Accident Detection and Notification System Using Android

Now a day it is seen that there are number of vehicle and accident causing due to it is increasing day by day. Many people get injured and some of them even die due to unavailability of emergency facilities. The emergency responders take much long time to reach the spot which some time fail to save the lives. So to reduce this scenario there is need to decrease the time between the accidents occurred and the emergency facility provided to them. With the help of android phone which will detect the accident through collision detection circuit and notification will be sent to the known people using GSM which can be already noted and alert to hospital system and police station with alert message along with the link of map using GPS which will address the exact place of accident. DOI: 10.17762/ijritcc2321-8169.15034

Drive in Peace

In this paper, in order to implement a computer vision-based recognition system of driving fatigue. In addition to detecting human face in different light sources and the background conditions, and tracking eyes state combined with fuzzy logic to determine whether the driver of the physiological phenomenon of fatigue from face of detection. Driving fatigue recognition has been valued highly in recent years by many scholars and used extensively in various fields, for example, driver activity tracking, driver visual attention monitoring, and in-car camera systems.In this paper, we use the Windows operating system as the development environment, and utilize PC as the hardware platform. First, the system uses a camera to obtain the frame with a human face to detect, and then uses the frame to set the appropriate skin color scope to find face. Next, we find and mark out the eyes and the lips from the selected face area. Finally, we combine the image processing of eyes features with fuzzy logic to determine the driver's fatigue level, and make the graphical man-machine interface with MiniGUI for users to operate.Along with that we are using Arduino Uno microcontroller which is connected to MQ2-smoke sensor through which we can detect smoke which appears through issue in the car system. The results of experiment show that we achieve this system on PC platform successfully

Design of Microsleep Alerting System of Pilot to Reduce Air Accidents

The pilot’s micro sleep often caused by fatigue and/or drowsiness receives increasing attention for the last few years, especially after it became evident that pilot’s micro sleep also one of the major factor causing serious aircraft accidents. The system comprises EOG, EEG and IR module. EEG measures the electrical activity of the brain called brain wave pattern through intrusive electrodes. EOG tapes the electrical potential of eyeball movements and the IR module senses the eye blink frequency.  Then all these signals are applied to a robust signal processing unit and microcontroller. When an indicating feature corresponds to the micro sleep events are detected, the warning system is activated. This envisioned micro sleep alerting system would continuously monitor the alertness of the pilot and provides immediate warning signal, when micro sleep detected with high certainty

Assessing the Human Factor in Truck Driving

Human factors assessment techniques are commonly applied to a variety of workplaces to examine the nature of operations and how key functions are controlled operationally; however, these tools appear to overlook key aspects of truck driving, particularly the driver’s relationship to the driving experience. The fundamental issue is with the ability to completely decompose truck driving and accurately document the truck drivers working environment will be problematic. Therefore, to demonstrate how a truck driver moves between each series of sub-tasks will require a purpose-built assessment tool that that is both practical and relevant to truck driving

Multi-sensor driver drowsiness monitoring

A system for driver drowsiness monitoring is proposed, using multi-sensor data acquisition and investigating two decision-making algorithms, namely a fuzzy inference system (FIS) and an artificial neural network (ANN), to predict the drowsiness level of the driver. Drowsiness indicator signals are selected allowing non-intrusive measurements. The experimental set-up of a driver-drowsiness-monitoring system is designed on the basis of the soughtafter indicator signals. These selected signals are the eye closure via pupil area measurement, gaze vector and head motion acquired by a monocular computer vision system, steering wheel angle, vehicle speed, and force applied to the steering wheel by the driver. It is believed that, by fusing these signals, driver drowsiness can be detected and drowsiness level can be predicted. For validation of this hypothesis, 30 subjects, in normal and sleep-deprived conditions, are involved in a standard highway simulation for 1.5 h, giving a data set of 30 pairs. For designing a feature space to be used in decision making, several metrics are derived using histograms and entropies of the signals. An FIS and an ANN are used for decision making on the drowsiness level. To construct the rule base of the FIS, two different methods are employed and compared in terms of performance: first, linguistic rules from experimental studies in literature and, second, mathematically extracted rules by fuzzy subtractive clustering. The drowsiness levels belonging to each session are determined by the participants before and after the experiment, and videos of their faces are assessed to obtain the ground truth output for training the systems. The FIS is able to predict correctly 98 per cent of determined drowsiness states (training set) and 89 per cent of previously unknown test set states, while the ANN has a correct classification rate of 90 per cent for the test data. No significant difference is observed between the FIS and the ANN; however, the FIS might be considered better since the rule base can be improved on the basis of new observations

Drivers' Real-Time Drowsiness Identification Using Facial Features and Automatic Vehicle Speed Control

The road crash is one of the significant problems that is of great concern in today's world. Road accidents are often caused by drivers' carelessness and negligence. The drowsy condition of the drivers, which occurs due to overwork, fatigue, and many other factors, is one of those causes. It is therefore most critical to establish systems that can detect the driver's drowsy state and provide the drivers with the appropriate warning system. In addition to the automatic speed control of the car, this system thus supports drivers in incidents by providing warnings in advance. This means that road collisions that are harmful to living lives are minimised. This is achieved by using the technique of image recognition, where driver drowsiness is observed, and using this method, simultaneous warning and speed monitoring of the vehicle is carried out