Real-time Safety Diagnosis System for Connected Vehicles with Parallel Computing Architecture (Project O6)

The ongoing STRIDE F4 project – Automatic Safety Diagnosis in Connected Vehicle Environment – is to construct a computational pipeline of a near-crash diagnosis system to identify near-crash events by processing the Basic Safety Messages (BSMs) generated in the Connected Vehicle (CV) environment on the individual level. The in-vehicle system identifies outliers by analyzing BSMs from nearby vehicles and comparing with each individual driver’s past normal driving pattern provided by the Traffic Management Center (or a cloud server). The speed of data processing and transmission at both the cloud system and the in-vehicle system can be quite demanding. For the near-crash warning signal to be generated promptly in real-time environment, parallel computing is indispensable. The parallel computing technology can be incorporated into both the cloud system and the in-vehicle system. First, the amount of BSMs received by the cloud server from the CVs could be massive up to several hundreds of GBs/sec. The data collection, data updating and warning massage broadcasting at the cloud server and the in-vehicle system can be carried out in a parallel fashion by using parallel computing. Second, vehicles are equipped with small computers to analyze the BSMs from all nearby vehicles. The in-vehicle data processing can also be accelerated by parallel computing. The research team proposes to continue their current research using the parallel computing technology to accelerate the data processing and analysis in both the cloud system and the in-vehicle system. The group has extensive experience in parallel computing in solving large-scale fluid flow problems using the Message Passing Interface (MPI) library and the OpenCL technology. These technologies make the most out of today’s heterogeneous computing systems equipped with multi-core CPUs and GPUs. The team would like to leverage their existing parallel computing practice and adapt it to the traffic safety message processing and analysis.

Language

• English

Project

• Status: Active
• Funding: $57500
• Contract Numbers:

  69A3551747104

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)

  University of Florida
  365 Weil Hall
  Gainesville, FL  United States  32611
• Project Managers:

  Tucker-Thomas, Dawn

• Performing Organizations:

  Jackson State University, Jackson

  Department of Civil and Environmental Engineering
  Jackson, MS  United States  39217-0168
• Principal Investigators:

  Tu, Shuang

• Start Date: 20220501
• Expected Completion Date: 20230430
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Cloud computing; Connected vehicles; Data processing operations; Near crashes; Warning systems
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01854188
• Record Type: Research project
• Source Agency: Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)
• Contract Numbers: 69A3551747104
• Files: UTC, RIP
• Created Date: Aug 10 2022 3:07PM