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

Preliminary on Human Driver Behavior: A Review

Drowsiness is one of the main factors causing traffic accidents. Research on drowsiness can effectively reduce the traffic accident rate. According to the existing literature, this paper divides the current measurement techniques into subjective and objective ones. Among them, invasive detection and non-invasive detection based on vehicles or drivers are the main objective detection methods.Then, this paper studies the characteristics of drowsiness, and analyzes the advantages and disadvantages of each detection method in practical application. Finally, the development of detection technology is prospected, and provides ideas for the follow-up development of fatigue driving detection technology

Drowsy Driver Detection System (DDDS)

Driver weariness is one of the key causes of road mishaps in the world. Detecting the drowsiness of the driver can be one of the surest ways of quantifying driver fatigue. In this project we have developed an archetype drowsiness detection system. This mechanism works by monitoring the eyes of the driver and sounding an alarm when he/she feels heavy eyed. The system constructed is a non-intrusive real-time perceiving system. The priority is on improving the safety of the driver. In this mechanism the eye blink of the driver is detected. If the driver?s eyes remain closed for greater than a certain period of time, the driver is deemed to be tired and an alarm is sounded. The programming for this is carried out in OpenCV using the Haar cascade library for the detection of facial features

Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

This paper provides an overview of Human Machine Interface (HMI) design and command systems in commercial or experimental operation across transport modes. It presents and comments on different HMIs from the perspective of vehicle automation equipment and simulators of different application domains. Considering the fields of cognition and automation, this investigation highlights human factors and the experiences of different industries according to industrial and literature reviews. Moreover, to better focus the objectives and extend the investigated industrial panorama, the analysis covers the most effective simulators in operation across various transport modes for the training of operators as well as research in the fields of safety and ergonomics. Special focus is given to new technologies that are potentially applicable in future train cabins, e.g., visual displays and haptic-shared controls. Finally, a synthesis of human factors and their limits regarding support for monitoring or driving assistance is propose

DRIVER FATIGUE ACCIDENT PREVENTION USING EYE BLINK SENSOR

This paper proposes a way to detect the level of fatigue among drivers through detection of eye blinks. Fatigue or tiredness is one of the main factors that cause road accidents. Drivers who are not fit to drive a car due to fatigue may lose focus and make poor judgments while driving and consequently cause road accidents. By detecting parameters such as the duration of eye closure and reopening during blinks, drivers will realize whether they are in a fatigue condition or not. This project is done by performing data collection through previous researches. The algorithms will be modified and implemented to validate the system. A camera will be used to locate the face and narrow the scope further down to the eye region. After extracting the eye region, the algorithm will be used to detect the duration of each eye blinks in order to detect fatigue

Drivers’ drowsiness detection based on an optimized random forest classification and single-channel electroencephalogram

The state of functioning (posture) of a driver at the wheel of a car involves a complex set of psychological, physiological, and physical parameters. This combination induces fatigue, which manifests itself in repeated yawning, stinging eyes, a frozen gaze, a stiff and painful neck, back pain, and other signs. The driver may fight fatigue for a few moments, but it inevitably leads to drowsiness, periods of micro-sleep, and then falling asleep. At the first signs of drowsiness, the risk of an accident becomes immense. In Morocco, drowsiness at the wheel is the cause of 1/3 of fatal accidents on the freeways. Thus, in this paper, a new hybrid data analysis and an efficient machine learning algorithm are designed to detect the drowsiness of drivers who spend most of their time behind the wheel over long distances (older than 35 years). This analysis is based on a single channel of electroencephalogram (EEG) recordings using time, frequency fast Fourier transform (FFT), and power spectral density (PSD) analysis. To distinguish between the two states of alertness and drowsiness, several features were extracted from each domain (time, FFT, and PSD), and subjected to different classifier architectures to conduct a general comparison and achieve the highest detection accuracy (98.5%) and best time consumption (13 milliseconds)

HW/SW Co-design and Prototyping Approach for Embedded Smart Camera: ADAS Case Study

In 1968, Volkswagen integrated an electronic circuit as a new control fuel injection system, called the “Little Black Box”, it is considered as the first embedded system in the automotive industry. Currently, automobile constructors integrate several embedded systems into any of their new model vehicles. Behind these automobile’s electronics systems, a sophisticated Hardware/Software (HW/SW) architecture, which is based on heterogeneous components, and multiple CPUs is built. At present, they are more oriented toward visionbased systems using tiny embedded smart camera. This visionbased system in real time aspects represents one of the most challenging issues, especially in the domain of automobile’s applications. On the design side, one of the optimal solutions adopted by embedded systems designer for system performance, is to associate CPUs and hardware accelerators in the same design, in order to reduce the computational burden on the CPU and to speed-up the data processing. In this paper, we present a hardware platform-based design approach for fast embedded smart Advanced Driver Assistant System (ADAS) design and prototyping, as an alternative for the pure time-consuming simulation technique. Based on a Multi-CPU/FPGA platform, we introduced a new methodology/flow to design the different HW and SW parts of the ADAS system. Then, we shared our experience in designing and prototyping a HW/SW vision based on smart embedded system as an ADAS that helps to increase the safety of car’s drivers. We presented a real HW/SW prototype of the vision ADAS based on a Zynq FPGA. The system detects the fatigue/drowsiness state of the driver by monitoring the eyes closure and generates a real time alert. A new HW Skin Segmentation step to locate the eyes/face is proposed. Our new approach migrates the skin segmentation step from processing system (SW) to programmable logic (HW) taking the advantage of High-Level Synthesis (HLS) tool flow to accelerate the implementation, and the prototyping of the Vision based ADAS on a hardware platform

Eye-tracking assistive technologies for individuals with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research