A classification method for driver trajectories during curve-negotiation

Conference Paper (2019)
Author(s)

Sarah Barendswaard (TU Delft - Human-Robot Interaction)

Daan Pool (TU Delft - Control & Simulation)

E.R. Boer (TU Delft - Human-Robot Interaction)

D.A. Abbink (TU Delft - Human-Robot Interaction)

Research Group
Human-Robot Interaction
DOI related publication
https://doi.org/10.1109/SMC.2019.8914301
More Info
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Publication Year
2019
Language
English
Related content
Research Group
Human-Robot Interaction
Pages (from-to)
3729-3734
ISBN (electronic)
978-1-7281-4569-3

Abstract

When taking a curve, drivers follow their own unique trajectory. Most driver style classifiers in literature are based on inertial inputs, denoting whether a given driver is aggressive or calm. However, this does not give any indication of a drivers trajectory style, i.e. whether a driver is curve cutting. To fill this void, this paper introduces a novel rule based classifier that categorises seven different trajectory styles. The classifier is applied to data from a fixed-base driving simulator study in which 45 subjects drove on three roads, comprising three different velocities: 25, 50 and 80 km/h, with three corresponding radii: 20, 80 and 204 m. The results show that some classes are more prevalent than others, with biased outer curve negotiation performed by a majority of the subjects and with no drivers classified as centerline drivers. The proposed trajectory classifier is shown to exhibit high levels of consistency, with 93% of drivers exhibiting consistent trajectory classes for at least 66% of the right curves driven and 84% exhibits consistent trajectory classes for atleast 66% of the left curves driven. Where this consistency indicates a potential for generalising the classification results to other curves. Additionally, this classifier can be used to adapt trajectory-driven advanced driver assistance systems, thereby serving as an alternative to driver modelling.

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