Safe-D: Safety through Disruption

Driving Risk Assessment Based on High-frequency, High-resolution Telematics Data



The emerging connected vehicle and Automated Driving System (ADS) as well as widely available advanced in-vehicle telematics data collection/transmitting systems produce gigantic amount of high-frequency, high-resolution driving data. These telematics data provide comprehensive information on driving style, driving environment, road condition, and vehicle condition. The telematics data has been used for a number of safety areas such as insurance pricing, teenage driving risk evaluation, and fleet safety management. The surge of ride-hailing service in the last decade provides a novel alternative mode for travelers. The ride-hailing drivers are a unique driver population with substantial operational responsibilities and the safety management is critical for the drivers. The smartphone ride-hailing app can conveniently collect kinematic information from sensors on smartphones, thus make the telematics data available for the entire driver population. Parallel to this proposed study, the research team has evaluated telematics feature in prediction crash risk for millions of ride-hailing drivers. This project will address the following main safety research questions using high-frequency, high resolution telematics data: 1) characterize the high-frequency kinematic signatures for safety critical events as well as during normal operations; 2) develop models to predict high risk drivers based on the kinematics signatures. 3) develop models to distinguish and predict crashes from normal driving scenarios based on the high frequency data. The project will contribute to connected vehicles and ADS real-time safety monitoring, NDS data analysis, hail-driving driver safety prediction, as well as fleet and driver safety management programs.

Project Highlights

Coming Soon!

Final Report

Coming Soon!

EWD & T2 Products

WEB APP – This webapp detects/predicts crashes and near-crashes based on kinematic driving data. The model adopts a combination of convolutional neural network (CNN) and gated recurrent unit (GRU) network to capture both local features and temporal dependency of the kinematic signatures.


Coming Soon!

Research Investigators (PI*)

Feng Guo (VTTI/VT)*
Chen Qian (Student-VTTI/VT)
Liang Shi (Student-VTTI/VT)

Project Information

Start Date: 2019-12-15
End Date: 2021-08-30
Status: Active
Grant Number: 69A3551747115
Total Funding: $150,000.00
Source Organization: Safe-D National UTC
Project Number: VTTI-00-028

Safe-D Theme Areas

Big Data Analytics
Automated Vehicles
Connected Vehicles

Safe-D Application Areas

Risk Assessment
Driver Factors and Influences
Performance Measures
Planning for Safety

More Information

UTC Project Information Form

Sponsor Organization

Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States

Performing Organization

Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061