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.
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.
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.
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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Dataset
Darbha, S., & Rajagopal, K. (2023). A Diagnostic Machine Learning Model for Air Brake Systems in Commercial Vehicles [supporting dataset] (Report No. 04-100). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://doi.org/10.15787/VTT1/9SWAHY
Darbha, Swaroop and Kumbakonam Rajagopal. A Diagnostic Machine Learning Model for Air Brake Systems in Commercial Vehicles [supporting dataset]. Report no. 04-100. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://doi.org/10.15787/VTT1/9SWAHY.
Darbha, Swaroop, and Kumbakonam Rajagopal A Diagnostic Machine Learning Model for Air Brake Systems in Commercial Vehicles [supporting dataset]. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. 04-100, ROSA P. https://doi.org/10.15787/VTT1/9SWAHY.
Vehicle manufacturers are introducing increasingly sophisticated vehicle automation systems to improve driving efficiency, comfort, and safety. Despite these improvements, partially and fully automated vehicles introduce new safety risks to the driving environment. Driver inattention can contribute to increased risk, especially when control transfe
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Dataset
Paras, C. R., & Ferris, T. K. (2023). Countermeasures To Detect and Combat Inattention While Driving Partially Automated Systems [supporting dataset] (Report No. 01-002). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://doi.org/10.15787/VTT1/ZXVADS
Paras, Carolina Rodriguez and Thomas K. Ferris. Countermeasures To Detect and Combat Inattention While Driving Partially Automated Systems [supporting dataset]. Report no. 01-002. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023. https://doi.org/10.15787/VTT1/ZXVADS.
Paras, Carolina Rodriguez, and Thomas K. Ferris Countermeasures To Detect and Combat Inattention While Driving Partially Automated Systems [supporting dataset]. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2023, Report no. 01-002, ROSA P. https://doi.org/10.15787/VTT1/ZXVADS.
The emerging transportation technologies – vehicle connectivity, electrification, and automation – are expected to impact highway expenditures, revenue, and user equity. With regard to the expenditures, the provision of new infrastructure and modification of existing infrastructure will incur significant capital spending. With regard to revenue, th
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Mwamba, I. C., Alabi, B. N., Benny, D., Nafakh, A. J., Labi, S., & Sinha, K. C. (2023). Changes in Highway Expenditures and Revenues in an Era of CAVs – Part A: Road Agencies (Report No. CCAT Report 33A). Center for Connected and Automated Transportation. Purdue University. http://dx.doi.org/10.5703/1288284317702
Mwamba, Isaiah C, Bortiorkor N.T Alabi, Deepak Benny, Abdullah Jalal Nafakh, Samuel Labi, and Kumares C. Sinha. Changes in Highway Expenditures and Revenues in an Era of CAVs – Part A: Road Agencies. Report no. CCAT Report 33A. Center for Connected and Automated Transportation. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317702.
Mwamba, Isaiah C, et al. Changes in Highway Expenditures and Revenues in an Era of CAVs – Part A: Road Agencies. Center for Connected and Automated Transportation. Purdue University, 2023, Report no. CCAT Report 33A, ROSA P. http://dx.doi.org/10.5703/1288284317702.
A sustainable transportation future is one in which people eschew personal car ownership in favor of using automated vehicle (AV) based ridehailing services in a shared mode. However, the traveling public has historically shown a disinclination towards sharing rides and carpooling with strangers. In a future of AV-based ridehailing services, it wil
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Batur, I., Pendyala, R. M., & Magassy, T. B. (2023). A Multidimensional Analysis of Willingness to Share Rides in a Future of Autonomous Vehicles. Center for Teaching Old Models New Tricks (TOMNET). https://rosap.ntl.bts.gov/view/dot/74123
Batur, Irfan, Ram M. Pendyala, and Tassio B Magassy. A Multidimensional Analysis of Willingness to Share Rides in a Future of Autonomous Vehicles. Center for Teaching Old Models New Tricks (TOMNET), 2023. https://rosap.ntl.bts.gov/view/dot/74123.
Batur, Irfan, et al. A Multidimensional Analysis of Willingness to Share Rides in a Future of Autonomous Vehicles. Center for Teaching Old Models New Tricks (TOMNET), 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/74123.
This research aims to develop data-driven models for suggesting the initiation of an automated car-to-bicycle overtaking process that will be assessed subjectively by human drivers and bicyclists in a driving simulator environment. A naturalistic driving dataset with 102 vehicles involved served as the data source for model development. The models
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Lin, B. T. W., Bao, S., Guo, H., Chen, S. T., Chuang, T. H., & Su, H. J. (2023). A Data-Driven Autonomous Driving System for Overtaking Bicyclists (Report No. UMTRI-2023-21). University of Michigan. Center for Connected and Automated Transportation. https://dx.doi.org/10.7302/22224
Lin, Brian T W, Shan Bao, Huizhong Guo, Szu-Tung Chen, Tzu-Hsuan Chuang, and Hao-Jie Su. A Data-Driven Autonomous Driving System for Overtaking Bicyclists. Report no. UMTRI-2023-21. University of Michigan. Center for Connected and Automated Transportation, 2023. https://dx.doi.org/10.7302/22224.
Lin, Brian T W, et al. A Data-Driven Autonomous Driving System for Overtaking Bicyclists. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. UMTRI-2023-21, ROSA P. https://dx.doi.org/10.7302/22224.
Partial- and conditional-automated driving systems (ADS) can not only assist drivers with their driving tasks but also significantly reduce the driving-related burden. Yet still, when the AVS is engaged, the human driver still plays a critical role such as monitoring the driving environment and performing certain driving tasks when called upon by t
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Li, Y., Sharma, A., John, A. P., Alabi, B. N., Chen, S., & Labi, S. (2023). Development of Situational Awareness Enhancing System for Manual Takeover of AV (Report No. 53). Center for Connected and Automated Transportation. Purdue University. http://dx.doi.org/10.5703/1288284317730
Li, Yujie, Aishwarya Sharma, Ajuna P John, Bortiorkor N.T Alabi, Sikai Chen, and Samuel Labi. Development of Situational Awareness Enhancing System for Manual Takeover of AV. Report no. 53. Center for Connected and Automated Transportation. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317730.
Li, Yujie, et al. Development of Situational Awareness Enhancing System for Manual Takeover of AV. Center for Connected and Automated Transportation. Purdue University, 2023, Report no. 53, ROSA P. http://dx.doi.org/10.5703/1288284317730.
The application of on-demand ridesharing services can improve the efficiency and usage of a metropolitan transportation system. Implementing air taxis service can reduce the number of required aerial vehicles, mitigate air pollution, increase the revenue of the transportation network companies, and boost local economics. In this report, we propose
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Yang, S., Zhou, J., Sun, D., & Delaurentis, D. (2023). Ride-Sharing With Advanced Air Mobility (Report No. 58). University of Michigan. Center for Connected and Automated Transportation. http://dx.doi.org/10.5703/1288284317662
Yang, Shuting, Jiazhen Zhou, Dengfeng Sun, and Daniel Delaurentis. Ride-Sharing With Advanced Air Mobility. Report no. 58. University of Michigan. Center for Connected and Automated Transportation, 2023. http://dx.doi.org/10.5703/1288284317662.
Yang, Shuting, et al. Ride-Sharing With Advanced Air Mobility. University of Michigan. Center for Connected and Automated Transportation, 2023, Report no. 58, ROSA P. http://dx.doi.org/10.5703/1288284317662.
The transition of the vehicle fleet to incorporate AV will be a long and complex process. AVs will gradually form a larger and larger share of the fleet mix, offering opportunities and challenges for improved efficiency and safety. At any given point during this transition a portion of the AV fleet will be consuming roadway capacity while unoccupie
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Mantri, S., Lownes, N., & Bergman, D. (2023). Prioritizing People - Mixed Equilibrium Assignment for AV Based on Occupancy (Phase II). University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education. https://rosap.ntl.bts.gov/view/dot/72368
Mantri, Sruthi, Nicholas Lownes, and David Bergman. Prioritizing People - Mixed Equilibrium Assignment for AV Based on Occupancy (Phase II). University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2023. https://rosap.ntl.bts.gov/view/dot/72368.
Mantri, Sruthi, et al. Prioritizing People - Mixed Equilibrium Assignment for AV Based on Occupancy (Phase II). University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education, 2023, ROSA P. https://rosap.ntl.bts.gov/view/dot/72368.
In the era of connectivity and automation, the prospective vehicular applications of these technologies go beyond transportation operations and traffic management. Connected and automated vehicles (CAVs) are capable of centralized control, information sharing through connectivity, and integrated surveillance. Also, CAVs are readily amenable to inno
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Pourgholamali, M., Sharma, A., Ye, Z., Arefkhani, H., Dietz, E., & Labi, S. (2023). Exploring the Prospective Role of Connected Vehicles in Monitoring and Response to Pandemics and Disasters (Report No. 54). Center for Connected and Automated Transportation. Purdue University. http://dx.doi.org/10.5703/1288284317736
Pourgholamali, Mohammadhosein, Aishwarya Sharma, Zhongnan Ye, Hessam Arefkhani, Eric Dietz, and Samuel Labi. Exploring the Prospective Role of Connected Vehicles in Monitoring and Response to Pandemics and Disasters. Report no. 54. Center for Connected and Automated Transportation. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317736.
Pourgholamali, Mohammadhosein, et al. Exploring the Prospective Role of Connected Vehicles in Monitoring and Response to Pandemics and Disasters. Center for Connected and Automated Transportation. Purdue University, 2023, Report no. 54, ROSA P. http://dx.doi.org/10.5703/1288284317736.
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