Driver Capability Monitoring in Highly Automated Driving: from state to capability monitoring

Joel Goncalves; Cristina Olaverri-Monreal; Klaus Bengler

doi:10.1109/ITSC.2015.376

Driver Capability Monitoring in Highly Automated Driving: from state to capability monitoring

Joel Goncalves
, Cristina Olaverri-Monreal (Speaker)
, Klaus Bengler

Center for Technology Experience

Technical University of Munich (TUM)

Research output: Chapter in Book or Conference Proceedings › Conference Proceedings with Oral Presentation › peer-review

Abstract

A collision probability estimator in the advent of an emergency Take Over Request (TOR) that considers the driver reaction time and the driver state is an essential tool for developing driver assistance systems for Highly Automated Driving (HAD). In this paper we present an architecture for capturing the driver state and behavior inside the vehicle. This system is then used to predict the collision probability in the situation where drivers resolve the TOR doing a keep lane maneuver (KLM) and brake to avoid the collision. Since this maneuver can be executed safely even under fast reactions, we use it as a reference to determine if is it safe for transferring the vehicle control to the driver.

Original language	English
Title of host publication	Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems
Pages	2329-2334
Number of pages	6
DOIs	https://doi.org/10.1109/ITSC.2015.376
Publication status	Published - 2015
Event	18th IEEE International Conference on Intelligent Transportation Systems - Duration: 15 Sept 2015 → 18 Sept 2015

Conference

Conference	18th IEEE International Conference on Intelligent Transportation Systems
Period	15/09/15 → 18/09/15

Research Field

Former Research Field - Technology Experience

Keywords

Collision Prediction
Driver Monitoring System

Access to Document

10.1109/ITSC.2015.376Licence: Unspecified

Cite this

@inproceedings{489ce895bd9c411d8c470c2589241959,

title = "Driver Capability Monitoring in Highly Automated Driving: from state to capability monitoring",

abstract = "A collision probability estimator in the advent of an emergency Take Over Request (TOR) that considers the driver reaction time and the driver state is an essential tool for developing driver assistance systems for Highly Automated Driving (HAD). In this paper we present an architecture for capturing the driver state and behavior inside the vehicle. This system is then used to predict the collision probability in the situation where drivers resolve the TOR doing a keep lane maneuver (KLM) and brake to avoid the collision. Since this maneuver can be executed safely even under fast reactions, we use it as a reference to determine if is it safe for transferring the vehicle control to the driver.",

keywords = "Collision Prediction, Driver Monitoring System, Collision Prediction, Driver Monitoring System",

author = "Joel Goncalves and Cristina Olaverri-Monreal and Klaus Bengler",

year = "2015",

doi = "10.1109/ITSC.2015.376",

language = "English",

isbn = "978-1-4673-6595-6",

pages = "2329--2334",

booktitle = "Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems",

note = "18th IEEE International Conference on Intelligent Transportation Systems ; Conference date: 15-09-2015 Through 18-09-2015",

}

Goncalves, J, Olaverri-Monreal, C & Bengler, K 2015, Driver Capability Monitoring in Highly Automated Driving: from state to capability monitoring. in Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems. pp. 2329-2334, 18th IEEE International Conference on Intelligent Transportation Systems, 15/09/15. https://doi.org/10.1109/ITSC.2015.376

Driver Capability Monitoring in Highly Automated Driving: from state to capability monitoring. / Goncalves, Joel; Olaverri-Monreal, Cristina (Speaker); Bengler, Klaus.
Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems. 2015. p. 2329-2334.

Research output: Chapter in Book or Conference Proceedings › Conference Proceedings with Oral Presentation › peer-review

TY - GEN

T1 - Driver Capability Monitoring in Highly Automated Driving: from state to capability monitoring

AU - Goncalves, Joel

AU - Bengler, Klaus

A2 - Olaverri-Monreal, Cristina

PY - 2015

Y1 - 2015

N2 - A collision probability estimator in the advent of an emergency Take Over Request (TOR) that considers the driver reaction time and the driver state is an essential tool for developing driver assistance systems for Highly Automated Driving (HAD). In this paper we present an architecture for capturing the driver state and behavior inside the vehicle. This system is then used to predict the collision probability in the situation where drivers resolve the TOR doing a keep lane maneuver (KLM) and brake to avoid the collision. Since this maneuver can be executed safely even under fast reactions, we use it as a reference to determine if is it safe for transferring the vehicle control to the driver.

AB - A collision probability estimator in the advent of an emergency Take Over Request (TOR) that considers the driver reaction time and the driver state is an essential tool for developing driver assistance systems for Highly Automated Driving (HAD). In this paper we present an architecture for capturing the driver state and behavior inside the vehicle. This system is then used to predict the collision probability in the situation where drivers resolve the TOR doing a keep lane maneuver (KLM) and brake to avoid the collision. Since this maneuver can be executed safely even under fast reactions, we use it as a reference to determine if is it safe for transferring the vehicle control to the driver.

KW - Collision Prediction

KW - Driver Monitoring System

KW - Collision Prediction

KW - Driver Monitoring System

U2 - 10.1109/ITSC.2015.376

DO - 10.1109/ITSC.2015.376

M3 - Conference Proceedings with Oral Presentation

SN - 978-1-4673-6595-6

SP - 2329

EP - 2334

BT - Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems

T2 - 18th IEEE International Conference on Intelligent Transportation Systems

Y2 - 15 September 2015 through 18 September 2015

ER -

Driver Capability Monitoring in Highly Automated Driving: from state to capability monitoring

Abstract

Conference

Research Field

Keywords

Access to Document

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Cite this