Cart (Loading....) | Create Account
Close category search window
 

Bayesian on-line learning of driving behaviors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Maye, J. ; Autonomous Syst. Lab., ETH Zurich, Zurich, Switzerland ; Triebel, R. ; Spinello, L. ; Siegwart, R.

This paper presents a novel self-supervised online 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.

Published in:

Robotics and Automation (ICRA), 2011 IEEE International Conference on

Date of Conference:

9-13 May 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.