Drivers’ Manoeuvre Classification for Safe HRI

Erwin José López Pulgarín*, Guido Herrmann, Ute Leonards

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
237 Downloads (Pure)

Abstract

Ever increasing autonomy of machines and the need to interact with them creates challenges to ensure safe operation. Recent technical and commercial interest in increasing autonomy of vehicles has led to the integration of more sensors and actuators inside the vehicle, making them more like robots. For interaction with semi-autonomous cars, the use of these sensors could help to create new safety mechanisms. This work explores the concept of using motion tracking (i.e. skeletal tracking) data gathered from the driver whilst driving to learn to classify the manoeuvre being performed. A kernel-based classifier is trained with empirically selected features based on data gathered from a Kinect V2 sensor in a controlled environment. This method shows that skeletal tracking data can be used in a driving scenario to classify manoeuvres and sets a background for further work.

Original languageEnglish
Title of host publicationTowards Autonomous Robotic Systems
Subtitle of host publication18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017, Proceedings
PublisherSpringer London
Pages475-483
Number of pages9
ISBN (Electronic)9783319641072
ISBN (Print)9783319641065
DOIs
Publication statusPublished - 20 Jul 2017
Event18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017 - Guildford, United Kingdom
Duration: 19 Jul 201721 Jul 2017

Publication series

NameLecture Notes in Computer Science (Lecture notes in Artificial Intelligence)
PublisherSpringer
Volume10454
ISSN (Print)0302-9743

Conference

Conference18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017
CountryUnited Kingdom
CityGuildford
Period19/07/1721/07/17

Bibliographical note

Best poster prize sponsored by UK-RAS Network

Structured keywords

  • Brain and Behaviour
  • Cognitive Science
  • Visual Perception

Keywords

  • Classification
  • Driver actions
  • HRI
  • Machine learning
  • Semi-autonomous
  • vehicles
  • Vehicles

Fingerprint Dive into the research topics of 'Drivers’ Manoeuvre Classification for Safe HRI'. Together they form a unique fingerprint.

  • Prizes

    Erwin Lopez - ‘Best Poster Price’ at the TAROS 2017, 18th Towards Autonomous Robotic Systems (TAROS) Conference.

    Erwin José López Pulgarín (Recipient), Leonards, Ute B (Recipient) & Guido Herrmann (Recipient), Jul 2017

    Prize: Prizes, Medals, Awards and Grants

    Cite this

    López Pulgarín, E. J., Herrmann, G., & Leonards, U. (2017). Drivers’ Manoeuvre Classification for Safe HRI. In Towards Autonomous Robotic Systems: 18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017, Proceedings (pp. 475-483). (Lecture Notes in Computer Science (Lecture notes in Artificial Intelligence); Vol. 10454). Springer London. https://doi.org/10.1007/978-3-319-64107-2_37