Driver lane change intention recognition by using entropy-based fusion techniques and support vector machine learning strategy

Permanent URL: http://hdl.handle.net/2047/d20002976
Title:
Driver lane change intention recognition by using entropy-based fusion techniques and support vector machine learning strategy
Creator:
Huang, Xianyi (Author)
Contributor:
Lin, Yingzi (Advisor)
Publisher:
Boston, Massachusetts : Northeastern University, 2012
Date Accepted:
December 2012
Date Awarded:
May 2013
Type of resource:
Text
Genre:
Masters theses
Format:
electronic
Digital origin:
born digital
Abstract/Description:
In this Thesis, we focus on the analysis of driver lane-changing behavior based on the fact that lane changing is a ubiquitous driving maneuver in common driving environments and regarded as the most critical driving intention. Therefore, lane changing as a case study for driving intention recognition is introduced in this study. Our methodology is to employ machine learning method i.e., support vector machine, to the classification of driving intentions using vehicle performance data and driver eye gaze data from the measurement of the driving tasks (i.e., lane following and lane changing) in the well-designed simulation environment. To improve recognition performance, this thesis illustrates the use of entropy based fusion method to discard those largely negative dependent data to decrease the redundancy level of input data. Based on entropy correlation coefficient analysis, heading angle, as one kind of performance data, is harmful for recognition result. Incorporation of eye gaze data enhances the recognition performance. Introduced by three-stage nested design, experiments are executed to screen out the best "supplies" for pattern learning. Final results show that feature set fused by steering angle, gas pressure, velocity and acceleration performance data as well as eye gaze data achieves 88.78% accuracy with a time length of 0.6 seconds at 5% false alarm level.
Subjects and keywords:
Entropy
Information Fusion
Lane Change Intention
Support Vector Machine
Computer and Systems Architecture
Computer Engineering
DOI:
https://doi.org/10.17760/d20002976
Permanent Link:
http://hdl.handle.net/2047/d20002976
Use and reproduction:
In Copyright: This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the right-holder(s). (http://rightsstatements.org/vocab/InC/1.0/)
Copyright restrictions may apply.

Downloads