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Autonomous Vehicles

rosaP Repository & Open Science Access Portal

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.

Showing 421 - 440 of 847 results
  • Dataset Supporting Files
  • An Approach for the Selection and Description of Elements Used to Define Driving Scenarios
  • Opposing Traffic Safety Assist Draft Test Procedure Performability Validation
  • Software and Hardware Systems for Autonomous Smart Parking Accommodating Both Traditional and Autonomous Vehicles
  • Software and Hardware Systems for Autonomous Smart Parking Accommodating Both Traditional and Autonomous Vehicles Research Brief
  • Uses and Challenges of Collecting LiDAR Data From a Growing Autonomous Vehicle Fleet Implications for Infrastructure Planning and Inspection Practices
  • Editorial Advances in Planning for Emerging Transportation Technologies Towards Automation Connectivity and Electric Propulsion
  • Assessment of Autonomous Vehicle Sharing for Evacuation and Disaster Relief
  • Developing Analysis Modeling and Simulation Tools for Connected Automated Vehicle Applications A Case Study on SR 99 in California
  • Florida ATMA Pilot Demonstration and Evaluation
  • Dataset
  • Dataset Supporting Files
  • Dataset Supporting Files
  • Determination of Improved Pavement Smoothness When Using 3D Modeling and Automatic Machine Guidance techbrief
  • Older Adults New Mobility and Automated Vehicles
  • Intelligent Driving Intelligence Test for Autonomous Vehicles with Naturalistic and Adversarial Environment
  • Safety Impact Evaluation of a NarrowAutomated VehicleExclusive Reversible Lane on an Existing Smart Freeway
  • Data Mining Twitter To Improve Automated Vehicle Safety
  • Examining Seniors Adaptation to Mixed Function Automated Vehicles Analysis of Naturalistic Driving Data
  • Drivers Mental Models of Advanced Vehicle Technologies A Proposed Framework for Identifying and Predicting Operator Errors