research-article

A low-cost solution for an integrated multisensor lane departure warning system

Authors:

Joshua M. Clanton,

David M. Bevly,

A. Scottedward HodelAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 10, Issue 1

Pages 47 - 59

https://doi.org/10.1109/TITS.2008.2011690

Published: 01 March 2009 Publication History

Abstract

The responsibility of a vision-based lane departure warning (LDW) system is to alert a driver of an unintended lane departure. Because these systems solely rely on the vision sensor's ability to detect the lane markings on the roadway, these systems are extremely sensitive to the roadway conditions. When a vehicle's LDW system fails to detect lane markers on the roadway, it loses its ability to alert the driver of an unintended lane departure. The goal of this research is to use GPS combined with inertial sensors and a high-accuracy map to assist a vision-based LDW system. GPS navigation systems are available in many automobiles, along with automotive-grade inertial sensors. The low accuracy of a typical GPS receiver found in an automotive navigation system is largely attributed to a position error. This error is too large to allow the GPS receiver to locate a vehicle in a particular lane on a roadway. A method to measure this error using a vision-based LDW system, together with a high-accuracy map, is presented in this paper. With the error known, the accuracy of the GPS receiver is increased to a high-enough level to localize the vehicle on a particular lane. Next, a method fusing GPS/inertial navigation sensor/vision and a high-accuracy map for highway lane tracking is presented. This method provides a backup lateral offset measurement that can be used for LDW when the LDW vision system loses track of the lane markings.

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 10, Issue 1

March 2009

197 pages

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IEEE Press

Publication History

Published: 01 March 2009

Revised: 04 December 2007

Received: 01 May 2007

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https://dl.acm.org/doi/10.1155/2022/3425295
Kim HPark JMin KHuh K(2021)Anomaly Monitoring Framework in Lane Detection With a Generative Adversarial NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.297339822:3(1603-1615)Online publication date: 1-Mar-2021
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Xie SChen SZheng NWang J(2020)Modeling Methodology of Driver-Vehicle-Environment System Dynamics in Mixed Driving Situation2020 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV47402.2020.9304850(1984-1991)Online publication date: 19-Oct-2020
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