Modeling Autonomous Driving Systems-Equipped Vehicle Interactions With Traffic Signs [Brochure]

Details

• Creators:
  Wang, Xuan ; Li, Qianwen ; Yao, Handong ; Hourdos, John ; McHale, Gene
• Corporate Creators:
  United States. Department of Transportation. Federal Highway Administration
• Corporate Contributors:
  United States. Department of Transportation. Federal Highway Administration. Turner-Fairbank Highway Research Center ; University of Georgia
• Subject/TRT Terms:
  • Autonomous vehicles
  • Traffic signs
  • Signalized intersections
  • Vehicle to infrastructure communications
• Publication/ Report Number:
  FHWA-HRT-26-035
• DOI:
  https://doi.org/10.21949/qhyv-yk59
• Resource Type:
  Booklet/Pamphlet
• Right Statement:
  Public Domain
• Geographical Coverage:
  United States
• Corporate Publisher:
  United States. Department of Transportation. Federal Highway Administration
• Abstract:
  Prior studies report conflicting impacts of autonomous vehicles (AVs). A study by Li et al. suggests AVs may engage in riskier behaviors compared with human-driven vehicles. A study by Morando et al. highlights AV potential to enhance safety. A better understanding of AV behaviors is crucial for safety. This goal calls for an investigation using real-world data. Existing freeway-based models may misrepresent AV behaviors at sign-controlled intersections, resulting in unclear impact assessments. To date, no known studies have systematically modeled AV responses to traffic signs, leaving a critical research gap.
• Format:
  PDF
• Funding:
  693JJ324C000003
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/89235/dot_89235_DS1.pdf
• File Type:
  [PDF - 766.30 KB]
• Collection(s):
  Federal Highway Administration
• Main Document Checksum:
  urn:sha-512:ed0e05d2e35ca99dcd1457aef1e9f9903cfd521bcdec3a92bbef23143383c83f68f4f1885c5a23bea0bbbd2c93df4b3e0a95a1501c0599b96703e356388f1adf