Reference Machine Vision for ADAS functions
Dec 30 2021: Abhishek Nayak
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 was to develop a reference system for lane detection (LD) that will provide a
benchmark for evaluating different lane markings, pavements and perception algorithms. The goal of the
project is to create a system that will validate the effectiveness of lane markings and the vision
algorithms through a systematic development of LD metrics, and testing procedures for LD algorithms. I
generated an extensive lane detection (LD) dataset by driving on various roads in Central Texas with
changing weather conditions, time of day, pavement marking quality, and pavement materials. Further, I
studied the relationship between lane marking quality and LD algorithm performance used in ADAS using
statistical methods to propose a reference test system for state agencies and OEMs to benchmark lane marking
quality and its effect on LD performance.
Follow this link to view the project on Safe-D Website.
This is Safe-D UTC sponsored project. (https://rip.trb.org/view/1599232)
Referances:
- Nayak, A., Pike, A., & Rathinam, S.(2022). Effect of pavement markings on machine vision used in ADAS functions (No. 2022-01-0154). SAE Technical Paper.
- Nayak, A., Pike, A., Rathinam, S., & Gopalswamy, S. (2020). Reference test system for machine vision used for ADAS functions (No. 2020-01-0096). SAE Technical Paper.