Real-time and Secure Analysis of Pedestrian Data for Connected Vehicles

Details

• Creators:
  Apon, Amy ; Chowdhury, Mashrur ; Comert, Gurcan ; Rahman, Mizanur ; Islam, Mhafuzul ; Sood, Eshaa Deepak ; Deng, Hsien-Wen
• Corporate Creators:
  Clemson University
• Corporate Contributors:
  Center for Connected Multimodal Mobility, Clemson University ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  Cameras Connected Vehicles Data Analysis Data Collection Machine Learning Messages (Communications) Pedestrian Detectors Pedestrians Pedestrian Safety Personal Safety Messages Signalized Intersections Vulnerable Road Users Deep Learning

  More +
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report (09/20/2017 - 6/31/2019)
• Corporate Publisher:
  Center for Connected Multimodal Mobility, Clemson University
• Abstract:
  Although Vehicle-to-Pedestrian (V2P) communication can significantly improve pedestrian safety at a signalized intersection, this safety is hindered when pedestrians do not carry hand-held devices (e.g., Dedicated short-range communication (DSRC) and 5G enabled cell phone) to communicate with connected vehicles nearby. To overcome this limitation, in this project, traffic cameras at a signalized intersection were used to accurately detect and locate pedestrians via a vision-based deep learning technique to generate safety alerts in real-time about possible conflicts between vehicles and pedestrians. The contribution of this project lies in the development of a system using a vision-based deep learning model that is able to generate personal safety messages (PSMs) in real-time (every 100 milliseconds). We develop a pedestrian alert safety system (PASS) to generate a safety alert of an imminent pedestrian-vehicle crash using generated PSMs to improve pedestrian safety at a signalized intersection. Our approach estimates the location and velocity of a pedestrian more accurately than existing DSRC-enabled pedestrian hand-held devices. A connected vehicle application, the Pedestrian in Signalized Crosswalk Warning (PSCW), was developed to evaluate the vision-based PASS. Numerical analyses show that our vision-based PASS is able to satisfy the accuracy and latency requirements of pedestrian safety applications in a connected vehicle environment.
• Format:
  PDF
• Funding:
  69A3551747117
• Collection(s):
  University Transportation Centers US Transportation Collection
• Main Document Checksum:
  urn:sha-512:30e3665854e4c9ccfcc680d2877954f194f552b262c0eb04d3c0668ea30fdbca0a32c75b8fbeb035432b64473e3cdc43ba9ae5fa2f46a0931d16e0a11c21f55a
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/55526/dot_55526_DS1.pdf
• File Type:
  [PDF - 1.45 MB ]