Infrastructure Safety Support System for Smart Cities with Autonomous Vehicles

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 human drivers with inputs to improve their decision making when obvious decisions may not possible. The research objectives of this project are to: (1) Develop an infrastructure embedded sensor network to provide real-time traffic and road condition information such as traffic volume (e.g. ADT, peak-hour traffic), traffic composition (vehicle classification), vehicle speed, dynamic weight via weigh-in-motion (WIM), traffic density, traffic flow rate, road roughness, and other data; (2) Develop algorithms that the infrastructure safety support system will use to process the sensor-based real-time traffic data and pavement conditions to support the decision-making processes of autonomous vehicles such as driving speed and safe vehicle following distances when sharing the road with human-driven vehicles; (3) Develop real-time warnings based on the data derived from the infrastructure support system; (4) Optimize the infrastructure support system such as the sensor and V2I facility layout; (5) Validate the developed infrastructure support system through simulations and field tests. In addition, the team will use the results from this development to enhance curricula that would engage and mentor students in the practice of developing safe smart cities. This project will involve three graduate students and several undergraduate students. The trainings through this project will prepare students for potential careers in smart city developments.

• Record URL:
  • https://www.ugpti.org/resources/reports/details.php?id=1056
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/61602
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Upper Great Plains Transportation Institute

  North Dakota State University
  1320 Albrecht Boulevard
  Fargo, ND  United States  581052

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Huang, Ying
  • 0000-0003-4119-9522
  • Lu, Pan
  • 0000-0002-1640-3598
  • Bridgelall, Raj
  • 0000-0003-3743-6652
  • Yang, Xinyi
  • 0000-0002-9020-7261
  • Ren, Yihao
  • 0000-0002-9203-3525
• Publication Date: 2021-12

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Photos; References; Tables;
• Pagination: 37p

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Decision making; Sensors; Traffic safety; Vehicle to infrastructure communications
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01833379
• Record Type: Publication
• Report/Paper Numbers: MPC 21-447
• Contract Numbers: MPC-547
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Jan 24 2022 10:47AM