Automotive surface identification system

Aleksandr Bystrov; Edward Hoare; Thuy-Yung Tran; Nigel Clarke; Marina Gashinova; Mikhail Cherniakov

doi:10.1109/ICVES.2017.7991911

Automotive surface identification system

Aleksandr Bystrov, Edward Hoare, Thuy-Yung Tran, Nigel Clarke, Marina Gashinova, Mikhail Cherniakov

Electronic, Electrical and Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

In this paper the practical issues of automotive surface identification system development are considering. The novelty of this work is the combining of different training algorithms, neural network structures and methods to increase the classification accuracy and avoid overfitting of real-world data. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real driving condition.

Original language	English
Title of host publication	Vehicular Electronics and Safety (ICVES), IEEE International Conference on
Publisher	IEEE Xplore
Pages	115-120
Number of pages	6
DOIs	https://doi.org/10.1109/ICVES.2017.7991911
Publication status	Published - 27 Jul 2017

Keywords

sonar applications
Remote Sensing
Artificial neural network
parameter extraction

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10.1109/ICVES.2017.7991911

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@inproceedings{db585863d45748c7a078100649a17073,

title = "Automotive surface identification system",

abstract = "In this paper the practical issues of automotive surface identification system development are considering. The novelty of this work is the combining of different training algorithms, neural network structures and methods to increase the classification accuracy and avoid overfitting of real-world data. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real driving condition. ",

keywords = "sonar applications, Remote Sensing, Artificial neural network, parameter extraction",

author = "Aleksandr Bystrov and Edward Hoare and Thuy-Yung Tran and Nigel Clarke and Marina Gashinova and Mikhail Cherniakov",

year = "2017",

month = jul,

day = "27",

doi = "10.1109/ICVES.2017.7991911",

language = "English",

pages = "115--120",

booktitle = "Vehicular Electronics and Safety (ICVES), IEEE International Conference on",

publisher = "IEEE Xplore",

}

TY - GEN

T1 - Automotive surface identification system

AU - Bystrov, Aleksandr

AU - Hoare, Edward

AU - Tran, Thuy-Yung

AU - Clarke, Nigel

AU - Gashinova, Marina

AU - Cherniakov, Mikhail

PY - 2017/7/27

Y1 - 2017/7/27

N2 - In this paper the practical issues of automotive surface identification system development are considering. The novelty of this work is the combining of different training algorithms, neural network structures and methods to increase the classification accuracy and avoid overfitting of real-world data. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real driving condition.

AB - In this paper the practical issues of automotive surface identification system development are considering. The novelty of this work is the combining of different training algorithms, neural network structures and methods to increase the classification accuracy and avoid overfitting of real-world data. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real driving condition.

KW - sonar applications

KW - Remote Sensing

KW - Artificial neural network

KW - parameter extraction

U2 - 10.1109/ICVES.2017.7991911

DO - 10.1109/ICVES.2017.7991911

M3 - Conference contribution

SP - 115

EP - 120

BT - Vehicular Electronics and Safety (ICVES), IEEE International Conference on

PB - IEEE Xplore

ER -

Automotive surface identification system

Abstract

Keywords

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