrastructure for automated driving. Autonomous vehicles will likely yield a transformation of urban form, its land use and mobility system. We propose to establish quantitative modeling frameworks to analyze these impacts and implications. The frameworks will provide a quantifiab
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Yin, Y. (2024). Adapting Land Use and Infrastructure for Automated Driving: Part A (Report No. 8). University of Michigan. Center for Connected and Automated Transportation. https://dx.doi.org/10.7302/21950
Yin, Yafeng. Adapting Land Use and Infrastructure for Automated Driving: Part A. Report no. 8. University of Michigan. Center for Connected and Automated Transportation, 2024. https://dx.doi.org/10.7302/21950.
Yin, Yafeng Adapting Land Use and Infrastructure for Automated Driving: Part A. University of Michigan. Center for Connected and Automated Transportation, 2024, Report no. 8, ROSA P. https://dx.doi.org/10.7302/21950.
This study explored how children are restrained when traveling in ride-share vehicles. An observational survey was conducted from July to August 2022. The target population was children from birth to 12 years old transported in ride-share vehicles. About half of children observed were traveling unrestrained and the remainder were either using the v
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De Leonardis, D., Levi, S., Benedick, A. K., Eisenhauer, E., Ferg, R., & Petraglia, E. (2024). Child Passenger Safety Perception and Practices in Ride-Sharing Vehicles (Report No. DOT HS 813 532). United States. Department of Transportation. National Highway Traffic Safety Administration. https://rosap.ntl.bts.gov/view/dot/72946
De Leonardis, Doreen, Sharon Levi, Amy K Benedick, Elizabeth Eisenhauer, Robyn Ferg, and Elizabeth Petraglia. Child Passenger Safety Perception and Practices in Ride-Sharing Vehicles. Report no. DOT HS 813 532. United States. Department of Transportation. National Highway Traffic Safety Administration, 2024. https://rosap.ntl.bts.gov/view/dot/72946.
De Leonardis, Doreen, et al. Child Passenger Safety Perception and Practices in Ride-Sharing Vehicles. United States. Department of Transportation. National Highway Traffic Safety Administration, 2024, Report no. DOT HS 813 532, ROSA P. https://rosap.ntl.bts.gov/view/dot/72946.
This edition of Traffic Tech briefly describes a full report that provides insight into how children are being restrained when traveling in ride-sharing vehicles such as Uber and Lyft. From July to August 2022 researchers observed children 12 and younger transported in ride-share vehicles in the Mid-Atlantic and Northeastern regions. About half of
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Sifrit, K. J. (2024). Child Passenger Safety Perception and Practices in Ride-Sharing Vehicles [Traffic Tech] (Report No. DOT HS 813 533). United States. Department of Transportation. National Highway Traffic Safety Administration. https://rosap.ntl.bts.gov/view/dot/72945
Sifrit, Kathy J.. Child Passenger Safety Perception and Practices in Ride-Sharing Vehicles [Traffic Tech]. Report no. DOT HS 813 533. United States. Department of Transportation. National Highway Traffic Safety Administration, 2024. https://rosap.ntl.bts.gov/view/dot/72945.
Sifrit, Kathy J. Child Passenger Safety Perception and Practices in Ride-Sharing Vehicles [Traffic Tech]. United States. Department of Transportation. National Highway Traffic Safety Administration, 2024, Report no. DOT HS 813 533, ROSA P. https://rosap.ntl.bts.gov/view/dot/72945.
This paper proposes a novel physics-informed machine learning framework for motion planning and control of autonomous vehicles. By integrating longitudinal and lateral control, a nonlinear control problem is formulated using Model Predictive Control (MPC). To address computational challenges, a self-supervised framework, Recurrent Predictive Contro
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Li, X., Wang, Y., Ozbay, K., & Jiang, Z. P. (2024). Physics-informed Machine Learning with Heuristic Feedback Control Layer 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/86348
Li, Xianning, Yebin Wang, Kaan Ozbay, and Zhong-Ping Jiang. Physics-informed Machine Learning with Heuristic Feedback Control Layer 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), 2024. https://rosap.ntl.bts.gov/view/dot/86348.
Li, Xianning, et al. Physics-informed Machine Learning with Heuristic Feedback Control Layer 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), 2024, ROSA P. https://rosap.ntl.bts.gov/view/dot/86348.
As privately owned and shared autonomous vehicles (AVs and SAVs) and automated trucks (ATrucks) become available, TxDOT and partner agencies must anticipate their travel, trade, emissions, cost, and other implications. Introducing these modes can significantly alter mode choices for passenger travel and freight and impact traffic volumes and conges
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Kockelman, K., Vellimana, M., Paithankar, P., & Mori, K. (2023). Implementation of Understanding the Impact of Autonomous Vehicles on Long-Distance Travel Mode and Destination Choice in Texas [Project Summary] (Report No. 5-7081-01). University of Texas at Austin. Center for Transportation Research. https://rosap.ntl.bts.gov/view/dot/76658
Kockelman, Kara, Maithreyi Vellimana, Priyanka Paithankar, and Kentaro Mori. Implementation of Understanding the Impact of Autonomous Vehicles on Long-Distance Travel Mode and Destination Choice in Texas [Project Summary]. Report no. 5-7081-01. University of Texas at Austin. Center for Transportation Research, 2023. https://rosap.ntl.bts.gov/view/dot/76658.
Kockelman, Kara, et al. Implementation of Understanding the Impact of Autonomous Vehicles on Long-Distance Travel Mode and Destination Choice in Texas [Project Summary]. University of Texas at Austin. Center for Transportation Research, 2023, Report no. 5-7081-01, ROSA P. https://rosap.ntl.bts.gov/view/dot/76658.
Truck-Mounted Attenuators (TMAs) are energy-absorbing devices added to heavy shadow vehicles to provide a mobile barrier that protects work crews from errant vehicles entering active work zones. In mobile and short duration operations, drivers manually operate the TMA, keeping pace with the work zone as needed to function as a mobile barrier protec
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Talledo Vilela, J. P., Mollenhauer, M. A., White, E. E., & Vaughan, E. W. (2023). Automated Truck Mounted Attenuator: Phase 2 Performance Measurement and Testing (Report No. VTTI-06-005). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/73294
Talledo Vilela, Jean Paul, Michael A Mollenhauer, Elizabeth E White, and Elijah W Vaughan. Automated Truck Mounted Attenuator: Phase 2 Performance Measurement and Testing. Report no. VTTI-06-005. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/73294.
Talledo Vilela, Jean Paul, et al. Automated Truck Mounted Attenuator: Phase 2 Performance Measurement and Testing. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. VTTI-06-005, ROSA P. https://rosap.ntl.bts.gov/view/dot/73294.
Researchers focused on three research issues: 1) predicting and making recommendations for future evacuations under wildfire conditions using connected vehicle data, 2) driving behaviors in wildfire evacuations, and 3) autonomous vehicle perceptions in winter conditions. This research addresses the urgent need for enhanced emergency evacuation stra
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Lu, P., Huang, Y., Ali, A., Ahmad, S., Yang, X., Ren, Y., & Ahmed, H. U. (2023). Regional Emergency Evacuation Analysis in Traffic With Connected and Autonomous Vehicles [Research Brief] (Report No. MPC 23-509 (project 685)). Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/73133
Lu, Pan, Ying Huang, Asad Ali, Salman Ahmad, Xinyi Yang, Yihao Ren, and Hafiz Usman Ahmed. Regional Emergency Evacuation Analysis in Traffic With Connected and Autonomous Vehicles [Research Brief]. Report no. MPC 23-509 (project 685). Mountain-Plains Consortium, 2023. https://rosap.ntl.bts.gov/view/dot/73133.
Lu, Pan, et al. Regional Emergency Evacuation Analysis in Traffic With Connected and Autonomous Vehicles [Research Brief]. Mountain-Plains Consortium, 2023, Report no. MPC 23-509 (project 685), ROSA P. https://rosap.ntl.bts.gov/view/dot/73133.
This study compares the operational and policy-related data from 120 autonomous shuttle deployments worldwide. Additionally, an analysis was conducted to identify the strengths, weaknesses, opportunities, and threats associated with autonomous shuttle deployments. The study also comprehensively analyzes the perceptions of practitioners and industry
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Diba, D. S., Gore, N., & Pulugurtha, S. S. (2023). Autonomous Shuttle Implementation and Best Practices [Research Brief] (Report No. 2321). Mineta Transportation Institute. https://rosap.ntl.bts.gov/view/dot/73206
Diba, Dil Samina, Ninad Gore, and Srinivas S. Pulugurtha. Autonomous Shuttle Implementation and Best Practices [Research Brief]. Report no. 2321. Mineta Transportation Institute, 2023. https://rosap.ntl.bts.gov/view/dot/73206.
Diba, Dil Samina, et al. Autonomous Shuttle Implementation and Best Practices [Research Brief]. Mineta Transportation Institute, 2023, Report no. 2321, ROSA P. https://rosap.ntl.bts.gov/view/dot/73206.
To help mitigate road fatalities due to human error, transportation stakeholders are turning to advanced driver assistance systems and autonomous vehicle (AV) development. However, the stakeholders continue to seek assurance of the safety performance of this new technology. This is often done using simulation testing of AV sensors and other platfor
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Saka, Z. A., & Labi, S. (2023). Investigation of AV Operational Issues Using Simulation Equipment (Report No. Report No. 34). Center for Connected and Automated Transportation. Purdue University. http://dx.doi.org/10.5703/1288284317737
Saka, Zainab A and Samuel Labi. Investigation of AV Operational Issues Using Simulation Equipment. Report no. Report No. 34. Center for Connected and Automated Transportation. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317737.
Saka, Zainab A, and Samuel Labi Investigation of AV Operational Issues Using Simulation Equipment. Center for Connected and Automated Transportation. Purdue University, 2023, Report no. Report No. 34, ROSA P. http://dx.doi.org/10.5703/1288284317737.
Most road fatalities are caused by human error. To help mitigate this issue and enhance overall transportation safety, companies are turning to advanced driver assistance systems and autonomous vehicle development. Perception, a key module of these systems, mostly uses light detection and ranging (LiDAR) sensors and enables object detection and env
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In an ever-evolving world characterized by environmental challenges and technological advancements, the confluence of natural disasters, particularly increasing wildfire threats, and the role of connected and autonomous vehicles (CAVs) stands as a critical focal point for research and innovation. This study embarks on a comprehensive exploration of
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Lu, P., Huang, Y., Ali, A., Ahmad, S., Yang, X., Ren, Y., & Ahmed, H. U. (2023). Regional Emergency Evacuation Analysis in Traffic With Connected and Autonomous Vehicles (Report No. MPC-685;MPC 23-509). Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/73131
Lu, Pan, Ying Huang, Asad Ali, Salman Ahmad, Xinyi Yang, Yihao Ren, and Hafiz Usman Ahmed. Regional Emergency Evacuation Analysis in Traffic With Connected and Autonomous Vehicles. Report no. MPC-685;MPC 23-509. Mountain-Plains Consortium, 2023. https://rosap.ntl.bts.gov/view/dot/73131.
Lu, Pan, et al. Regional Emergency Evacuation Analysis in Traffic With Connected and Autonomous Vehicles. Mountain-Plains Consortium, 2023, Report no. MPC-685;MPC 23-509, ROSA P. https://rosap.ntl.bts.gov/view/dot/73131.
When, where, and how autonomous shuttles are deployed can have significant safety, economic, and policy impacts on their operation and performance. This research analyzes data related to 120 existing deployments of autonomous shuttles, looking at safety, operational, economic, and policy-related issues. Analysis shows that autonomous shuttles would
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Diba, D. S., Gore, N., & Pulugurtha, S. S. (2023). Autonomous Shuttle Implementation and Best Practices (Report No. 23-33;CA-MTI-2321). Mineta Transportation Institute. https://doi.org/10.31979/mti.2023.2321
Diba, Dil Samina, Ninad Gore, and Srinivas S. Pulugurtha. Autonomous Shuttle Implementation and Best Practices. Report no. 23-33;CA-MTI-2321. Mineta Transportation Institute, 2023. https://doi.org/10.31979/mti.2023.2321.
Diba, Dil Samina, et al. Autonomous Shuttle Implementation and Best Practices. Mineta Transportation Institute, 2023, Report no. 23-33;CA-MTI-2321, ROSA P. https://doi.org/10.31979/mti.2023.2321.
When, where, and how autonomous shuttles are deployed can have significant safety, economic, and policy impacts on their operation and performance. This research analyzes data related to 120 existing deployments of autonomous shuttles, looking at safety, operational, economic, and policy-related issues. Analysis shows that autonomous shuttles would
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Dataset
Diba, D. S., Gore, N., & Pulugurtha, S. S. (2023). Autonomous Shuttle Implementation and Best Practices [supporting dataset] (Report No. 23-33;CA-MTI-2321). Mineta Transportation Institute. https://doi.org/10.31979/mti.2023.2321
Diba, Dil Samina, Ninad Gore, and Srinivas S. Pulugurtha. Autonomous Shuttle Implementation and Best Practices [supporting dataset]. Report no. 23-33;CA-MTI-2321. Mineta Transportation Institute, 2023. https://doi.org/10.31979/mti.2023.2321.
Diba, Dil Samina, et al. Autonomous Shuttle Implementation and Best Practices [supporting dataset]. Mineta Transportation Institute, 2023, Report no. 23-33;CA-MTI-2321, ROSA P. https://doi.org/10.31979/mti.2023.2321.
United States. Department of Transportation. National Highway Traffic Safety Administration. Office of Behavioral Safety Research
2023-12-01
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PDF
This PowerPoint slide was created to explore a new method to get summary information across to a non-technical audience. The slide is intended to be used in presentations on child restraint use when giving an overview of the topic.
United States. Department of Transportation. National Highway Traffic Safety Administration. Office of Behavioral Safety Research (2023). Child Passenger Safety Perception and Practices in Ride-Sharing Vehicles [PowerPoint Presentation]. United States. Department of Transportation. National Highway Traffic Safety Administration. https://rosap.ntl.bts.gov/view/dot/72944
United States. Department of Transportation. National Highway Traffic Safety Administration. Office of Behavioral Safety Research. Child Passenger Safety Perception and Practices in Ride-Sharing Vehicles [PowerPoint Presentation]. United States. Department of Transportation. National Highway Traffic Safety Administration, 2023. https://rosap.ntl.bts.gov/view/dot/72944.
United States. Department of Transportation. National Highway Traffic Safety Administration. Office of Behavioral Safety Research Child Passenger Safety Perception and Practices in Ride-Sharing Vehicles [PowerPoint Presentation]. United States. Department of Transportation. National Highway Traffic Safety Administration, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/72944.
United States. Department of Transportation. Federal Highway Administration
2023-12-01
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Manual on Uniform Traffic Control Devices for Streets and Highways
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PDF
The purpose of the Manual on Uniform Traffic Control Devices (MUTCD) is to establish uniform national criteria for the use of traffic control devices that meet the needs and expectancy of road users on all streets, highways, pedestrian and bicycle facilities, and site roadways open to public travel. There are nine parts to this manual: (1) General,
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United States. Department of Transportation. Federal Highway Administration (2023). Manual on Uniform Traffic Control Devices for Streets and Highways [11th Edition]. United States. Department of Transportation. Federal Highway Administration. https://rosap.ntl.bts.gov/view/dot/73253
United States. Department of Transportation. Federal Highway Administration. Manual on Uniform Traffic Control Devices for Streets and Highways [11th Edition]. United States. Department of Transportation. Federal Highway Administration, 2023. https://rosap.ntl.bts.gov/view/dot/73253.
United States. Department of Transportation. Federal Highway Administration Manual on Uniform Traffic Control Devices for Streets and Highways [11th Edition]. United States. Department of Transportation. Federal Highway Administration, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/73253.
GPS/GNSS systems have been a very critical element to the operation of various modern vehicles as knowing the position of vehicles is very important for many reasons, such as the safety of the vehicles. As an example, the precise GNSS positioning requires receiving clear signals from many satellites over the sky for an automobile at any given locat
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Lee, T. H. (2023). RF and Antenna Threats, Risks, and Mitigations for GNSS Receivers (Report No. CARMEN UTC Project 4). Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC). https://doi.org/10.5281/zenodo.10380893
Lee, Teh-Hong. RF and Antenna Threats, Risks, and Mitigations for GNSS Receivers. Report no. CARMEN UTC Project 4. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC), 2023. https://doi.org/10.5281/zenodo.10380893.
Lee, Teh-Hong RF and Antenna Threats, Risks, and Mitigations for GNSS Receivers. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC), 2023, Report no. CARMEN UTC Project 4, ROSA P. https://doi.org/10.5281/zenodo.10380893.
This document serves as a comprehensive guide on how to use the SAM AV Module within the context of the 5-7081-01 implementation project. This module is a variation SAM-V4 model (Statewide Analysis Model) modified to include autonomous vehicles (AVs), shared autonomous vehicles (SAVs) and autonomous trucks (ATrucks). This project introduces autonom
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Kockelman, K., Vellimana, M., & Paithankar, P. (2023). User’s Guide for SAM AV Module: Guide for the Texas Statewide Analysis Model with Autonomous Vehicles, Shared-Autonomous Vehicles & Autonomous Trucks (Report No. 5-7081 P1). Texas. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/76659
Kockelman, Kara, Maithreyi Vellimana, and Priyanka Paithankar. User’s Guide for SAM AV Module: Guide for the Texas Statewide Analysis Model with Autonomous Vehicles, Shared-Autonomous Vehicles & Autonomous Trucks. Report no. 5-7081 P1. Texas. Department of Transportation, 2023. https://rosap.ntl.bts.gov/view/dot/76659.
Kockelman, Kara, et al. User’s Guide for SAM AV Module: Guide for the Texas Statewide Analysis Model with Autonomous Vehicles, Shared-Autonomous Vehicles & Autonomous Trucks. Texas. Department of Transportation, 2023, Report no. 5-7081 P1, ROSA P. https://rosap.ntl.bts.gov/view/dot/76659.
In GNSS-degraded environments, whether the degrada6on is naturally occurring (e.g., in deep urban canyons) or inten6onal (e.g., in the presence of a jammer or a spoofer), ambient radio frequency (RF) signals can be exploited as an alterna6ve posi6oning, naviga6on, and 6ming (PNT) source. These signals are commonly referred to as signals of opportun
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Kassas, Z. (2023). PNT With Signals of Opportunity and Real-World Jammed and Spoofed Environments. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/72823
Kassas, Zak. PNT With Signals of Opportunity and Real-World Jammed and Spoofed Environments. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/72823.
Kassas, Zak PNT With Signals of Opportunity and Real-World Jammed and Spoofed Environments. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC), 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/72823.
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