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.
Integrating Autonomous Cars and Trucks into TxDOT's Statewide Analysis Model Training Webinar Nov 29, 2023.
Kockelman, K., Vellimana, M., Paithankar, P., & Mori, K. (2023). Integrating Autonomous Cars & Trucks into TxDOT’s Statewide Analysis Model [Training Webinar: Nov 29, 2023]. University of Texas at Austin. Center for Transportation Research. https://rosap.ntl.bts.gov/view/dot/76666
Kockelman, Kara, Maithreyi Vellimana, Priyanka Paithankar, and Kentaro Mori. Integrating Autonomous Cars & Trucks into TxDOT’s Statewide Analysis Model [Training Webinar: Nov 29, 2023]. University of Texas at Austin. Center for Transportation Research, 2023. https://rosap.ntl.bts.gov/view/dot/76666.
Kockelman, Kara, et al. Integrating Autonomous Cars & Trucks into TxDOT’s Statewide Analysis Model [Training Webinar: Nov 29, 2023]. University of Texas at Austin. Center for Transportation Research, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/76666.
Highly automated transportation systems rely on a steady stream of signals and information from external sources for localization, route planning, perception, and general situational awareness. This includes reliance on positioning, navigation, and timing (PNT) information: Location is essential autonomous navigation and planning; and accurate timi
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Humphreys, T. E., Chen, Q. A., Ozguner, U., & Toth, C. (2023). Resilience and Validation of GNSS PNT Solutions. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/72661
Humphreys, Todd E., Qi Alfred Chen, Umit Ozguner, and Charles Toth. Resilience and Validation of GNSS PNT Solutions. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/72661.
Humphreys, Todd E., et al. Resilience and Validation of GNSS PNT Solutions. 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/72661.
The emerging field of Advanced Air Mobility (AAM) presents myriad opportunities for disrupting traditional modes of transport, including passenger travel to cargo logistics. However, its path to full-scale adoption is fraught with regulatory, market, and logistical challenges. This report presents a nuanced understanding of AAM's complexities and i
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Bridgelall, R., & Tolliver, D. (2023). Autonomous Aircraft: Challenges and Opportunities (Report No. MPC-666). Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/72546
Bridgelall, Raj and Denver Tolliver. Autonomous Aircraft: Challenges and Opportunities. Report no. MPC-666. Mountain-Plains Consortium, 2023. https://rosap.ntl.bts.gov/view/dot/72546.
Bridgelall, Raj, and Denver Tolliver Autonomous Aircraft: Challenges and Opportunities. Mountain-Plains Consortium, 2023, Report no. MPC-666, ROSA P. https://rosap.ntl.bts.gov/view/dot/72546.
Numerous demonstrations and deployments of automated shuttles and buses are occurring in downtown areas, university campuses, business and medical parks, and entertainment complexes throughout the United States. This research project focused on ensuring that individuals with disabilities have equal and safe access to automated shuttles and buses to
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Turnbull, K. F., Higgins, L., & Gick, B. N. (2023). Automated Shuttles and Buses for All Users (Report No. 05-093). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/73309
Turnbull, Katherine F., Laura Higgins, and Brittney N Gick. Automated Shuttles and Buses for All Users. Report no. 05-093. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/73309.
Turnbull, Katherine F., et al. Automated Shuttles and Buses for All Users. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. 05-093, ROSA P. https://rosap.ntl.bts.gov/view/dot/73309.
The number of automated features in surface vehicles is increasing as new vehicles are released each year. Some features allow drivers to temporarily take their attention off the road to engage in other tasks. However, sometimes it is important for drivers to immediately take control of the vehicle. To take control safely, drivers must understand w
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Greatbatch, R., Dunn, N. J., Kim, H., & Krasner, A. (2023). Guiding Driver Responses During Manual Takeovers from Automated Vehicles (Report No. VTTI-00-026). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/73246
Greatbatch, Richard, Naomi J. Dunn, Hyungil Kim, and Alexander Krasner. Guiding Driver Responses During Manual Takeovers from Automated Vehicles. Report no. VTTI-00-026. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/73246.
Greatbatch, Richard, et al. Guiding Driver Responses During Manual Takeovers from Automated Vehicles. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. VTTI-00-026, ROSA P. https://rosap.ntl.bts.gov/view/dot/73246.
Cost-effective ways to obtain and distribute traffic data, and thereby, to facilitate operations decisions. This includes a need to make Signal Phase and Timing (SPaT) and MAP data more useful to traffic data end-users. Existing data collection and delivery methods are time consuming and costly. Therefore, to facilitate traffic operations in the cu
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Gowda, M., Fehr, W., Balmos, A., Ajagu, R., Krogmeier, J. V., Abbas, M., & Labi, S. (2023). Economical Acquisition of Intersection Data to Facilitate CAV Operations (Report No. 71). Center for Connected and Automated Transportation. Purdue University. http://dx.doi.org/10.5703/1288284317731
Gowda, Manish, Walt Fehr, Andrew Balmos, Richard Ajagu, James V. Krogmeier, Montasir Abbas, and Samuel Labi. Economical Acquisition of Intersection Data to Facilitate CAV Operations. Report no. 71. Center for Connected and Automated Transportation. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317731.
Gowda, Manish, et al. Economical Acquisition of Intersection Data to Facilitate CAV Operations. Center for Connected and Automated Transportation. Purdue University, 2023, Report no. 71, ROSA P. http://dx.doi.org/10.5703/1288284317731.
Since 1995, several countries and states have implemented roadway traffic safety projects with the goal of achieving a highway system with no fatal or serious injury crashes. South Carolina’s Target Zero plan is multifaceted in that it identifies several preventative measures to reduce fatalities. A common thread of these programs is that they are
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Sarasua, W., Michalaka, D., Murray-Tuite, P., Brown, K., Ogle, J. H., Davis, W. J., & Kohneh, J. N. (2023). Potential Reduction of Fatal Crashes in South Carolina Due to Connected and Automated Vehicles. Center for Connected Multimodal Mobility, Clemson University. https://rosap.ntl.bts.gov/view/dot/73806
Sarasua, Wayne, Dimitra Michalaka, Pamela Murray-Tuite, Kweku Brown, Jennifer H Ogle, William J. Davis, and Jamal Nahofti Kohneh. Potential Reduction of Fatal Crashes in South Carolina Due to Connected and Automated Vehicles. Center for Connected Multimodal Mobility, Clemson University, 2023. https://rosap.ntl.bts.gov/view/dot/73806.
Sarasua, Wayne, et al. Potential Reduction of Fatal Crashes in South Carolina Due to Connected and Automated Vehicles. Center for Connected Multimodal Mobility, Clemson University, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/73806.
User trust is pivotal to autonomous vehicle (AV) operations which are driven by artificial intelligence (AI). A promising way to build user trust is to use explainable artificial intelligence (XAI) which requires the AI system to provide the user with the underlying explanations for its decisions. Motivated by the need to enhance user trust and the
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Dong, J., Chen, S., & Labi, S. (2023). Promoting CAV Deployment by Enhancing the Perception Phase of the Autonomous Driving Using Explainable AI (Report No. 74). University of Michigan. Center for Connected and Automated Transportation. http://dx.doi.org/10.5703/1288284317701
Dong, Jiqian, Sikai Chen, and Samuel Labi. Promoting CAV Deployment by Enhancing the Perception Phase of the Autonomous Driving Using Explainable AI. Report no. 74. University of Michigan. Center for Connected and Automated Transportation, 2023. http://dx.doi.org/10.5703/1288284317701.
Dong, Jiqian, et al. Promoting CAV Deployment by Enhancing the Perception Phase of the Autonomous Driving Using Explainable AI. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. 74, ROSA P. http://dx.doi.org/10.5703/1288284317701.
Safely introducing autonomy to trucks requires monitoring their brake systems continuously. Out-of-adjustment push rods and leakages in the air brake system are two major reasons for increased braking distances in trucks, resulting in safety violations. Air leakages can occur due to small cracks or loose/improperly fit couplings, which do not affec
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Darbha, S., & Rajagopal, K. (2023). A Diagnostic Machine Learning Model for Air Brake Systems in Commercial Vehicles (Report No. 04-100). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/73248
Darbha, Swaroop and Kumbakonam Rajagopal. A Diagnostic Machine Learning Model for Air Brake Systems in Commercial Vehicles. Report no. 04-100. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/73248.
Darbha, Swaroop, and Kumbakonam Rajagopal A Diagnostic Machine Learning Model for Air Brake Systems in Commercial Vehicles. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. 04-100, ROSA P. https://rosap.ntl.bts.gov/view/dot/73248.
The emerging field of Advanced Air Mobility (AAM) presents myriad opportunities for disrupting traditional modes of transport, including passenger travel to cargo logistics. However, its path to full-scale adoption is fraught with regulatory, market, and logistical challenges. This report presents a nuanced understanding of AAM's complexities and i
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Bridgelall, R., & Tolliver, D. (2023). Autonomous Aircraft: Challenges and Opportunities [Brief]. Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/72612
Bridgelall, Raj and Denver Tolliver. Autonomous Aircraft: Challenges and Opportunities [Brief]. Mountain-Plains Consortium, 2023. https://rosap.ntl.bts.gov/view/dot/72612.
Bridgelall, Raj, and Denver Tolliver Autonomous Aircraft: Challenges and Opportunities [Brief]. Mountain-Plains Consortium, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/72612.
Highly automated vehicles (HAV), whether ground, aerial, or maritime, rely on a steady stream of signals and information from external sources: signals that localize the vehicle and provide information about the road network, other nearby vehicles, larger-scale traffic, and state of the infrastructure and the environment. Currently, many deployed s
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Redmill, K. A., Ahmed, Q., Chen, Q. A., Humphries, T., & Ozguner, U. (2023). Analysis and Simulation of PNT Threats and Risks to HATS. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/72822
Redmill, Keith A, Qadeer Ahmed, Qi Alfred Chen, Todd Humphries, and Umit Ozguner. Analysis and Simulation of PNT Threats and Risks to HATS. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/72822.
Redmill, Keith A, et al. Analysis and Simulation of PNT Threats and Risks to HATS. 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/72822.
The integrity of a PNT system can be monitored at three primary levels. The first one is the GPS/GNSS system level, which includes the signal structure/authentication and signal power/diversity which is beyond the scope from a user perspective. The second, the GPS/GNSS receiver level is of high interest and techniques include antenna design, receiv
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Toth, C., & Humphreys, T. E. (2023). Experimentation, Demonstration and Testing of PNT Related Threats, Risks, and Mitigations to HATS. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/72824
Toth, Charles and Todd E. Humphreys. Experimentation, Demonstration and Testing of PNT Related Threats, Risks, and Mitigations to HATS. Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC), 2023. https://rosap.ntl.bts.gov/view/dot/72824.
Toth, Charles, and Todd E. Humphreys Experimentation, Demonstration and Testing of PNT Related Threats, Risks, and Mitigations to HATS. 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/72824.
Work zones pose significant safety challenges on highways, with Commercial Motor Vehicles (CMVs) being particularly susceptible to higher risk due to their larger size, slower acceleration/deceleration rates, and expanded blind spots. This study aimed to assess the impact of various work zone warning measures on the driving behavior of CMV operator
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Jeihani, M., Khadem, N., Taherpour, A., Kabir, M., & Ardeshiri, A. (2023). Comprehensive Study on CMV Safety Using ITS in Work Zones on Freeways and Arterials. Morgan State University. Urban Mobility & Equity Center. https://rosap.ntl.bts.gov/view/dot/72423
Jeihani, Mansoureh, Nashid Khadem, Abolfazl Taherpour, Muhib Kabir, and Anam Ardeshiri. Comprehensive Study on CMV Safety Using ITS in Work Zones on Freeways and Arterials. Morgan State University. Urban Mobility & Equity Center, 2023. https://rosap.ntl.bts.gov/view/dot/72423.
Jeihani, Mansoureh, et al. Comprehensive Study on CMV Safety Using ITS in Work Zones on Freeways and Arterials. Morgan State University. Urban Mobility & Equity Center, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/72423.
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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Dataset
Talledo Vilela, J. P., Mollenhauer, M., White, E., & Vaughan, E. W. (2023). Automated Truck Mounted Attenuator: Phase 2 Performance Measurement and Testing [supporting dataset] (Report No. VTTI-06-005). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://doi.org/10.15787/VTT1/A1FNMW
Talledo Vilela, Jean Paul, Mike Mollenhauer, Elizabeth White, and Elijah W Vaughan. Automated Truck Mounted Attenuator: Phase 2 Performance Measurement and Testing [supporting dataset]. Report no. VTTI-06-005. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://doi.org/10.15787/VTT1/A1FNMW.
Talledo Vilela, Jean Paul, et al. Automated Truck Mounted Attenuator: Phase 2 Performance Measurement and Testing [supporting dataset]. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. VTTI-06-005, ROSA P. https://doi.org/10.15787/VTT1/A1FNMW.
The economic impact of air pollution due to motor vehicles by 2030 will be 100 billion dollars, and the public health impacts from traffic pollution will grow to 17 billion dollars. The transportation industry also faces the challenge of monitoring and controlling greenhouse gases (GHGs). CSU studied criteria air pollutants (CAPs)under different tr
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Nedunuri, K. K., & Kandiah, R. (2023). CAV Systems Incorporating Air Pollution Information from Traffic Congestion (Report No. CCAT Final Report 49). University of Michigan. Center for Connected and Automated Transportation. http://dx.doi.org/10.5703/1288284317733
Nedunuri, Krishna Kumar and Ramanitharan Kandiah. CAV Systems Incorporating Air Pollution Information from Traffic Congestion. Report no. CCAT Final Report 49. University of Michigan. Center for Connected and Automated Transportation, 2023. http://dx.doi.org/10.5703/1288284317733.
Nedunuri, Krishna Kumar, and Ramanitharan Kandiah CAV Systems Incorporating Air Pollution Information from Traffic Congestion. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. CCAT Final Report 49, ROSA P. http://dx.doi.org/10.5703/1288284317733.
Today’s sensor technologies are not at the level of emulating human eyes with 100% accuracy. Recent accidents with autonomous vehicles where sensors failed to correctly identify the threat suggests that sensor possible inaccuracies or inability to identify upcoming obstacles due to environmental conditions, road geometry, unpredictable vehicle mane
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Ioannou, P., & Waqas, M. (2023). Systematic and Provably Safe Design Methodology for Connected and Automated Vehicles (Report No. PSR-22-12). METRANS Transportation Center (Calif.). https://doi.org/10.25554/4n72-ze78
Ioannou, Petros and Muhammad Waqas. Systematic and Provably Safe Design Methodology for Connected and Automated Vehicles. Report no. PSR-22-12. METRANS Transportation Center (Calif.), 2023. https://doi.org/10.25554/4n72-ze78.
Ioannou, Petros, and Muhammad Waqas Systematic and Provably Safe Design Methodology for Connected and Automated Vehicles. METRANS Transportation Center (Calif.), 2023, Report no. PSR-22-12, ROSA P. https://doi.org/10.25554/4n72-ze78.
The North Carolina Department of Transportation (NCDOT) partnered with Cary, NC (Cary) and Beep, Inc. (Beep) to bring a novel-design, all-electric, low-speed automated shuttle to Fred G. Bond Metro Park (Bond Park) in Cary for a 13-week pilot through the Connected Autonomous Shuttle Supporting Innovation (CASSI) program. Beep operated a Navya Auton
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Searcy, S., & Curran, S. (2023). Connected Autonomous Shuttle Supporting Innovation (CASSI) in Cary’s Bond Park. North Carolina. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/73005
Searcy, Sarah and Shelley Curran. Connected Autonomous Shuttle Supporting Innovation (CASSI) in Cary’s Bond Park. North Carolina. Department of Transportation, 2023. https://rosap.ntl.bts.gov/view/dot/73005.
Searcy, Sarah, and Shelley Curran Connected Autonomous Shuttle Supporting Innovation (CASSI) in Cary’s Bond Park. North Carolina. Department of Transportation, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/73005.
For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve stati
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Feng, S., Yan, X., & Liu, H. (2023). Modeling Naturalistic Driving Environment with High-Resolution Trajectory Data (Report No. 69A3551747105). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.7302/22417
Feng, Shuo, Xintao Yan, and Henry Liu. Modeling Naturalistic Driving Environment with High-Resolution Trajectory Data. Report no. 69A3551747105. University of Michigan. Center for Connected and Automated Transportation, 2023. https://doi.org/10.7302/22417.
Feng, Shuo, et al. Modeling Naturalistic Driving Environment with High-Resolution Trajectory Data. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. 69A3551747105, ROSA P. https://doi.org/10.7302/22417.
The development of connected and automated vehicles (CAVs) holds promise for reducing traffic crashes and maintaining mobility among older adults. Challenges remain, however, in ensuring that CAVs are accessible, acceptable, affordable, and otherwise inclusive for older adults. The objective of this project was to increase graduate students’ awaren
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Dataset
Molnar, L. J., Zhou, F., Eby, D., Flannagan, C., Zakrajsek, J., St. Louis, R. M., Zanier, N., Yi, P., Marovic, C., & Whittenberger, R. (2023). Promoting Inclusive Design and Deployment of Connected and Automated Vehicles for Older Adults Through Education and Training of Engineering Students and Older Drivers [supporting dataset] (Report No. UA-CETran 2023-04). University of Michigan. Center for Connected and Automated Transportation. https://doi.org/10.4231/28GB-5023
Molnar, Lisa J, Feng Zhou, David Eby, Carol Flannagan, Jennifer Zakrajsek, Renée M. St. Louis, Nicole Zanier, Ping Yi, Claudia Marovic, and Reneé Whittenberger. Promoting Inclusive Design and Deployment of Connected and Automated Vehicles for Older Adults Through Education and Training of Engineering Students and Older Drivers [supporting dataset]. Report no. UA-CETran 2023-04. University of Michigan. Center for Connected and Automated Transportation, 2023. https://doi.org/10.4231/28GB-5023.
Molnar, Lisa J, et al. Promoting Inclusive Design and Deployment of Connected and Automated Vehicles for Older Adults Through Education and Training of Engineering Students and Older Drivers [supporting dataset]. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. UA-CETran 2023-04, ROSA P. https://doi.org/10.4231/28GB-5023.
This project aims to develop a robot (xBOT) that is customized for testing autonomous-connected vehicles. xBOT itself would be an automated-connected robot that behaves, and be perceived, as a free-moving Pedestrian, Scooter, Bicycle, Motorbike, or full-sized Vehicle. The project’s original intention was to build xBOT by modifying the Segway Ninebo
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Lakshmanan, S., & Xiang, W. (2023). xBOT – A Versatile Robot to Assist Testing of Autonomous-Connected Vehicles (Report No. Final Report 76). University of Michigan. Center for Connected and Automated Transportation. https://dx.doi.org/10.7302/22448
Lakshmanan, Sridhar and Weidong Xiang. xBOT – A Versatile Robot to Assist Testing of Autonomous-Connected Vehicles. Report no. Final Report 76. University of Michigan. Center for Connected and Automated Transportation, 2023. https://dx.doi.org/10.7302/22448.
Lakshmanan, Sridhar, and Weidong Xiang xBOT – A Versatile Robot to Assist Testing of Autonomous-Connected Vehicles. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. Final Report 76, ROSA P. https://dx.doi.org/10.7302/22448.
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