A study on development of the blind spot detection system for the IoT-based smart connected car

Kwon, Donghwoon; Park, Suwoo; Baek, Sunhee; Malaiya, Ritesh K.; Yoon, Geumchae; Ryu, Jeong-Tak

doi:10.1109/icce.2018.8326077

Cited by 35 publications

(9 citation statements)

References 5 publications

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“…In [19], the authors study the development of a blind-spot detection system for smart vehicles. The proposed method is based on Fully Connected Networks (FCN), which are said to perform better than CNN in real-time image input.…”

Section: Deep Learningmentioning

confidence: 99%

“…V2V communication together with the use of an RF method compared with an SVM and an ANN is used for accident detection in the roads [37]. A novel approach to detect objects in the blind spot of smart vehicles is used in [19], using an FCN method. The DRAM method developed in [25] can be also used for road accident detection since it can help with multiple object detection from image data.…”

Section: Accident Detection/preventionmentioning

confidence: 99%

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A Review of Machine Learning and IoT in Smart Transportation

et al. 2019

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With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

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Section: Deep Learningmentioning

confidence: 99%

Section: Accident Detection/preventionmentioning

confidence: 99%

A Review of Machine Learning and IoT in Smart Transportation

et al. 2019

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“…Kwon et al [ 65 ] developed a camera-based blind spot detection system for IoT based smart connected cars and replaced the existing radar-based blind-spot detection system.…”

Section: Cmos Image Sensor Applicationsmentioning

confidence: 99%

CMOS Image Sensors in Surveillance System Applications

Sukhavasi

Elleithy

Abuzneid

et al. 2021

Sensors

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Recent technology advances in CMOS image sensors (CIS) enable their utilization in the most demanding of surveillance fields, especially visual surveillance and intrusion detection in intelligent surveillance systems, aerial surveillance in war zones, Earth environmental surveillance by satellites in space monitoring, agricultural monitoring using wireless sensor networks and internet of things and driver assistance in automotive fields. This paper presents an overview of CMOS image sensor-based surveillance applications over the last decade by tabulating the design characteristics related to image quality such as resolution, frame rate, dynamic range, signal-to-noise ratio, and also processing technology. Different models of CMOS image sensors used in all applications have been surveyed and tabulated for every year and application.

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“…[6], as illustrated in the Figure 6. [84] presents an improved BSM system using radar and camera sensor on an IoT (Internet of Thing) based vehicle.…”

Section: ) Lane Change Assist (Lca)mentioning

confidence: 99%

A Review on Driving Control Issues for Smart Electric Vehicles

Haque

Rahman²,

Islam

et al. 2021

IEEE Access

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Smart electric vehicles (EVs) are attractive because of their clean, zero-emission, low impact on the environment whilst providing a safer and smoother riding experience. To provide the latter, driving control requires appropriate systems and algorithms to optimize smart vehicle performance, maximize vehicle stability and protection, minimize accident probability, heighten driving comfort, and optimize transportation costs. Despite advancements in these areas, the realization of optimal smart EVs still requires considerable effort. This paper reviews driving control systems and algorithms for smart EVs, including the advanced driving assistant system, implementation of sensors, vehicle dynamics, and control algorithms. The major contribution of this review is to identify promising work to assist researchers with the most advanced trends in this area for prospective regulations. INDEX TERMSSmart Electric Vehicles (EVs), Driving Control Systems (DCS), Advanced Driving Assistance System (ADAS), Algorithms.

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A study on development of the blind spot detection system for the IoT-based smart connected car

Cited by 35 publications

References 5 publications

A Review of Machine Learning and IoT in Smart Transportation

A Review of Machine Learning and IoT in Smart Transportation

CMOS Image Sensors in Surveillance System Applications

A Review on Driving Control Issues for Smart Electric Vehicles

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