Vehicle detection for forward collision warning system based on a cascade classifier using adaboost algorithm

Lai, Yeong−Kang; Chou, Yu‐Hsi; Schumann, Thomas

doi:10.1109/icce-berlin.2017.8210585

Cited by 10 publications

(4 citation statements)

References 4 publications

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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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“…We need a careful trade-off between the number of sensing devices and the number of alerting devices while maintaining a competitive price for the vehicle because high costs will impact sales for the vehicle. One approach is to increase the speed at which objects can be detected and improve the level and range of vision of the moving vehicle [ 114 , 115 , 116 ]. Such improvements will enable a faster and more appropriate response to emergency situations.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Sensor Technologies for Intelligent Transportation Systems

Ibáñez

Zeadally

Contreras-Castillo

2018

Sensors

472

200

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Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment.

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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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Vehicle detection for forward collision warning system based on a cascade classifier using adaboost algorithm

Cited by 10 publications

References 4 publications

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

Sensor Technologies for Intelligent Transportation Systems

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

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