ViLDAR—Visible Light Sensing-Based Speed Estimation Using Vehicle Headlamps

Abuella, Hisham; Miramirkhani, Farshad; Ekin, Sabit; Uysal, Murat; Ahmed, Samir

doi:10.1109/tvt.2019.2941705

Cited by 33 publications

(24 citation statements)

References 34 publications

(43 reference statements)

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“…In [186], Demir et al proposed a dynamic soft handover technique based on coordinated multipoint transmission. A vehicle speed estimation system based on sensing the headlamp's VL variation is tested in [187]. For path-control purposes, vehicles can keep track of the distance between them via exchanging a clock signal contained in Manchester-encoded signals [188].…”

Section: B the Owc-iott In Smart Citiesmentioning

confidence: 99%

A Top-Down Survey on Optical Wireless Communications for the Internet of Things

Çelik¹,

Romdhane²,

Kaddoum³

et al. 2022

Preprint

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The Internet of Things (IoT) is a transformative technology marking the beginning of a new era where physical and digital worlds are integrated by connecting a plethora of uniquely identifiable smart objects. Although the Internet of terrestrial things (IoTT) has been at the center of our IoT perception, it has been recently extended to different environments, such as the Internet of underWater things (IoWT), the Internet of Biomedical things (IoBT), and Internet of underGround things (IoGT). Even though radio frequency (RF) based wireless networks are regarded as the default means of connectivity, they are not always the best option due to the limited spectrum, interference limitations caused by the ever-increasing number of devices, and severe propagation loss in transmission mediums other than air. As a remedy, optical wireless communication (OWC) technologies can complement, replace, or co-exist with audio and radio wave-based wireless systems to improve overall network performance. To this aim, this paper reveals the full potential of OWC-based IoT networks by providing a top-down survey of four main IoT domains: IoTT, IoBT, and IoGT. Each domain is covered by a dedicated and self-contained section that starts with a comparative analysis, explains how OWC can be hybridized with existing wireless technologies, points out potential OWC applications fitting best the related IoT domain, and discusses open communication and networking research problems. More importantly, instead of presenting a visionary OWC-IoT framework, the survey discloses that OWC-IoT has become a reality by emphasizing ongoing proof-of-concept prototyping efforts and available commercial off-the-shelf (COTS) OWC-IoT products.

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Section: B the Owc-iott In Smart Citiesmentioning

confidence: 99%

A Top-Down Survey on Optical Wireless Communications for the Internet of Things

Çelik¹,

Romdhane²,

Kaddoum³

et al. 2022

Preprint

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“…One important step is the optimal selection of vehicle sensors in terms of cost, range parameters and reliability [18]. The most common automotive sensing technologies are Radio Detection and Ranging (RADAR) and Light Detection and Ranging (LiDAR) [1,16,18], which use radio frequency or laser signals, respectively. RADAR sensors are more robust to weather conditions, but have lower accuracy being exposed to interference issues of the dynamic and…”

Section: Introductionmentioning

confidence: 99%

Collision Avoidance Using Deep Learning-Based Monocular Vision

Rill

Faragó

2021

SN COMPUT. SCI.

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Autonomous driving technologies, including monocular vision-based approaches, are in the forefront of industrial and research communities, since they are expected to have a significant impact on economy and society. However, they have limitations in terms of crash avoidance because of the rarity of labeled data for collisions in everyday traffic, as well as due to the complexity of driving situations. In this work, we propose a simple method based solely on monocular vision to overcome the data scarcity problem and to promote forward collision avoidance systems. We exploit state-of-the-art deep learning-based optical flow and monocular depth estimation methods, as well as object detection to estimate the speed of the ego-vehicle and to identify the lead vehicle, respectively. The proposed method utilizes car stop situations as collision surrogates to obtain data for time to collision estimation. We evaluate this approach on our own driving videos, collected using a spherical camera and smart glasses. Our results indicate that similar accuracy can be achieved on both video sources: the external road view from the car’s, and the ego-centric view from the driver’s perspective. Additionally, we set forth the possibility of using spherical cameras as opposed to traditional cameras for vision-based automotive sensing.

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“…VVLC maintains connectivity using vehicles' headlight and taillight [2]. These optical devices are directional with an asymmetrical radiation pattern [3][4][5][6][7]. Although the directional nature of optical devices limits the interference among other light sources even in the presence of a high density of vehicles [2], it makes the VVLC link geo-spatial dependent.…”

Section: Introductionmentioning

confidence: 99%

“…However, LED lamps are better suited for VVLC as they can support high-speed communication. A comparison between the path loss obtained by the Lambertian model and a simulated model of the asymmetric radiation pattern was provided in [5]. The study simulated the radiation pattern of low-beam headlights (Philips Luxeon Rebel white LED) using a non-sequential raytracing software tool.…”

Section: Introductionmentioning

confidence: 99%