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.
Future deployments of autonomous vehicles raise questions on how the actions of such vehicles may affect transportation systems as a whole, including the human-driven vehicles with which they share the road. The project team proposes to build a model of how autonomous vehicles can affect such mixed-autonomy systems and in particular their resilienc
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The COVID-19 pandemic has caused unprecedented growth in the use of online grocery services, influencing mobility choices as well as a range of decisions (e.g., where, how, and how much to shop). Before the pandemic, only 20% of customers in the US had ever bought their groceries online. But in June 2020, three months into the pandemic, nearly 80%
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Promising advances in autonomous vehicle (AV) technology have fueled industry and research fields to dedicate significant efforts to the study of the integration of AVs into the traffic network. While most studies anticipate a beneficial role of AVs, contributing to improved traffic efficiency and roadway safety, the underlying assumptions on the i
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Traffic related crashes cause more than 38,000 fatalities every year in the United States. They are the leading cause of death among drivers up to 54 years in age and incur $871 million in losses each year. Driver errors contribute to about 94% of these crashes. In response, automotive companies have been developing vehicles with advanced driver as
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Traffic related crashes cause more than 38,000 fatalities every year in the United States. They are the leading cause of death among drivers up to 54 years in age and incur $871 million in losses each year. Driver errors contribute to about 94% of these crashes. In response, automotive companies have been developing vehicles with advanced driver as
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This paper focuses on assessing the transportation system and sub-population level impacts of different pricing and fleet sizing policies for shared AV services in Seattle. While the conclusions of this research are meant to be generalizable, we focus our study on Seattle, Washington because it’s a diverse city with known inequalities among income,
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Driverless vehicles must operate with a safety integrity level, but urban environments degrade GNSS navigation accuracy and thereby fault-free integrity. Integration with INS helps maintain continuity, but position errors drift over time without GNSS signals. Whether modern navigation systems can provide satisfactory integrity for driverless vehicl
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Research on firm location choice has traditionally received less attention compared to residential location choices. This study focuses on modeling the location choice of smaller economic units (establishments) within the framework of the North American Industrial Classification System (NAICS) sectors. It seeks to uncover critical insights into the
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Automation is the future of transportation. Research on autonomous driving technology and vehicles is taking place throughout America, and the technology is primed to transform existing and future transportation systems. As the technology for autonomous vehicles continues to develop and eventually becomes ready for real-world testing, cooperative a
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New York City is piloting connected vehicle (CV) technology to support the Vision Zero initiative and help eliminate injuries and fatalities caused by crashes. As a part of the USDOT CV Pilot Deployment Program, a Mobile Accessible Pedestrian Signal System (PED-SIG) was developed. The PED-SIG application provides audio alerts and haptic prompts to
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This white paper explores the applicability of cooperative driving for advanced connected vehicles (CD for ACV) on urban roadways based on insights, data analysis, and stakeholder feedback documented as a part of the USDOT Connected Vehicle Pilot Deployment (CVPD). Three testable use cases are identified and mapped for New York City (NYC) applicati
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Emerging automated vehicles (AV) may be able to provide advanced information about the surrounding information with video cameras, radar sensors, lidar sensors, etc. Such information will enable estimating and predicting transportation system states on mobility, energy, and emissions. In this study, a physical informed neural network is developed t
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A solar-powered automated transportation network (ATN) connecting the North and South campuses of San José State University with three passenger stations was designed, visualized, and analyzed in terms of its energy usage, carbon offset, and cost. The study’s methodology included the use of tools and software such as ArcGIS, SketchUp, Infraworks, S
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SAE Level 5 autonomy requires the autonomous vehicle to be able to accurately sense the environment and detect obstacles in all weather and visibility conditions. This sensing problem becomes significantly challenging in weather conditions that include such events as sudden change in lighting, smoke, fog, snow, and rain. There is no standalone sens
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Connected and Autonomous Vehicle (CAV) technologies enable communication among vehicles, and vehicles and infrastructure, paving the way for multiple safety and operational applications. This research developed and tested traffic signal control algorithms and control programs which utilized CAV-equipped heavy trucks and traffic signals. The focus o
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Leveraging recent advancements in distributed optimization and reinforcement learning, and the growing connectivity and computational capability of vehicles and infrastructure, we propose to advance real-time adaptive signal control via distributed control and optimization. This report consists of three parts. Part 1 develops distributed algorithms
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Driverless vehicles must be self-aware to make learned and ethical decisions to avoid crashes in multimodal and diverse settings. This proposed effort will develop an Infrastructure Safety Support System by embedding vehicle-to-infrastructure (V2I) enabled sensor networks into the transportation infrastructure to provide autonomous vehicles and hum
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This project develops the main modules and algorithm models for the digital twin platform for a smart mobility testing ground currently under construction. LiDAR (Line Detection And Ranging)-sensor-based object detection and 3D infrastructure modeling modules are developed and tested in the project. The developed digital twin model is pilot tested
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United States. Committee on the Marine Transportation System (CMTS)
2021-12-01
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The U.S. Committee on the Marine Transportation System (CMTS) in partnership with the Transportation Research Board (TRB) held the Sixth Biennial Marine Innovative Science and Technology Conference, “Advancing the Maritime Transportation System through Automation and Autonomous Technology: Trends, Applications, and Challenges,” virtually on March 1
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Recently, 2D detection in images has made significant progress owing to the emergence of a convolutional neural network (CNN), which can extract high-level features from the images. However, detecting objects in 3D instead of 2D space is an essential topic when building perception systems for autonomous driving. An autonomous vehicle (AV) needs to
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