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Carnegie Mellon Robotics Institute

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Abstract

In this paper, we describe a preliminary analysis of driver data collected during a recently completed small scale data collection effort. We will demonstrate that a popular method for computing Time To Lane Crossing (TLC) does not always accurately predict the driver's actual TLC. We will then use a memory based learning approach to show why this is. Finally, we will present results in predicting the driver's future lane position using the new memory based approach.

Keywords

lane tracking, human factors

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): NavLab

Text Reference

Parag Batavia, Dean Pomerleau, and Chuck Thorpe, "Predicting Lane Position for Roadway Departure Prevention," Proceedings of the IEEE Intelligent Vehicles Symposium, October, 1998.

BibTeX Reference

@inproceedings{Batavia_1998_470,
   author = "Parag Batavia and Dean Pomerleau and Chuck Thorpe",
   title = "Predicting Lane Position for Roadway Departure Prevention",
   booktitle = "Proceedings of the IEEE Intelligent Vehicles Symposium",
   publisher = "IEEE",
   month = "October",
   year = "1998",
}

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