Sensors for Automotive Remote Road Surface Classification

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

doi:10.1109/ICVES.2018.8519499

Sensors for Automotive Remote Road Surface Classification

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

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

Abstract

In this paper, we compare the common remote sensing technologies in terms of their application for road surface classification. The presence of surface classification system in a vehicle will increase the safety of driving, especially in adverse weather conditions, as well as when driving off-road. The paper presents an overview of the application of optical, laser, ultrasonic, and microwave sensors for surface classification. From the analysis it follows that sensor data fusion allows obtaining more accurate and reliable results.

Original language	English
Title of host publication	2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Print)	978-1-5386-3544-5
DOIs	https://doi.org/10.1109/ICVES.2018.8519499
Publication status	Published - 14 Sept 2018
Event	2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES) - Madrid, Spain Duration: 12 Sept 2018 → 14 Sept 2018

Conference

Conference	2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)
Period	12/09/18 → 14/09/18

Keywords

Roads
Optical surface waves
Snow
Laser radar
Rough surfaces
Surface roughness
Ice

Access to Document

10.1109/ICVES.2018.8519499

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8519499

Cite this

@inproceedings{657ccc4163674ee29f8c5f6ee3c99e21,

title = "Sensors for Automotive Remote Road Surface Classification",

abstract = "In this paper, we compare the common remote sensing technologies in terms of their application for road surface classification. The presence of surface classification system in a vehicle will increase the safety of driving, especially in adverse weather conditions, as well as when driving off-road. The paper presents an overview of the application of optical, laser, ultrasonic, and microwave sensors for surface classification. From the analysis it follows that sensor data fusion allows obtaining more accurate and reliable results.",

keywords = "Roads, Optical surface waves, Snow, Laser radar, Rough surfaces, Surface roughness, Ice",

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

year = "2018",

month = sep,

day = "14",

doi = "10.1109/ICVES.2018.8519499",

language = "English",

isbn = "978-1-5386-3544-5",

pages = "1--6",

booktitle = "2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)",

publisher = "IEEE",

note = "2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES) ; Conference date: 12-09-2018 Through 14-09-2018",

}

Bystrov, A, Hoare, E, Tran, T-Y, Clarke, N, Gashinova, M & Cherniakov, M 2018, Sensors for Automotive Remote Road Surface Classification. in 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)., 8519499, IEEE, pp. 1-6, 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), 12/09/18. https://doi.org/10.1109/ICVES.2018.8519499

TY - GEN

T1 - Sensors for Automotive Remote Road Surface Classification

AU - Bystrov, Aleksandr

AU - Hoare, Edward

AU - Tran, Thuy-Yung

AU - Clarke, Nigel

AU - Gashinova, Marina

AU - Cherniakov, Mikhail

PY - 2018/9/14

Y1 - 2018/9/14

N2 - In this paper, we compare the common remote sensing technologies in terms of their application for road surface classification. The presence of surface classification system in a vehicle will increase the safety of driving, especially in adverse weather conditions, as well as when driving off-road. The paper presents an overview of the application of optical, laser, ultrasonic, and microwave sensors for surface classification. From the analysis it follows that sensor data fusion allows obtaining more accurate and reliable results.

AB - In this paper, we compare the common remote sensing technologies in terms of their application for road surface classification. The presence of surface classification system in a vehicle will increase the safety of driving, especially in adverse weather conditions, as well as when driving off-road. The paper presents an overview of the application of optical, laser, ultrasonic, and microwave sensors for surface classification. From the analysis it follows that sensor data fusion allows obtaining more accurate and reliable results.

KW - Roads

KW - Optical surface waves

KW - Snow

KW - Laser radar

KW - Rough surfaces

KW - Surface roughness

KW - Ice

UR - https://ieeexplore.ieee.org/document/8519499/

U2 - 10.1109/ICVES.2018.8519499

DO - 10.1109/ICVES.2018.8519499

M3 - Conference contribution

SN - 978-1-5386-3544-5

SP - 1

EP - 6

BT - 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)

PB - IEEE

T2 - 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)

Y2 - 12 September 2018 through 14 September 2018

ER -

Sensors for Automotive Remote Road Surface Classification

Abstract

Conference

Keywords

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