Intelligent system for vehicular accident detection and notification

Anil, Bankar Sanket; Vilas, Kale Aniket; Jagtap, Satyawan R.

doi:10.1109/iccsp.2014.6950048

Cited by 26 publications

(8 citation statements)

References 3 publications

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“…The major drawback of this approach is that there is a possibility of false alarm if the driver drops the helmet by mistake , additionally, more storage and computational capabilities are needed because the system needs to communicate with the cloud services. B.S.Anil et al [58] presented a solution for collision detection. The system detects the accident using flex and accelerometer sensors and the location of accident is communicated via GSM modem to the emergency services.…”

Section: D: Using Accelerometer Sensorsmentioning

confidence: 99%

A Comprehensive Study on IoT Based Accident Detection Systems for Smart Vehicles

et al. 2020

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With population growth, the demand for vehicles has increased tremendously, which has created an alarming situation in terms of traffic hazards and road accidents. The road accidents percentage is growing exponentially and so are the fatalities caused due to accidents. However, the primary cause of the increased rate of fatalities is due to the delay in emergency services. Many lives could be saved with efficient rescue services. The delay happens due to traffic congestion or unstable communication to the medical units. The implementation of automatic road accident detection systems to provide timely aid is crucial. Many solutions have been proposed in the literature for automatic accident detection .The techniques include crash prediction using smartphones, vehicular ad-hoc networks, GPS/GSM based systems, and various machine learning techniques. With such high rates of deaths associated with road accidents, road safety is the most critical sector that demands significant exploration. In this paper, we present a critical analysis of various existing methodologies used for predicting and preventing road accidents, highlighting their strengths, limitations, and challenges that need to be addressed to ensure road safety and save valuable lives.

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Section: D: Using Accelerometer Sensorsmentioning

confidence: 99%

A Comprehensive Study on IoT Based Accident Detection Systems for Smart Vehicles

et al. 2020

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“…If any stress is applied, then the Wheatstone bridge is unbalanced and gives an output signal which is measured through the stress value. [21] . GSM is used to reduce and digitize the data which is sent to the other network using the two different streams of a channel at the particular time slot.…”

Section: Load Cell Sensormentioning

confidence: 99%

SANJYOT – WE SAVE LIFE Using Big Data - Apache Spark

Tyagi¹,

Chauhan²,

Sighal³

et al. 2020

EAI Endorsed Transactions on Cloud Systems

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With its high importance in the Indian economy, roadways are one of the major forms of transportation in India that cannot be avoided. There is a public safety problem with roadways and a significant number of injuries in our minds. Due to lack of proper real-time monitoring of the accident details that could be transferred to the nearest police station, hospitals, the percentage of incidents in the country is rising to such a high level that leads to loss of human life. Paper helps to provide a solution to this issue by providing information on the location of accident and the severity of the victim during the accident, which will be instantly shared with the police station, hospitals with details such as images, location, video and impact of the accident for quicker decision making, will improve the chances of saving the lives of the sufferers. It is a system which is fully automated. Therefore, protecting the lives of individuals will be a huge contribution to humanity.

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“…This application gives the detection of driving safety and automatically takes emergency measures when detecting the accident. Similarly, Anil et al [11] also proposed a project, aiming to work as an intelligent detection and warning system when the vehicular accidents happen. Martinelli et al [12] designed and developed a context-aware mobile application consisting of a secondary system of a driver situation analysis and alarm information to prevent traffic accidents.…”

Section: Related Workmentioning

confidence: 99%

Context Awareness-Based Accident Prevention During Mobile Phone Use

Sun

Guo³

et al. 2020

IEEE Access

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In recent years, with the continuous development of technologies, people's dependence on smartphones is growing increasingly. Excessive dependence has been causing a considerable number of people to face potential accidents and injuries on account of the use of mobile phones. Based on the above considerations, this paper defines the dangerous context in mathematical forms, using the data collected by sensors in phones and context-aware technology to study the accident prevention methods in the process of using mobile phones. The research involves three parts. Human behavior awareness subproblem refers to the type of behavior currently being taken by the mobile phone holder, which is different from the traditional human behavior recognition problem. The spatial position awareness subproblem refers to the perception of whether the phone holder is in a dangerous area prone to accidental injury. The interactive awareness subproblem refers to how the mobile phone holder interacts with the mobile phone to determine whether the mobile phone holder is in the process of using the mobile phone. The study of accident prevention methods during the use of mobile phones will be based on the output of the above subproblems. After designing and implementing the solutions toward the three subproblems, algorithms are integrated, and the application is designed. INDEX TERMS Dangerous context, context awareness, mobile phone use, accident prevention.

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Intelligent system for vehicular accident detection and notification

Cited by 26 publications

References 3 publications

A Comprehensive Study on IoT Based Accident Detection Systems for Smart Vehicles

A Comprehensive Study on IoT Based Accident Detection Systems for Smart Vehicles

SANJYOT – WE SAVE LIFE Using Big Data - Apache Spark

Context Awareness-Based Accident Prevention During Mobile Phone Use

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