Abstract
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
- A systems approach to validate the effectiveness of different types of lane markings for detectability using state-of-the-art lane detection (LD) algorithms.
- Statistical analysis of the effect of environmental factors (Day vs Night), driving direction, lane marking material characteristics (reflective properties like Qd/RL, marking quality), lane making layouts (30ft gap vs 40ft gap, 4inch wide vs 6 inches wide), and evaluation characteristics (Lane detection (LD) algorithm, Near Field-of-view (FOV) vs Far FOV) on LD performance.
- Annotated image datasets along with lane marking material characteristic data.
- College Station Dataset (On-road with Material data)
- 3M panel dataset (Closed course with material data)
Final Report
EWD & T2 Products
Outreach activities:
Abhishek Nayak volunteered as a judge in the 2021 Virtual Edition of the Virginia State Science and Engineering Fair (VSSEF) conducted on April 10, 2021, and participated in knowledge-sharing discussions.
Professional development courses, seminars:
Rathinam, Sivakumar (2020) “Understanding the Correlation between the Quality of Markings and Lane Detection/Following Systems”. Invited talk and panel discussion at “Development of Needs and Scope for Cooperative Infrastructure-Vehicle Detection and Localization for Automated Vehicles”, 3rd IEEE-ITS symposium, Sept 21, 2020.
Rathinam, Sivakumar, (2019) “Reference Machine Vision for Advanced Driver Assist Systems (ADAS)”. Breakout session on “Reading the Road Ahead: Preparing Highway Infrastructure for ADAS and High Automation,” at Automated Vehicles Symposium 2019, Orlando FL, July 16, 2019.
Student thesis/dissertations resulting from the project:
Abhishek Nayak; Department of Mechanical Engineering, Texas A&M University; “Planning and vision-based systems for Autonomous vehicles”, May 2022 (expected).
Student Impact Statement – Abhishek Nayak (pdf): The student(s) working on this project provided an impact statement describing what the project allowed them to learn/do/practice and how it benefited their education. Student’s Linkedin can be found here.
Nayak, A., Rathinam, S., Pike, A., & Gopalswamy, S. (2020). Reference Test System for Machine Vision Used for ADAS Functions (No. 2020-01-0096). SAE Technical Paper.
Final Dataset
The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/5FGGKD.
Presentations/Publications
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)
Rathinam, Sivakumar (2020) “Understanding the Correlation between the Quality of Markings and Lane Detection/Following Systems”. Invited talk and panel discussion at “Development of Needs and Scope for Cooperative Infrastructure-Vehicle Detection and Localization for Automated Vehicles”, 3rd IEEE-ITS symposium, Sept 21, 2020.
Nayak, Abhishek, (2019) “Reference Machine Vision for ADAS Functions”. Invited talk at Safe-D UTC Graduate Student Leadership Development Seminar Series, College Station TX, October 10, 2019.
Rathinam, Sivakumar (2019) “Reference Machine Vision for ADAS Functions”. Invited talk and panel session at the “Autonomous cars conference”, Brookings Institution, Washington D.C., July 25, 2019.
Rathinam, Sivakumar, (2019) “Reference Machine Vision for Advanced Driver Assist Systems (ADAS)”. in the Breakout session on “Reading the Road Ahead: Preparing Highway Infrastructure for ADAS and High Automation,” at, Automated Vehicles Symposium 2019, Orlando FL, July 16, 2019.
Nayak, Abhishek. (2019). RAVEV – Response of Autonomous Vehicles to Emergency Vehicles. Poster presented at the 4th Annual Texas A&M Transportation Technology Conference, College Station TX, April 30, 2019.
Research Investigators (PI*)
Sivakumar Rathinam (TAMU)*
Swaminathan Gopalswamy (TTI/TAMU)
Abhishek Nayak (TAMU)
Project Information
Start Date: 2019-01-01
End Date: 2021-05-15
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
RiP URL
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
USA