Robust Prediction of Lane Departure Based on Driver Physiological Signals Ford Motor Company

Format:

Conference/Event

Author/Creator:

Kochhar, Kochhar, author.

Contributor:

Murphey, Yi

Watta, Paul

Zhao, Hong

Conference Name:

SAE 2016 World Congress and Exhibition (2016-04-12 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2016

Summary:

AbstractLane change events can be a source of traffic accidents; drivers can make improper lane changes for many reasons. In this paper we present a comprehensive study of a passive method of predicting lane changes based on three physiological signals: electrocardiogram (ECG), respiration signals, and galvanic skin response (GSR). Specifically, we discuss methods for feature selection, feature reduction, classification, and post processing techniques for reliable lane change prediction. Data were recorded for on-road driving for several drivers. Results show that the average accuracy of a single driver test was approximately 70%. It was greater than the accuracy for each cross-driver test. Also, prediction for younger drivers was better

Notes:

Vendor supplied data

Publisher Number:

2016-01-0115

Access Restriction:

Restricted for use by site license