NDLI: Bayesian on-line learning of driving behaviors

Content Provider	IEEE Xplore Digital Library
Author	Maye, J. Triebe, R. Spinello, L. Siegwart, R.
Copyright Year	2011
Description	Author affiliation: Autonomous Systems Lab, ETH Zurich, Switzerland (Maye, J.; Triebe, R.; Siegwart, R.) \|\| Social Robotics Lab, University of Freiburg, Germany (Spinello, L.)
Abstract	This paper presents a novel self-supervised on-line learning method to discover driving behaviors from data acquired with an inertial measurement unit (IMU) and a camera. Both sensors where mounted in a car that was driven by a human through a typical city environment with intersections, pedestrian crossings and traffic lights. The presented system extracts motion segments from the IMU data and relates them to visual cues obtained from camera data. It employs a Bayesian on-line estimation method to discover the motion segments based on change-point detection and uses a Dirichlet Compound Multinomial (DCM) model to represent the visual features extracted from the camera images. By incorporating these visual cues into the on-line estimation process, labels are computed that are equal for similar motion segments. As a result, typical traffic situations such as braking maneuvers in front of a red light can be identified automatically. Furthermore, appropriate actions in form of observed motion changes are associated to the discovered traffic situations. The approach is evaluated on a real data set acquired in the center of Zurich.
Starting Page	4341
Ending Page	4346
File Size	1638079
Page Count	6
File Format	PDF
ISBN	9781612843865
ISSN	10504729
e-ISBN	9781612843858
DOI	10.1109/ICRA.2011.5980414
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motion segmentation Computational modeling Vehicles Visualization Cameras Image segmentation Data models
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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