Developing intelligent blind spot detection system for heavy goods vehicles

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

6 Citations (Scopus)

Abstract

Collisions between Heavy Goods Vehicle and Vulnerable Road Users such as cyclists or pedestrians often result in severe injuries of the weaker road users. Blind Spot Mirrors and advanced Blind Spot Detection systems assist in avoiding collisions. Blind Spot Mirrors are however only useful if the drivers are trained to use them. The paper describes the development of a monitoring solution for assist in truck driver training. The system also can be used as a blind spot detection system to warn truck drivers. The work is performed within the DESERVE project, which aims at designing and developing a Tool Platform for embedded Advanced Driver Assistance Systems (ADAS) to exploit the benefits of cross-domain software reuse, standardised interfaces, and easy and safety-compliant integration of heterogeneous modules to cope with the expected increase of functions complexity and the impellent need of cost reduction.
Original languageEnglish
Title of host publicationIntelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages293 - 298
ISBN (Electronic)978-1-4673-8200-7, 978-1-4673-8199-4
DOIs
Publication statusPublished - 2 Nov 2015
MoE publication typeA4 Article in a conference publication
EventIEEE 11th International Conference on Intelligent Computer Communication and Processing - Cluj-Napoca, Romania
Duration: 3 Sep 20155 Sep 2015
Conference number: 11

Conference

ConferenceIEEE 11th International Conference on Intelligent Computer Communication and Processing
Abbreviated titleICCP
CountryRomania
CityCluj-Napoca
Period3/09/155/09/15

Fingerprint

Truck drivers
Mirrors
Driver training
Advanced driver assistance systems
Computer software reusability
Cost reduction
Interfaces (computer)
Monitoring

Keywords

  • collision avoidance
  • driver information systems
  • injuries
  • object detection
  • pedestrians
  • road accidents
  • road safety
  • software reusability

Cite this

Pyykönen, P., Virtanen, A., & Kyytinen, A. (2015). Developing intelligent blind spot detection system for heavy goods vehicles. In Intelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on (pp. 293 - 298). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/ICCP.2015.7312674
Pyykönen, Pasi ; Virtanen, Ari ; Kyytinen, Arto. / Developing intelligent blind spot detection system for heavy goods vehicles. Intelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on. Institute of Electrical and Electronic Engineers IEEE, 2015. pp. 293 - 298
@inproceedings{3d99ce7e262b40eba797ba5aa716c088,
title = "Developing intelligent blind spot detection system for heavy goods vehicles",
abstract = "Collisions between Heavy Goods Vehicle and Vulnerable Road Users such as cyclists or pedestrians often result in severe injuries of the weaker road users. Blind Spot Mirrors and advanced Blind Spot Detection systems assist in avoiding collisions. Blind Spot Mirrors are however only useful if the drivers are trained to use them. The paper describes the development of a monitoring solution for assist in truck driver training. The system also can be used as a blind spot detection system to warn truck drivers. The work is performed within the DESERVE project, which aims at designing and developing a Tool Platform for embedded Advanced Driver Assistance Systems (ADAS) to exploit the benefits of cross-domain software reuse, standardised interfaces, and easy and safety-compliant integration of heterogeneous modules to cope with the expected increase of functions complexity and the impellent need of cost reduction.",
keywords = "collision avoidance, driver information systems, injuries, object detection, pedestrians, road accidents, road safety, software reusability",
author = "Pasi Pyyk{\"o}nen and Ari Virtanen and Arto Kyytinen",
note = "SDA: SHP: TransSmart",
year = "2015",
month = "11",
day = "2",
doi = "10.1109/ICCP.2015.7312674",
language = "English",
pages = "293 -- 298",
booktitle = "Intelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on",
publisher = "Institute of Electrical and Electronic Engineers IEEE",
address = "United States",

}

Pyykönen, P, Virtanen, A & Kyytinen, A 2015, Developing intelligent blind spot detection system for heavy goods vehicles. in Intelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on. Institute of Electrical and Electronic Engineers IEEE, pp. 293 - 298, IEEE 11th International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania, 3/09/15. https://doi.org/10.1109/ICCP.2015.7312674

Developing intelligent blind spot detection system for heavy goods vehicles. / Pyykönen, Pasi; Virtanen, Ari; Kyytinen, Arto.

Intelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on. Institute of Electrical and Electronic Engineers IEEE, 2015. p. 293 - 298.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

TY - GEN

T1 - Developing intelligent blind spot detection system for heavy goods vehicles

AU - Pyykönen, Pasi

AU - Virtanen, Ari

AU - Kyytinen, Arto

N1 - SDA: SHP: TransSmart

PY - 2015/11/2

Y1 - 2015/11/2

N2 - Collisions between Heavy Goods Vehicle and Vulnerable Road Users such as cyclists or pedestrians often result in severe injuries of the weaker road users. Blind Spot Mirrors and advanced Blind Spot Detection systems assist in avoiding collisions. Blind Spot Mirrors are however only useful if the drivers are trained to use them. The paper describes the development of a monitoring solution for assist in truck driver training. The system also can be used as a blind spot detection system to warn truck drivers. The work is performed within the DESERVE project, which aims at designing and developing a Tool Platform for embedded Advanced Driver Assistance Systems (ADAS) to exploit the benefits of cross-domain software reuse, standardised interfaces, and easy and safety-compliant integration of heterogeneous modules to cope with the expected increase of functions complexity and the impellent need of cost reduction.

AB - Collisions between Heavy Goods Vehicle and Vulnerable Road Users such as cyclists or pedestrians often result in severe injuries of the weaker road users. Blind Spot Mirrors and advanced Blind Spot Detection systems assist in avoiding collisions. Blind Spot Mirrors are however only useful if the drivers are trained to use them. The paper describes the development of a monitoring solution for assist in truck driver training. The system also can be used as a blind spot detection system to warn truck drivers. The work is performed within the DESERVE project, which aims at designing and developing a Tool Platform for embedded Advanced Driver Assistance Systems (ADAS) to exploit the benefits of cross-domain software reuse, standardised interfaces, and easy and safety-compliant integration of heterogeneous modules to cope with the expected increase of functions complexity and the impellent need of cost reduction.

KW - collision avoidance

KW - driver information systems

KW - injuries

KW - object detection

KW - pedestrians

KW - road accidents

KW - road safety

KW - software reusability

U2 - 10.1109/ICCP.2015.7312674

DO - 10.1109/ICCP.2015.7312674

M3 - Conference article in proceedings

SP - 293

EP - 298

BT - Intelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on

PB - Institute of Electrical and Electronic Engineers IEEE

ER -

Pyykönen P, Virtanen A, Kyytinen A. Developing intelligent blind spot detection system for heavy goods vehicles. In Intelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on. Institute of Electrical and Electronic Engineers IEEE. 2015. p. 293 - 298 https://doi.org/10.1109/ICCP.2015.7312674