相关论文: In-the-wild Drowsiness Detection from Facial Expre…

Driver drowsiness detection using videos/images is one of the most essential areas in today's time for driver safety. The development of deep learning techniques, notably Convolutional Neural Networks (CNN), applied in computer vision…

Significant losses in terms of life and property occur from road traffic accidents, which are often caused by drunk and drowsy drivers. Reducing accidents requires effective detection of alcohol impairment and drowsiness as well as…

A 20% rise in car crashes in 2021 compared to 2020 has been observed as a result of increased distraction and drowsiness. Drowsy and distracted driving are the cause of 45% of all car crashes. As a means to decrease drowsy and distracted…

Driver drowsiness detection (DDD) prevents road accidents caused by driver fatigue. Vehicle dynamics-based DDD has been proposed as a method that is both economical and high performance. However, there are concerns about the reliability of…

Robust driver attention prediction for critical situations is a challenging computer vision problem, yet essential for autonomous driving. Because critical driving moments are so rare, collecting enough data for these situations is…

The alertness level of drivers can be estimated with the use of computer vision based methods. The level of fatigue can be found from the value of PERCLOS. It is the ratio of closed eye frames to the total frames processed. The main…

Driving support systems, such as car navigation systems are becoming common and they support driver in several aspects. Non-intrusive method of detecting Fatigue and drowsiness based on eye-blink count and eye directed instruction…

Accurately detecting drowsiness is vital to driving safety. Among all measures, physiological-signal-based drowsiness monitoring can be more privacy-preserving than a camera-based approach. However, conflicts exist regarding how…

Driver monitoring systems (DMS) are a key component of vehicular safety and essential for the transition from semiautonomous to fully autonomous driving. A key task for DMS is to ascertain the cognitive state of a driver and to determine…

Visual Saliency refers to the innate human mechanism of focusing on and extracting important features from the observed environment. Recently, there has been a notable surge of interest in the field of automotive research regarding the…

Driver distractions are known to be the dominant cause of road accidents. While monitoring systems can detect non-driving-related activities and facilitate reducing the risks, they must be accurate and efficient to be applicable.…

Road maintenance during the Winter season is a safety critical and resource demanding operation. One of its key activities is determining road surface condition (RSC) in order to prioritize roads and allocate cleaning efforts such as…

In this paper, we try to analyze drowsiness which is a major factor in many traffic accidents due to the clear decline in the attention and recognition of danger drivers. The object of this work is to develop an automatic method to evaluate…

Detecting unsafe driving states, such as stress, drowsiness, and fatigue, is an important component of ensuring driving safety and an essential prerequisite for automatic intervention systems in vehicles. These concerning conditions are…

Since the number of cars has grown rapidly in recent years, driving safety draws more and more public attention. Drowsy driving is one of the biggest threatens to driving safety. Therefore, a simple but robust system that can detect drowsy…

Convolutional Neural Networks (CNNs) have been used successfully across a broad range of areas including data mining, object detection, and in business. The dominance of CNNs follows a breakthrough by Alex Krizhevsky which showed…

Reliable stress recognition is critical in applications such as medical monitoring and safety-critical systems, including real-world driving. While stress is commonly detected using physiological signals such as perinasal perspiration and…

Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently large and comprehensive datasets is currently a…

- Background / Introduction: Driver drowsiness is a significant concern and one of the leading causes of traffic accidents. Advances in cognitive neuroscience and computer science have enabled the detection of drivers' drowsiness using…

State-of-the-art convolutional neural networks excel in machine learning tasks such as face recognition, and object classification but suffer significantly when adversarial attacks are present. It is crucial that machine critical systems,…