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.
This report summarizes the potential impacts of driving automation on segments of the professional driving workforce in the United States, based largely on an analysis of published research findings and existing data sources. This section describes the scope, context, and methodology; following sections discuss potential workforce impacts, associat
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United States. Department of Transportation (2021). Driving Automation Systems in Long-Haul Trucking and Bus Transit: Preliminary Analysis of Potential Workforce Impacts - Report to Congress, January 2021. United States. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/54595
United States. Department of Transportation. Driving Automation Systems in Long-Haul Trucking and Bus Transit: Preliminary Analysis of Potential Workforce Impacts - Report to Congress, January 2021. United States. Department of Transportation, 2021. https://rosap.ntl.bts.gov/view/dot/54595.
United States. Department of Transportation Driving Automation Systems in Long-Haul Trucking and Bus Transit: Preliminary Analysis of Potential Workforce Impacts - Report to Congress, January 2021. United States. Department of Transportation, 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/54595.
This report summarizes the data collected while operating three vehicles equipped with SAE automation level 2 driver assistance systems. Using cameras, driver-annotated video was recorded to document the systems’ availability and noteworthy operation. Notable events were classified into three categories: events where the vehicle terminated its auto
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Rao, S. J., & Forkenbrock, G. J. (2021). Classification of Level 2 Driving Events Observed on Public Roads (Report No. DOT HS 812 980). United States. Department of Transportation. National Highway Traffic Safety Administration. https://doi.org/10.21949/1530206
Rao, Sughosh J. and Garrick J. Forkenbrock. Classification of Level 2 Driving Events Observed on Public Roads. Report no. DOT HS 812 980. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021. https://doi.org/10.21949/1530206.
Rao, Sughosh J., and Garrick J. Forkenbrock Classification of Level 2 Driving Events Observed on Public Roads. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021, Report no. DOT HS 812 980, ROSA P. https://doi.org/10.21949/1530206.
Transportation electrification is playing an increasingly essential role in mitigating climate change, especially coupled with a sustainable energy system. However, proper placement of charging infrastructures and management of charging activities is the key to ensuring the environmental benefits from the widespread adoption of electric vehicles. E
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Li, X. (2021). Spatial-temporal Modeling of Electric Vehicles Charging Infrastructure and Management for a Sustainable Energy System. University of California, Davis. https://rosap.ntl.bts.gov/view/dot/66447
Li, Xinwei. Spatial-temporal Modeling of Electric Vehicles Charging Infrastructure and Management for a Sustainable Energy System. University of California, Davis, 2021. https://rosap.ntl.bts.gov/view/dot/66447.
Li, Xinwei Spatial-temporal Modeling of Electric Vehicles Charging Infrastructure and Management for a Sustainable Energy System. University of California, Davis, 2021, ROSA P. https://rosap.ntl.bts.gov/view/dot/66447.
This project investigated how driver expectations about Level 2 ADAS systems affect driver engagement and performance. Many vehicles available to consumers offer some level of automated driving functionality, but the capabilities of these vehicles vary widely among makes and models. Drivers may have preconceived expectations about how these driver
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Russell, S. M., Atwood, J., & McLaughlin, S. B. (2021). Driver Expectations for System Control Errors, Driver Engagement, and Crash Avoidance in Level 2 Driving Automation Systems (Report No. DOT HS 812 982). United States. Department of Transportation. National Highway Traffic Safety Administration. https://doi.org/10.21949/1530205
Russell, Sheldon M., Jon Atwood, and Shane B. McLaughlin. Driver Expectations for System Control Errors, Driver Engagement, and Crash Avoidance in Level 2 Driving Automation Systems. Report no. DOT HS 812 982. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021. https://doi.org/10.21949/1530205.
Russell, Sheldon M., et al. Driver Expectations for System Control Errors, Driver Engagement, and Crash Avoidance in Level 2 Driving Automation Systems. United States. Department of Transportation. National Highway Traffic Safety Administration, 2021, Report no. DOT HS 812 982, ROSA P. https://doi.org/10.21949/1530205.
The project presents a pavement sensing system along with a list of promising computing models that can be used to predict pavement conditions using a vehicle-based sensing technology. The project started with data acquisition obtained from the previous field data collection followed by a series of data computing using machine learning methods to d
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Ho, C. H., Zhang, D., Gao, J., Gerosa, M., & Cambou, B. (2021). Development of Cost-Effective Sensing Systems and Analytics (CeSSA) To Monitor Roadway Conditions and Mobility Safety (Report No. PSR-19-12). Pacific Northwest Transportation Consortium (PacTrans) (UTC). https://rosap.ntl.bts.gov/view/dot/56832
Ho, Chun-Hsing, Dada Zhang, Jaiwei Gao, Marco Gerosa, and Bertrand Cambou. Development of Cost-Effective Sensing Systems and Analytics (CeSSA) To Monitor Roadway Conditions and Mobility Safety. Report no. PSR-19-12. Pacific Northwest Transportation Consortium (PacTrans) (UTC), 2021. https://rosap.ntl.bts.gov/view/dot/56832.
Ho, Chun-Hsing, et al. Development of Cost-Effective Sensing Systems and Analytics (CeSSA) To Monitor Roadway Conditions and Mobility Safety. Pacific Northwest Transportation Consortium (PacTrans) (UTC), 2021, Report no. PSR-19-12, ROSA P. https://rosap.ntl.bts.gov/view/dot/56832.
The objective of this research project is to help the Washington State Department of Transportation (WSDOT) identify the right time and location for selecting a corridor for connected vehicle (CV) implementation so that the technological needs and compatibilities are met and expected outcomes are significant. This research team surveyed twenty-one
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Hajbabaie, A., Bin Al Islam, S. M. A., Tajalli, M., & Mohebifard, R. (2020). Preparing for Traffic Signal Operations in a Multi-Modal Connected and Autonomous Vehicle Environment (Report No. WA-RD 902.1). Washington State Department of Transportation. https://rosap.ntl.bts.gov/view/dot/82439
Hajbabaie, Ali, S M A Bin Al Islam, Mehrdad Tajalli, and Rasool Mohebifard. Preparing for Traffic Signal Operations in a Multi-Modal Connected and Autonomous Vehicle Environment. Report no. WA-RD 902.1. Washington State Department of Transportation, 2020. https://rosap.ntl.bts.gov/view/dot/82439.
Hajbabaie, Ali, et al. Preparing for Traffic Signal Operations in a Multi-Modal Connected and Autonomous Vehicle Environment. Washington State Department of Transportation, 2020, Report no. WA-RD 902.1, ROSA P. https://rosap.ntl.bts.gov/view/dot/82439.
The objective of this project was to inventory the state of the practice for integrating AVs into the modeling process and to develop metrics, models and prototype tools for quantitative evaluation of AV scenario planning impacts. The outcome of the project is a usable framework and prototype planning model to establish the viability and access to
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ICF International (Firm) (2020). Incorporating Automated Vehicles into Scenario Planning Models (Report No. FHWA-JPO-22-926). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/66970
ICF International (Firm). Incorporating Automated Vehicles into Scenario Planning Models. Report no. FHWA-JPO-22-926. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2020. https://rosap.ntl.bts.gov/view/dot/66970.
ICF International (Firm) Incorporating Automated Vehicles into Scenario Planning Models. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2020, Report no. FHWA-JPO-22-926, ROSA P. https://rosap.ntl.bts.gov/view/dot/66970.
The research aims at developing a resilient framework to be applied to transportation systems using connected and autonomous vehicles (CAVs). This innovation potentially responds to accident rates often related to inefficient communication systems, supported by a variety of state-of-the-art safety applications. A Vehicular Ad hoc Network (VANET) is
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DatasetSupporting Files
Zhu, Q., Kieras, T., & Farooq, M. J. (2020). Developing Secure Strategies for Vehicular Ad hoc Networks in Connected and Autonomous Vehicles [supporting datasets]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART). https://doi.org/10.5281/zenodo.4310277
Zhu, Quanyan, Timothy Kieras, and Muhammad Junaid Farooq. Developing Secure Strategies for Vehicular Ad hoc Networks in Connected and Autonomous Vehicles [supporting datasets]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2020. https://doi.org/10.5281/zenodo.4310277.
Zhu, Quanyan, et al. Developing Secure Strategies for Vehicular Ad hoc Networks in Connected and Autonomous Vehicles [supporting datasets]. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2020, ROSA P. https://doi.org/10.5281/zenodo.4310277.
Sharing location-based information and positioning vehicles with respect to that information has been one of the fundamental challenges in intelligent transportation for over two decades. As Connected and Automated Vehicles (C/AVs) and related data-intensive transportation network management solutions start to emerge, this research project assesses
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Shuman, V., English, T., Gopalakrishna, D., Serulle, N. U., Stephens, D., Wilson, K., Guan, A., & Gouse, W. (2020). Infrastructure and V2X Mapping Needs Assessment and Development Support: Final Project Report (Report No. FHWA-JPO-20-828). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/57545
Shuman, Valerie, Tony English, Deepak Gopalakrishna, Nayel Urena Serulle, Denny Stephens, Keith Wilson, Adrian Guan, and William Gouse. Infrastructure and V2X Mapping Needs Assessment and Development Support: Final Project Report. Report no. FHWA-JPO-20-828. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2020. https://rosap.ntl.bts.gov/view/dot/57545.
Shuman, Valerie, et al. Infrastructure and V2X Mapping Needs Assessment and Development Support: Final Project Report. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2020, Report no. FHWA-JPO-20-828, ROSA P. https://rosap.ntl.bts.gov/view/dot/57545.
This project developed a method to characterize the impact of privately-owned autonomous electric vehicles on electric vehicle charger placement, distribution, utilization, and power demand. Using Seattle, WA as a case study, a least total cost optimization for charging station owner and driver costs is conducted for vehicle automation levels 0-3,
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Samaras, C., & Mersky, A. C. (2020). Estimating Changes in Parking Capacity and Urban Form From Vehicle Automation. Mobility21, Carnegie Mellon University. https://rosap.ntl.bts.gov/view/dot/57991
Samaras, Constantine and Avi C. Mersky. Estimating Changes in Parking Capacity and Urban Form From Vehicle Automation. Mobility21, Carnegie Mellon University, 2020. https://rosap.ntl.bts.gov/view/dot/57991.
Samaras, Constantine, and Avi C. Mersky Estimating Changes in Parking Capacity and Urban Form From Vehicle Automation. Mobility21, Carnegie Mellon University, 2020, ROSA P. https://rosap.ntl.bts.gov/view/dot/57991.
This project studies methods to control both vehicle and traffic under limited penetration of low-level connected and autonomous vehicles (LCAVs). The investigation includes three major parts: (1) the Eco-Driving algorithm for a single CAV with low-level automation; (2) the vehicle in the loop (VIL) simulation platform; and (3) the integrated vehic
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Ban, X. (., Guo, Q., Angah, O., & Liu, Z. (2020). Vehicle-Traffic Control with Limited-Capacity Connected/Automated Vehicles. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART). https://rosap.ntl.bts.gov/view/dot/59159
Ban, Xuegang (Jeff), Qiangqiang Guo, Ohay Angah, and Zhijun Liu. Vehicle-Traffic Control with Limited-Capacity Connected/Automated Vehicles. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2020. https://rosap.ntl.bts.gov/view/dot/59159.
Ban, Xuegang (Jeff), et al. Vehicle-Traffic Control with Limited-Capacity Connected/Automated Vehicles. Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), 2020, ROSA P. https://rosap.ntl.bts.gov/view/dot/59159.
This project developed the key modules and models for building a prototype smart mobility intersection testbed. The project proposed the key system design, developed the computer-vision based sensing module, created 3D infrastructure model, and explored a risk-based intersection traffic conflict analysis application as an application of the develop
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Jin, P. J., Jafari, M., Ge, Y., Huang, Y., Wang, Y., & Zhang, T. (2020). The Development of a Smart Intersection Mobility Testbed (SIMT) (Report No. CAIT-UTC-REG23). Rutgers University. Center for Advanced Infrastructure and Transportation. https://rosap.ntl.bts.gov/view/dot/64085
Jin, Peter J., Mohsen Jafari, Yi Ge, Yufei Huang, Yizhou Wang, and Tianya Zhang. The Development of a Smart Intersection Mobility Testbed (SIMT). Report no. CAIT-UTC-REG23. Rutgers University. Center for Advanced Infrastructure and Transportation, 2020. https://rosap.ntl.bts.gov/view/dot/64085.
Jin, Peter J., et al. The Development of a Smart Intersection Mobility Testbed (SIMT). Rutgers University. Center for Advanced Infrastructure and Transportation, 2020, Report no. CAIT-UTC-REG23, ROSA P. https://rosap.ntl.bts.gov/view/dot/64085.
United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office
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Connected vehicle (CV) technology is expected to significantly improve transportation systems due to the mobility, safety, and environmental benefits gained from connectivity between different vehicles and with infrastructure. Connectivity to real-time traffic signal data is a key component of CV applications and is enabled by wireless communicatio
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United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, & United States. Department of Transportation. Federal Highway Administration (2020). Feasibility Study and Assessment of Communications Approaches for Real-Time Traffic Signal Applications [Factsheet] (Report No. FHWA-20-827). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/53934
United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office and United States. Department of Transportation. Federal Highway Administration. Feasibility Study and Assessment of Communications Approaches for Real-Time Traffic Signal Applications [Factsheet]. Report no. FHWA-20-827. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2020. https://rosap.ntl.bts.gov/view/dot/53934.
United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, et al. Feasibility Study and Assessment of Communications Approaches for Real-Time Traffic Signal Applications [Factsheet]. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2020, Report no. FHWA-20-827, ROSA P. https://rosap.ntl.bts.gov/view/dot/53934.
This report addresses a multitude of contemporary issues in highway safety, evolving transportation alternatives, and activity and travel behavior modelling. The report begins by studying issues relating to big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis, showing that a combination machine le
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Maness, M., Mannering, F., Pinjari, A. R., Zhang, Y., Alnawmasi, N., Barbour, N., Islam, M., Bhat, C., Shankar, V., Abdel-Aty, M., Luong, T., & Mishra, D. (2020). An Exploration of Contemporary Issues in Highway Safety, Evolving Transportation Alternatives, and Activity and Travel Behavior Modeling. Center for Teaching Old Models New Tricks (TOMNET). https://rosap.ntl.bts.gov/view/dot/62802
Maness, Michael, Fred Mannering, Abdul R. Pinjari, Yu Zhang, Nawaf Alnawmasi, Natalia Barbour, and Mouyid Islam, et al.. An Exploration of Contemporary Issues in Highway Safety, Evolving Transportation Alternatives, and Activity and Travel Behavior Modeling. Center for Teaching Old Models New Tricks (TOMNET), 2020. https://rosap.ntl.bts.gov/view/dot/62802.
Maness, Michael, et al. An Exploration of Contemporary Issues in Highway Safety, Evolving Transportation Alternatives, and Activity and Travel Behavior Modeling. Center for Teaching Old Models New Tricks (TOMNET), 2020, ROSA P. https://rosap.ntl.bts.gov/view/dot/62802.
Mobile and slow-moving operations, such as striping, sweeping, bridge flushing and pothole patching, are critical for efficient and safe operation of the highway transportation system. A successfully implemented leader-follower autonomous truck mounted attenuators (ATMA) system will eliminate all injuries to DOT employees in follow truck (FT) provi
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Hu, X., & Tang, Q. (2020). Modeling and Development of Operation Guidelines for Leader-Follower Autonomous Truck-Mounted Attenuator Vehicles (Report No. 5-1121-0005-128-1). University of Nebraska-Lincoln. Mid-America Transportation Center. https://rosap.ntl.bts.gov/view/dot/73990
Hu, Xianbiao and Qing Tang. Modeling and Development of Operation Guidelines for Leader-Follower Autonomous Truck-Mounted Attenuator Vehicles. Report no. 5-1121-0005-128-1. University of Nebraska-Lincoln. Mid-America Transportation Center, 2020. https://rosap.ntl.bts.gov/view/dot/73990.
Hu, Xianbiao, and Qing Tang Modeling and Development of Operation Guidelines for Leader-Follower Autonomous Truck-Mounted Attenuator Vehicles. University of Nebraska-Lincoln. Mid-America Transportation Center, 2020, Report no. 5-1121-0005-128-1, ROSA P. https://rosap.ntl.bts.gov/view/dot/73990.
United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office
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Connected vehicle (CV) technology is expected to significantly improve transportation systems due to the mobility, safety, and environmental benefits gained from connectivity between different vehicles and with infrastructure. Enabled by wireless communications, connectivity to real-time traffic signal data is a key component of CV applications. La
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United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, & United States. Department of Transportation. Federal Highway Administration (2020). Feasibility Study and Assessment of Communications Approaches for Real-Time Traffic Signal Applications [Factsheet] (Report No. FHWA-JPO-20-826). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/53935
United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office and United States. Department of Transportation. Federal Highway Administration. Feasibility Study and Assessment of Communications Approaches for Real-Time Traffic Signal Applications [Factsheet]. Report no. FHWA-JPO-20-826. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2020. https://rosap.ntl.bts.gov/view/dot/53935.
United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, et al. Feasibility Study and Assessment of Communications Approaches for Real-Time Traffic Signal Applications [Factsheet]. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2020, Report no. FHWA-JPO-20-826, ROSA P. https://rosap.ntl.bts.gov/view/dot/53935.
Autonomous vehicles (AVs) are widely considered to be the future of surface transportation in the United States, but little is understood about how people will interact with these vehicles, what they will use them for, and how they will impact our roads. However, farmers have been interacting with some degree of AV technology, primarily auto-guidan
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Piatkowski, D. P., Pitla, S., Luck, J. D., & Hazelton, J. K. (2020). Preparing for a Driverless Future (Report No. MO50). Nebraska. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/56446
Piatkowski, Daniel P, Santosh Pitla, Joe D Luck, and Josephine K Hazelton. Preparing for a Driverless Future. Report no. MO50. Nebraska. Department of Transportation, 2020. https://rosap.ntl.bts.gov/view/dot/56446.
Piatkowski, Daniel P, et al. Preparing for a Driverless Future. Nebraska. Department of Transportation, 2020, Report no. MO50, ROSA P. https://rosap.ntl.bts.gov/view/dot/56446.
This report summarizes the state of the practice in the application of advanced technologies to winter road maintenance operations. Information was captured through a combination of literature search, surveys, and interviews with key stakeholders. Several promising technologies were identified as areas of interest. These areas include connected aut
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Minge, E., Gallagher, M. R., & Curd, C. (2020). Integrating Advanced Technologies into Winter Operations Decisions (Report No. CR 17-01). Minnesota. Department of Transportation. Clear Roads Pooled Fund. https://rosap.ntl.bts.gov/view/dot/57602
Minge, Erik, Mark R. Gallagher, and Chris Curd. Integrating Advanced Technologies into Winter Operations Decisions. Report no. CR 17-01. Minnesota. Department of Transportation. Clear Roads Pooled Fund, 2020. https://rosap.ntl.bts.gov/view/dot/57602.
Minge, Erik, et al. Integrating Advanced Technologies into Winter Operations Decisions. Minnesota. Department of Transportation. Clear Roads Pooled Fund, 2020, Report no. CR 17-01, ROSA P. https://rosap.ntl.bts.gov/view/dot/57602.
The evolution of scientific advances has often been characterized by the amalgamation of two or more technologies. With respect to vehicle connectivity and automation, recent literature suggests that these two emerging transportation technologies can and will jointly and profoundly shape the future of transportation. However, it is not certain how
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Ha, P., Chen, S., Du, R., Dong, J., Li, Y., Labi, S., Karoonsoontawong, A., Al-Kaisy, A. F., & Fountas, G. (2020). Vehicle Connectivity and Automation: A Sibling Relationship. Frontiers in Built Environment. https://doi.org/10.3389/fbuil.2020.590036
Ha, Paul, Sikai Chen, Runjia Du, Jiqian Dong, Yujie Li, Samuel Labi, Ampol Karoonsoontawong, Ahmed F. Al-Kaisy, and Grigorios Fountas. Vehicle Connectivity and Automation: A Sibling Relationship. Frontiers in Built Environment, 2020. https://doi.org/10.3389/fbuil.2020.590036.
Ha, Paul, et al. Vehicle Connectivity and Automation: A Sibling Relationship. Frontiers in Built Environment, 2020, ROSA P. https://doi.org/10.3389/fbuil.2020.590036.
Automated driving systems (ADS) have the potential to fundamentally change transportation, and a growing number of these systems have entered the market and are currently in use on public roadways. However, drivers may not use ADS as intended due to misunderstandings about system capabilities and limitations. Moreover, the real-world use and effect
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Kim, H., Song, M., & Doerzaph, Z. R. (2020). Real-World Use of Automated Driving Systems and their Safety Consequences: A Naturalistic Driving Data Analysis (Report No. VTTI-00-029). Safety through Disruption (Safe-D) University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/58336
Kim, Hyungil, Miao Song, and Zachary R Doerzaph. Real-World Use of Automated Driving Systems and their Safety Consequences: A Naturalistic Driving Data Analysis. Report no. VTTI-00-029. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2020. https://rosap.ntl.bts.gov/view/dot/58336.
Kim, Hyungil, et al. Real-World Use of Automated Driving Systems and their Safety Consequences: A Naturalistic Driving Data Analysis. Safety through Disruption (Safe-D) University Transportation Center (UTC), 2020, Report no. VTTI-00-029, ROSA P. https://rosap.ntl.bts.gov/view/dot/58336.
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