Comparison of Dimensionality Reduction Methods for Road Surface Identification System

Safont, Gonzalo; Salazar, Addisson; Rodríguez-Martínez, Alberto; Vergara, Luis

doi:10.1007/978-3-030-52246-9_40

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…The feature ranking method proposed in [13] was applied, retaining the best ranked feature of every channel to get 10 selected features.…”

Section: Featurementioning

confidence: 99%

“…However, during the last few years, the emergence of autonomous or semi-autonomous car technology has strongly increased the interest in on-board systems, which can perform real-time surface classification to aid the driving assistance systems of the car by automatic power-assisted steering. A variety of works have recently been reported in this very active research area [6][7][8][9][10][11][12][13][14][15][16][17]. Most of them propose machine learning approaches, where an automatic classifier is to be trained from labelled feature vectors derived from sensor signals.…”

Section: Introduction 1preliminariesmentioning

confidence: 99%

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On Training Road Surface Classifiers by Data Augmentation

et al. 2022

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It is demonstrated that data augmentation is a promising approach to reduce the size of the captured dataset required for training automatic road surface classifiers. The context is on-board systems for autonomous or semi-autonomous driving assistance: automatic power-assisted steering. Evidence is obtained by extensive experiments involving multiple captures from a 10-channel multisensor deployment: three channels from the accelerometer (acceleration in the X, Y, and Z axes); three microphone channels; two speed channels; and the torque and position of the handwheel. These captures were made under different settings: three worm-gear interface configurations; hands on or off the wheel; vehicle speed (constant speed of 10, 15, 20, 30 km/h, or accelerating from 0 to 30 km/h); and road surface (smooth flat asphalt, stripes, or cobblestones). It has been demonstrated in the experiments that data augmentation allows a reduction by an approximate factor of 1.5 in the size of the captured training dataset.

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“…The feature ranking method proposed in [13] was applied, retaining the best ranked feature of every channel to get 10 selected features.…”

Section: Featurementioning

confidence: 99%