Safe-D: Safety through Disruption

Reference Machine Vision for ADAS Functions



Studies have shown that fatalities due to unintentional roadway departures can be significantly reduced if Lane Departure Warning (LDW) and Lane Keep Assist (LKA) systems are used effectively. However, these systems are not yet popular because the systems are not robust due, in part to the lack of suitable standards for pavement markings that enable reliable functionality of the sensor system. The objective of this project is to develop a reference Lane Detection (LD) system that will provide a benchmark for evaluating different lane markings and perception algorithms. The project will also validate the effectiveness of lane markings as well as the vision algorithms through a systematic development of LD metrics, and testing of LD algorithms in a robust test/vehicle environment.

Project Highlights

Coming Soon!

Final Report

04-115 Final Report

EWD & T2 Products

Coming Soon!


Yang, L., Furukawa, T., Zuo, L., Parker, R., Doerzaph(2019). Estimation of Roadside for Autonomous Emergency Stop. In Proceedings of Fifth International Symposium on Future Active Safety Technology: Toward zero traffic accidents (FAST-zero), Blacksburg, VA, USA. (Accepted)

Research Investigators (PI*)

Sivakumar Rathinam (TAMU)*
Swaminathan Gopalswamy (TTI/TAMU)
Abhishek Nayak (TAMU)

Project Information

Start Date: 2019-01-01
End Date: 2020-05-15
Status: Complete
Grant Number: 69A3551747115
Total Funding: $340,160
Source Organization: Safe-D National UTC
Project Number: 04-115

Safe-D Theme Areas

Automated Vehicles
Connected Vehicles

Safe-D Application Areas

Infrastructure Technology
Planning for Safety
Operations and Design
Vehicle Technology

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

Texas A&M University
Texas A&M Transportation Institute
3135 TAMU
College Station, Texas 77843-3135