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 paper presents a comprehensive comparative analysis of the primary perception technologies, LiDAR, Radar, Camera, and Sonar, that underpin modern intelligent transportation systems and autonomous vehicles. While numerous studies have examined individual sensor technologies, this paper's primary contribution lies in its holistic, cross-modal an
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This research examines how Connected and Autonomous Vehicle (CAV) deployments can be made compatible with Complete Streets objectives through strategic infrastructure design and systematic interaction management to optimize urban space utilization. Urban transportation systems face increasing pressure to accommodate both autonomous vehicle technolo
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This report summarizes progress made on two problems of central importance to achieving increased safety in autonomous urban driving: (1) the development of end-to-end frameworks for cooperative perception, tracking and planning, and (2) the development of real-time strategies for collision avoidance when collisions are predicted. With respect to t
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Connected and autonomous transportation systems (CATS) promise major advances in efficiency and safety but also expose cyber-physical infrastructures to evolving cyber-attacks that threaten security and safety. This survey provides a unified synthesis of cyber resilience in CATS, organized along three axes: (1) operational scale (micro, meso, and m
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This study develops a Dynamic Bayesian Network (DBN) framework to examine how public confidence in autonomous vehicle (AV) safety and willingness-to-ride respond to policy interventions in crash-imminent pedestrian–passenger prioritization scenarios. Using stated preferences survey data from San Francisco (SF) and San Antonio (SA), the model integr
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This study addresses the challenge of effectively interpreting and navigating complex dynamic driving environments, using occupancy grids as a primary mode of spatial input representation. In this work, the research team presents a novel approach that combines the strengths of reinforcement learning (RL) and transformer-based architectures, particu
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Autonomous Driving (AD) vehicles must interact and respond in real-time to multiple sensor signals indicating the behavior of other agents in the environment, such as other vehicles, and pedestrians near the ego vehicle (i.e., the vehicle itself). While autonomous vehicle (AV) developers tend to generate numerous test cases in simulations to detect
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This project developed and evaluated an online adaptive platoon control framework for connected and automated vehicles (CAVs) that simultaneously enhances mobility and safety through integration with digital infrastructure based on the CARMA platform. The proposed Physics Enhanced Residual Learning (PERL) framework combines a physics-based centrali
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The current automated vehicles are not perfect, which means that human intervention, known as a takeover, is still necessary. For signaling takeover requests, informative (contralateral) displays were investigated and proven effective compared to instructional (ipsilateral) displays. However, how drivers interpret the information can vary based on
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The integration of autonomous vehicle (AV) shuttles and advanced driver assistance systems (ADAS) into public transit is positioned as a means of enhancing safety, accessibility, and operational efficiency. However, these technologies also transform the role of human operators, who must intervene during complex and high-risk scenarios. This study e
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This is a continuation of a successful Safety21 project on developing a training community for engineering and ethical skills for developing future autonomous vehicles. This project includes three components - (1) autonomous driving course development with a 1/10th-scale autonomous racecar where students learn advanced algorithms and software devel
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This proposed larger-scale effort aims to re-define and demonstrate the vision of full autonomy to one of safe autonomy, where a learning-enabled system is coupled with the foundations of cyber-physical systems to endow the system with an explicit awareness of both its capabilities and limitations. In turn, the system realizes when it is in or near
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Motivated by the shortcomings of using public road development of autonomous driving functions, this project focuses on the Vehicle-in-Virtual-Environment (VVE) method of safe, efficient, and low-cost connected and autonomous driving function development, evaluation, and demonstration. The VVE method places the actual vehicle inside a highly realis
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The Vehicle to Infrastructure (V2I) Benefit Cost Analysis (BCA) Tool (V2I BCA Tool) prototype was developed to help transportation agencies explore the potential benefits of V2I technology in the context of uncertainty around future fleet uptake in on-board units (OBUs), future deployment of automated vehicles (AVs), and the uncertainty around the
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The portion of the research project included in this report focuses on 28 Federal Motor Vehicle Safety Standards (FMVSS). It provides research findings, including the performance requirements and test procedures, in terms of options regarding technical translations, based on potential regulatory barriers identified for compliance verification of in
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In recent years, automated vehicles (AVs) are increasingly penetrating road networks with the main purpose of reducing driver error. Since around 94% of traffic crashes are due to driver errors, automated vehicles have the potential to enhance road safety by eliminating human drivers' tasks. Despite claims that these vehicles will increase road saf
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The U.S. Department of Transportation Volpe Center (Volpe) developed the Vehicle-to-Infrastructure (V2I) Benefit-Cost Analysis (BCA) Tool Prototype with funding and technical direction from the Federal Highway Administration’s (FHWA’s) Office of Safety and Operations Research and Development within the Turner-Fairbank Highway Research Center. The V
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The Autonomous Truck Mounted Attenuator (ATMA) system represents a specialized application of connected and autonomous vehicle (CAV) technologies, designed to enhance worker safety during roadway maintenance operations. Despite its growing adoption across state agencies, formalized deployment criteria remain absent from national guidelines such as
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Simulation agents are essential for designing and testing systems that interact with humans, such as autonomous vehicles (AVs). These agents serve various purposes, from benchmarking AV performance to stress-testing system limits, but all applications share one key requirement: reliability. To enable sound experimentation, a simulation agent must b
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We present an analysis of the impacts of last-mile delivery vehicles on pavement lifespans in residential streets. We argue that an increase in the number of parcels delivered daily could be a possible cause for increases in pavement maintenance expenditures in small cities and residential areas. This externality is often overlooked in the current
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