"DIFFERENTIATING SAFE DRIVING FROM UNSAFE DRIVING USING MOBILE PHONE SENSORS". A RESEARCH PAPER (original) (raw)
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Modern Applied Science, 2014
Traffic safety and energy efficiency of vehicles are strictly related to driver's behavior. The scientific literature has investigated on some specific dynamic parameters that, among the others, can be used as a measure of unsafe or aggressive driving style such as longitudinal and lateral acceleration of vehicle. Moreover, the use of modern mobile devices (smartphones and tablets), and their internal sensors (GPS receivers, three-axes accelerometers), allows road users to receive real time information and feedback that can be useful to increase awareness of drivers and promote safety. This paper focuses on the development of a prototype mobile application that can evaluate the grade of safety that drivers are keeping on the road by measuring of accelerations (longitudinal and lateral) and warning for users when it can be convenient to correct their driving style. The aggressiveness is evaluated by plotting vehicle's acceleration on a g-g diagram specially studied and designed, where horizontal and lateral acceleration is displayed inside areas of "Good Driving Style". Several experimental tests were carried out with different drivers and cars in order to estimate the system accuracy and the usability of the application. This work is part of the wider research project M2M, Mobile to Mobility: Information and communication technology systems for road traffic safety (PON National Operational Program for Research and Competitiveness 2007-2013) which is based on the use of mobile sensor computing systems for giving real-time information in order to reduce risks and to make the transportation system more safe and comfortable.
Analyzing Drivers behavior for Rash driving & Overspeeding using Smartphone: A Survey
2020
Considering the increasing number of vehicle accidents due to overspeeding and Rash driving over the globe, We hear by carried out Survey of different problem in case of accidents and work on the solution which helps the parents in case of accidents. In today’s life, everyone is in hurry to reach their destination as fast as possible. Various sensors are used to monitor drivers behavior, In literature survey, we studies various papers to observe Drivers pattern. This paper also provides some research directions which various researches can explore. A program installed on the mobile automatically calculates accelerations supported detector readings, and compares them with threshold set in the application. Once any proof of rash driving is determined, the mobile will automatically alert the driver or sends a message to given number in application for help well before accident actually happens IndexTerms–Three axis accelerometer, Gyroscope, GPS.
IJERT-Safe driving using mobile phones
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/safe-driving-using-mobile-phones https://www.ijert.org/research/safe-driving-using-mobile-phones-IJERTV2IS80304.pdf As vehicle manufacturers continue to increase their emphasis on safety with Safe Driving Using Mobile Phones (SDMP's), we propose a device that is not only already in abundance but portable enough as well to be one of the most effective multipurpose devices that are able to analyze and advise on safety conditions. Mobile smart phones today are equipped with numerous sensors that can help to aid in safety enhancements for drivers on the road. In this paper, we use the three-axis accelerometer of an Android-based Smartphone to record and analyze various driver behaviors and external road conditions that could potentially be hazardous to the health of the driver, the neighboring public, and the automobile. Effective use of these data can educate a potentially dangerous driver on how to safely and efficiently operate a vehicle. With real-time analysis and auditory alerts of these factors, we can increase a driver's overall awareness to maximize safety.
Mobile phone based drunk driving detection
2010
Drunk driving, or officially Driving Under the Influence (DUI) of alcohol, is a major cause of traffic accidents throughout the world. In this paper, we propose a highly efficient system aimed at early detection and alert of dangerous vehicle maneuvers typically related to drunk driving. The entire solution requires only a mobile phone placed in vehicle and with accelerometer and orientation sensor. A program installed on the mobile phone computes accelerations based on sensor readings, and compares them with typical drunk driving patterns extracted from real driving tests. Once any evidence of drunk driving is present, the mobile phone will automatically alert the driver or call the police for help well before accident actually happens. We implement the detection system on Android G1 phone and have it tested with different kinds of driving behaviors. The results show that the system achieves high accuracy and energy efficiency.
Development of a Real Time Supported Program for Motorbike Drivers Using Smartphone Built-in Sensors
International Journal of Engineering and Technology
Using mobile phones during traffic progress is one of the main causes traffic accidents because drivers do not focus on driving, they try to listen phone calls or to text some messages... Most of research work has focused to car driving. However, using motorbike is very popular in some developing countries such as Vietnam, India, etc. Up to now, there are just a few works, which focus to motorbike driving with obvious limitations. Thus, in this research, we proposed a complete solution for bikers who own a smartphone. Our work exploits the information from built-in sensors in Android smartphone. A complete scheme for motorbike driving is proposed. In this scheme, the user state is detected by improving the current Google activity recognition API. If the state is "On vehicle", the phone automatically switches to silent mode and send to the caller an SMS. Our work provides a mechanism to receive the calls from VIP contacts and urgent calls. The phone would switch back to the normal mode if the state is not "On vehicle". Furthermore, it sends the accident location to the relatives when an accident occurs to save their lives automatically. The application was tested carefully and it can be used to protect the lives of motorbike drivers. Keyword-3-DOF Accelerometers, GPS, Accidental Location, Motor Safe I. INTRODUCTION According to the Traffic Police Department, Vietnam has more than 45 million motorbikes in over 90 million people until 2016 with 21.568 traffic accidents occurred in this year and the number of people died and injured were 8.680 and 19.280 respectively (see Fig.1). One of the main causes in traffic accidents is using the phone while controlling vehicles (see Fig. 2). They usually use the phone for listening to music, calling, messaging or playing some app games like Pokemon Go...[3][4]. Hence, they lose focus on controlling vehicles because of being limited visibility and being distracted from other drivers... Then they cannot handle all situations. Furthermore, the time between the occurrence of accidents and the notification to relatives and medical services is too slow; it results in increasing the number of fatal cases [5]. In order to reduce the number of traffic accidents and people died as well, there are several published methods used to protect drivers during the controlling vehicles process in recent years by researchers and companies. Nevertheless, most of the reported publications focus on developing the supported systems for cars such as: Accelerometer based Transportation System [6], Accident Avoidance and Driver Assist Technologies [7], PRE-SAFE® [8], etc. The systems in [11][12][13] used smartphones to detect the accident in combination with automatic sending alert notification to relatives and hospital services, but these applications do not have function to switch the phone to silent mode when receiving incoming calls from unimportant or unknown people while driving. Therefore, drivers are easily distracted while driving.
Smart Phone based Rash Driving Detection
2017
Considering the increase in number of vehicle accidents over the globe, we hereby carried out survey of different problems due to which vehicle accidents are increasing rapidly. One such typical problem was drunk driving or driving under the influence of alcohol. Drunk driving, or officially driving under the Influence (DUI) of alcohol, is a major cause of traffic accidents throughout the world. Real-time abnormal driving behaviour monitoring is a cornerstone to improving driving safety. To improve driver's awareness of their driving habits so as to prevent potential car accidents, we need to consider a real-time monitoring approach, which detects abnormal driving behaviours. We propose a model for Driving Behaviour Detection and identification system to perform real-time highly-accurate abnormal driving behaviour monitoring using smartphone sensors.
Accident Prevention Alert System for Mobile Devices.
International Journal of Engineering Sciences & Research Technology, 2013
Availability and accessibility to high-speed vehicles is very common. In this fast paced world, while teenagers own such vehicles to have fun & excitement, others use it to reach their destination faster. This sometimes results in dangerous driving and causes major accidents. To avoid this, a lot of research and developments made in this field and hence a lot of safety devices are implemented on modern vehicles. Some of these include the electronics stability program (ESP), secure tyre system(STS), anti-lock braking system(ABS), airbags and seat belts etc. But these are very expensive which comes with a cost. This hinders common access/usage to such safety systems. Vehicles manufactured with these sensors are hard to find in lower priced economical vehicles. Also there is lack of portability of such systems, to owner having more than one vehicle need to have such costly system separately installed in each vehicle. So in this paper, we target mobile devices (not only smartphone)as an alternative device for ADASs that makes use of available sensors and can alert the driver while negligence driving.The proof of concept can done by developing an application in a mobile device that makes use of available following sensors, collect the data and analyze it to conclude on any danger and alert the driver.
Mobile Phone Detection and Notification for The Prevention of Car Accidents
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The use of mobile devices can easily divert a driver's attention away from the road. Dangerous driving, such as texting and driving, can cause havoc in traffic and jeopardize safety. The goal of this project is to develop an accident-avoidance system that can detect the presence of a mobile phone in the driver's hand by installing an in-car camera facing the driver and running a YOLOv3-Tiny algorithm for mobile phone detection. In addition, the model will issue an audio alert to the driver and use face detection algorithms to determine the driver's identity. Twilio APIs are being used to send live messages to car owners about the actions taken using the car's location information.
Rash driving, or officially driving under the Influence of alcohol, is a major cause of traffic accidents throughout the world. In this paper, we propose a highly efficient system aimed at early detection and alert of dangerous vehicle maneuvers typically related to drunk driving. The entire solution requires only a mobile phone placed in vehicle and with accelerometer and orientation sensor. After installing a program on the mobile phone, it will compute accelerations based on sensor readings and compare them with typical unsafe driving patterns extracted from real driving tests. Once any evidence of drunk driving is present, the mobile phone will automatically alert the driver or call the police for help well before accident actually happens .
Proceedings of the Workshop Program of the 19th International Conference on Distributed Computing and Networking, 2018
Integration of the physical world with the computerized world has led to the manifestation of Cyber-Physical Systems (CPSs) in an attempt to build a better and smarter world. In this paper, such a CPS named D&RSense has been proposed to promote smart transportation in order to make travelling more comfortable and safe. By studying driving patterns of drivers, D&RSense can get valuable insights to their braking and accelerating styles which can help to give them real-time warnings when they drive aggressively. Detection of rash driving prone areas across the city can help to recommend which areas of the city need stricter surveillance. D&RSense involves smartphones of commuters and utilizes their accelerometer and GPS sensors to detect driving events like braking and acceleration as well as poor road conditions like bumps and potholes by applying the ensemble learning method for classification, Random Forest (RF). The accuracy of the same has been compared to other supervised machine learning classifiers like Naïve Bayes, k-Nearest Neighbours (k-NN), Decision Trees (DT), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). Rash-driving prone areas and poor road segments during the course of the experiment have been plotted using Density-based spatial clustering of applications with noise (DBSCAN) algorithm. Effectiveness of the proposed application has been evaluated through extensive testing.