An integrated framework for autonomous driving: object detection, lane detection, and free space detection

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In this paper, we present a deep neural network based real-time integrated framework to detect objects, lane markings, and drivable space using a monocular camera for advanced driver assistance systems. The object detection framework detects and tracks objects on the road such as cars, trucks, pedestrians, bicycles, motorcycles, and traffic signs. The lane detection framework identifies the different lane markings on the road and also distinguishes between the ego lane and adjacent lane boundaries. The free space detection framework estimates the drivable space in front of the vehicle. In our integrated framework, we propose a pipeline combining the three deep neural networks into a single framework, for object detection, lane detection, and free space detection simultaneously. The integrated framework is implemented in C++ and runs real-time on the Nvidia's Drive PX 2 platform.
Originele taal-2Engels
Titel2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4)
RedacteurenXin-She Yang, Nilanjan Dey, Amit Joshi
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's260-265
Aantal pagina's6
ISBN van elektronische versie978-1-7281-3780-3
DOI's
StatusGepubliceerd - 31 jul 2019
Evenement2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4) - London, Verenigd Koninkrijk
Duur: 30 jul 201931 jul 2019

Congres

Congres2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4)
LandVerenigd Koninkrijk
StadLondon
Periode30/07/1931/07/19

Vingerafdruk

Advanced driver assistance systems
Traffic signs
Motorcycles
Bicycles
Railroad tracks
Trucks
Railroad cars
Pipelines
Cameras
Deep neural networks
Object detection

Citeer dit

Kemsaram, N., Das, A., & Dubbelman, G. (2019). An integrated framework for autonomous driving: object detection, lane detection, and free space detection. In X-S. Yang, N. Dey, & A. Joshi (editors), 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4) (blz. 260-265). [8904020] Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WorldS4.2019.8904020
Kemsaram, Narsimlu ; Das, Anweshan ; Dubbelman, Gijs. / An integrated framework for autonomous driving : object detection, lane detection, and free space detection. 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4). redacteur / Xin-She Yang ; Nilanjan Dey ; Amit Joshi. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 260-265
@inproceedings{f87b6a0eb82e48f582529f767365f74c,
title = "An integrated framework for autonomous driving: object detection, lane detection, and free space detection",
abstract = "In this paper, we present a deep neural network based real-time integrated framework to detect objects, lane markings, and drivable space using a monocular camera for advanced driver assistance systems. The object detection framework detects and tracks objects on the road such as cars, trucks, pedestrians, bicycles, motorcycles, and traffic signs. The lane detection framework identifies the different lane markings on the road and also distinguishes between the ego lane and adjacent lane boundaries. The free space detection framework estimates the drivable space in front of the vehicle. In our integrated framework, we propose a pipeline combining the three deep neural networks into a single framework, for object detection, lane detection, and free space detection simultaneously. The integrated framework is implemented in C++ and runs real-time on the Nvidia's Drive PX 2 platform.",
keywords = "Advanced driver assistance system, Artificial intelligence, Autonomous driving, Deep neural network, Free space detection, Lane detection, Object detection",
author = "Narsimlu Kemsaram and Anweshan Das and Gijs Dubbelman",
year = "2019",
month = "7",
day = "31",
doi = "10.1109/WorldS4.2019.8904020",
language = "English",
pages = "260--265",
editor = "Xin-She Yang and Nilanjan Dey and Amit Joshi",
booktitle = "2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4)",
publisher = "Institute of Electrical and Electronics Engineers",
address = "United States",

}

Kemsaram, N, Das, A & Dubbelman, G 2019, An integrated framework for autonomous driving: object detection, lane detection, and free space detection. in X-S Yang, N Dey & A Joshi (redactie), 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4)., 8904020, Institute of Electrical and Electronics Engineers, Piscataway, blz. 260-265, 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4), London, Verenigd Koninkrijk, 30/07/19. https://doi.org/10.1109/WorldS4.2019.8904020

An integrated framework for autonomous driving : object detection, lane detection, and free space detection. / Kemsaram, Narsimlu; Das, Anweshan; Dubbelman, Gijs.

2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4). redactie / Xin-She Yang; Nilanjan Dey; Amit Joshi. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 260-265 8904020.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

TY - GEN

T1 - An integrated framework for autonomous driving

T2 - object detection, lane detection, and free space detection

AU - Kemsaram, Narsimlu

AU - Das, Anweshan

AU - Dubbelman, Gijs

PY - 2019/7/31

Y1 - 2019/7/31

N2 - In this paper, we present a deep neural network based real-time integrated framework to detect objects, lane markings, and drivable space using a monocular camera for advanced driver assistance systems. The object detection framework detects and tracks objects on the road such as cars, trucks, pedestrians, bicycles, motorcycles, and traffic signs. The lane detection framework identifies the different lane markings on the road and also distinguishes between the ego lane and adjacent lane boundaries. The free space detection framework estimates the drivable space in front of the vehicle. In our integrated framework, we propose a pipeline combining the three deep neural networks into a single framework, for object detection, lane detection, and free space detection simultaneously. The integrated framework is implemented in C++ and runs real-time on the Nvidia's Drive PX 2 platform.

AB - In this paper, we present a deep neural network based real-time integrated framework to detect objects, lane markings, and drivable space using a monocular camera for advanced driver assistance systems. The object detection framework detects and tracks objects on the road such as cars, trucks, pedestrians, bicycles, motorcycles, and traffic signs. The lane detection framework identifies the different lane markings on the road and also distinguishes between the ego lane and adjacent lane boundaries. The free space detection framework estimates the drivable space in front of the vehicle. In our integrated framework, we propose a pipeline combining the three deep neural networks into a single framework, for object detection, lane detection, and free space detection simultaneously. The integrated framework is implemented in C++ and runs real-time on the Nvidia's Drive PX 2 platform.

KW - Advanced driver assistance system

KW - Artificial intelligence

KW - Autonomous driving

KW - Deep neural network

KW - Free space detection

KW - Lane detection

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M3 - Conference contribution

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Kemsaram N, Das A, Dubbelman G. An integrated framework for autonomous driving: object detection, lane detection, and free space detection. In Yang X-S, Dey N, Joshi A, redacteurs, 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4). Piscataway: Institute of Electrical and Electronics Engineers. 2019. blz. 260-265. 8904020 https://doi.org/10.1109/WorldS4.2019.8904020