Incremental Learning for Motion Prediction of Pedestrians and Vehicles

  • Alejandro Dizan Vasquez Govea

Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 64)

Table of contents

  1. Front Matter
  2. Introduction

    1. Alejandro Dizan Vasquez Govea
      Pages 1-7
  3. Part I: Background

    1. Front Matter
      Pages 9-9
    2. Alejandro Dizan Vasquez Govea
      Pages 11-24
  4. Part II: State of the Art

    1. Front Matter
      Pages 25-25
    2. Alejandro Dizan Vasquez Govea
      Pages 27-43
    3. Alejandro Dizan Vasquez Govea
      Pages 45-68
  5. Part III: Proposed Approach

    1. Front Matter
      Pages 69-69
    2. Alejandro Dizan Vasquez Govea
      Pages 71-82
    3. Alejandro Dizan Vasquez Govea
      Pages 83-94
  6. Part IV: Experiments

    1. Front Matter
      Pages 95-95
    2. Alejandro Dizan Vasquez Govea
      Pages 97-101
    3. Alejandro Dizan Vasquez Govea
      Pages 103-127
  7. Part V: Conclusion

    1. Front Matter
      Pages 129-129
    2. Alejandro Dizan Vasquez Govea
      Pages 131-135
  8. Back Matter

About this book

Introduction

Modeling and predicting human and vehicle motion is an active research domain. Owing to the difficulty in modeling the various factors that determine motion (e.g. internal state, perception) this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (e.g. camera, laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use offline learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished.

This books presents a lifelong learning approach where motion patterns can be learned incrementally, and in parallel with prediction. The approach is based on a novel extension to hidden Markov models, and the main contribution presented in this book, called growing hidden Markov models, which gives us the ability to learn incrementally both the parameters and the structure of the model. The proposed approach has been extensively validated with synthetic and real trajectory data. In our experiments our approach consistently learned motion models that were more compact and accurate than those produced by two other state-of-the-art techniques, confirming the viability of lifelong learning approaches to build human behavior models.

Keywords

Hidden Markov Models Motion prediction behaviour modelling hidden markov model machine learning robot robotics

Authors and affiliations

  • Alejandro Dizan Vasquez Govea
    • 1
  1. 1.Autonomous Systems LabETH ZürichZurichSwitzerland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-13642-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-13641-2
  • Online ISBN 978-3-642-13642-9
  • Series Print ISSN 1610-7438
  • Series Online ISSN 1610-742X
  • About this book