Intelligent Vehicle Collision Warning System Based on a Deep Learning Approach

Lai, Yeong−Kang; Huang, Yu-Hau; Schumann, Thomas

doi:10.1109/icce-china.2018.8448556

Cited by 5 publications

(6 citation statements)

References 1 publication

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“…Lai et al [18], [19] developed an AdaBoost-based cascade classifier that used both machine learning and deep learning techniques and involved vision-based vehicle detection for a forward-facing intelligent vehicle collision warning system.…”

Section: Related Workmentioning

confidence: 99%

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Chang

Chen

2019

IEEE Access

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Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96% and that the average response time for emergencyrelated announcements is approximately 7 s. INDEX TERMS Advanced driver assistance system (ADAS), artificial intelligence over Internet of Things (AIoT), automotive, deep learning, Internet of Vehicles (IoV), head-on and single-vehicle accident detection.

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Section: Related Workmentioning

confidence: 99%

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Chang

Chen

2019

IEEE Access

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“…Face mask and roll-over [15] S.I.D.S., hypothermia/hyperthermia [15] Artifcial intelligence and image processing/camera Accuracy is not mentioned, but the results of the algorithms implemented showed successful attempts in real time with low latency A new dataset of baby images having complex sleeping postures such as baby sucking thumbnail while sleeping, only side face visible, alignment issues, etc., may need to be developed Dangerous/sharp objects around the baby and face mask [30] High fever, hypothermia, breathing troubles, and milk vomiting [30] CNN and Gaussian mixture model (G.M.M. )/camera, body temperature sensor, heartbeat sensor, and temperature and humidity sensor Te delay between events occurring and receiving the alarm signal is 3-4 seconds.…”

Section: Health Risksmentioning

confidence: 99%

“…Joshi et al [29] presented the design of a smart cradle, which reduces the sleep disturbances of the baby from 50%, i.e., of the traditional to 35%, i.e., of the proposed one. Lai et al [30] integrated the CNN with the Gaussian mixture model (G.M.M.) to detect abnormal or harmful occurrences such as asphyxia, milk vomiting, dangerous or sharp objects in the vicinity, and sleeping on the stomach.…”

Section: Mixed Issues and Scenariosmentioning

confidence: 99%

Systematic Review of Sleep Monitoring Systems for Babies: Research Issues, Current Status, and Future Challenges

Khan,

Al-Hamid,

Chong

et al. 2024

Health & Social Care in the Community

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Recently, baby healthcare monitoring systems have emerged as a key research area. However, numerous research studies have been conducted on babies and their state of either awakening or sleeping. But there is a lack of comprehensive and systematic review in this area of research. The existing research considers mostly the children’s monitoring vital health parameters, in turn, giving relaxation to the parents in caring of their babies. Still, the fundamental practical issues related to daily life are missing. As a result, the safety of babies against suffocation and falling hazards for employed parents or mothers involved in the workforce has been overlooked. Only a few studies exist that cover the age range of 0 months to 4 years, with further focus on the age range of 6 months to 4 years. In response to these gaps in previous research and existing technologies, this review work proposes a vision for a baby safety system that utilizes various sensors and technologies like machine learning and image processing to ensure a safer environment for babies.

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“…For collision caution functionalities, [16,17] suggested a blockchain network connection centered on V2X interactions. The kernel unit was utilized in the suggested technique to evaluate the assertion details linked to other units.…”

Section: Literature Reviewmentioning

confidence: 99%

Accident Alert System of Vehicle and Life Security using IoT Devices and Image Processing

Mehmood¹,

Kajla²,

Awan³

et al. 2022

JCBI

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In today’s world, higher security deployments are needed, as the expansion of the transportation system has accelerated with time. Road traffic disasters have become a widespread problem in recent years. With the tremendous increase in traffic accidents, the fatality rate among people is quickly expanding. Whenever a mishap occurs on the roadways, it becomes a devastating situation for the victims. As an output result of the proposed model in this article, critical notification regarding the scene of an accident and car number was successfully sent to the pre-programmed number after determining the accident scene. The relevant contacts that were configured and added to the system successfully received an emergency message, providing the exact geographic coordinates of the accident scene. Following the receipt of the message, an audio call with a recorded voice was made to the pre-defined number. Moreover, Global Positioning System (GPS) was used to get the coordinates from the satellite. For this purpose, Global System for Mobile Communications (GSM) was utilized to attain the (GPS) coordinates in the event of an accident. Following on, the current location of an automobile through (GPS) was transmitted to certain contact details that were pre-programmed within the application. The system also reported the severity of the accident, as well as whether a vehicle collided with another vehicle or a disaster occurred to the vehicle itself.

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Intelligent Vehicle Collision Warning System Based on a Deep Learning Approach

Cited by 5 publications

References 1 publication

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Systematic Review of Sleep Monitoring Systems for Babies: Research Issues, Current Status, and Future Challenges

Accident Alert System of Vehicle and Life Security using IoT Devices and Image Processing

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