Skip to content

Drivers’ Manoeuvre Classification for Safe HRI

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Drivers’ Manoeuvre Classification for Safe HRI. / López Pulgarín, Erwin José; Herrmann, Guido; Leonards, Ute.

Towards Autonomous Robotic Systems: 18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017, Proceedings. Springer London, 2017. p. 475-483 (Lecture Notes in Computer Science (Lecture notes in Artificial Intelligence); Vol. 10454).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

López Pulgarín, EJ, 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. Lecture Notes in Computer Science (Lecture notes in Artificial Intelligence), vol. 10454, Springer London, pp. 475-483, 18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017, Guildford, United Kingdom, 19/07/17. https://doi.org/10.1007/978-3-319-64107-2_37

APA

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

Vancouver

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

Author

López Pulgarín, Erwin José ; Herrmann, Guido ; Leonards, Ute. / Drivers’ Manoeuvre Classification for Safe HRI. Towards Autonomous Robotic Systems: 18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017, Proceedings. Springer London, 2017. pp. 475-483 (Lecture Notes in Computer Science (Lecture notes in Artificial Intelligence)).

Bibtex

@inproceedings{5921e5a14c0c482f9082fec4e42a3298,
title = "Drivers’ Manoeuvre Classification for Safe HRI",
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.",
keywords = "Classification, Driver actions, HRI, Machine learning, Semi-autonomous, vehicles, Vehicles",
author = "{L{\'o}pez Pulgar{\'i}n}, {Erwin Jos{\'e}} and Guido Herrmann and Ute Leonards",
note = "Best poster prize sponsored by UK-RAS Network",
year = "2017",
month = "7",
day = "20",
doi = "10.1007/978-3-319-64107-2_37",
language = "English",
isbn = "9783319641065",
series = "Lecture Notes in Computer Science (Lecture notes in Artificial Intelligence)",
publisher = "Springer London",
pages = "475--483",
booktitle = "Towards Autonomous Robotic Systems",
address = "United Kingdom",

}

RIS - suitable for import to EndNote

TY - GEN

T1 - Drivers’ Manoeuvre Classification for Safe HRI

AU - López Pulgarín, Erwin José

AU - Herrmann, Guido

AU - Leonards, Ute

N1 - Best poster prize sponsored by UK-RAS Network

PY - 2017/7/20

Y1 - 2017/7/20

N2 - 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.

AB - 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.

KW - Classification

KW - Driver actions

KW - HRI

KW - Machine learning

KW - Semi-autonomous

KW - vehicles

KW - Vehicles

UR - http://www.scopus.com/inward/record.url?scp=85026766500&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-64107-2_37

DO - 10.1007/978-3-319-64107-2_37

M3 - Conference contribution

SN - 9783319641065

T3 - Lecture Notes in Computer Science (Lecture notes in Artificial Intelligence)

SP - 475

EP - 483

BT - Towards Autonomous Robotic Systems

PB - Springer London

ER -