Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue Detection

This work was supported by National Natural Science Foundation of China under Grant Nos. 61473203, 61873181 and the Natural Science Foundation of Tianjin, China under Grant No. 16JCYBJC18200.Peer reviewedPostprin

Sitting behaviour-based pattern recognition for predicting driver fatigue

The proposed approach based on physiological characteristics of sitting behaviours and sophisticated machine learning techniques would enable an effective and practical solution to driver fatigue prognosis since it is insensitive to the illumination of driving environment, non-obtrusive to driver, without violating driver&rsquo;s privacy, more acceptable by drivers

Real-time performance modelling of a sustained attention to response task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participant’s reaction times during a monotonous task. A lab-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Then relevant parameters are used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models are compared to detect in real-time – minute by minute - the lapses in vigilance during the task. We show that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables to detect vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared to Neural Networks and Generalised Linear Mixed Models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks

Drowsiness Detection for Driver Assistance

This thesis presents a noninvasive approach to detect drowsiness of drivers using behavioral and vehicle based measuring techniques. The system accepts stream of driver's images from a camera and steering wheel movement from G-27 Logitech racing wheel system. It first describes a standalone implementation of the behavioral based drowsiness detection method. The method accepts the input images and analyzes the facial expressions of the driver through sets of processing stages. In order to improve the reliability of the system, we also proposed a comprehensive approach of combining the facial expression analysis with a steering wheel data analysis in decision level as well as feature level integration. We also presented a new approach of modeling the temporal information of facial expressions of drowsiness using HMM. Each proposed approach has been implemented in a simulated driving setup. The detection performance of each method is evaluated through experiments and its parameter settings were optimized. Finally we present a case study which discusses the practicality of our system in a small-scaled intelligent transportation system where it switches the driving mechanism between manual and autonomous control depending on the state of the driver.Electrical Engineerin

Multi-modal Human Fatigue Classification using Wearable Sensors for Human-Robot Teams

Our main objective of this study is to create a fatigue detection model using real-time data by using wearable sensors. The purpose of this research is to learn more about the way humans experience fatigue in a supervisory human-machine environment. The goal of this study is to evaluate machine learning algorithms that assess fatigue detection and to use robots for adapting its interactions. The environment itself consists of two different tasks to analyze Physical fatigue and Mental fatigue in two different task environments that are (i) Jigsaw puzzle-solving task, and (ii) Pick and Place task. Physical fatigue and mental fatigue are detected using wearable sensors: MYO armband and BioPac Bioharness. During the experiment, the Physiological metrics used are Heart rate, respiration rate, Heart rate variability, posture, breathing wave amplitude, and EMG. All these Physiological signals are collected simultaneously in a real-time task environment. The data collected by these physiological signals are then processed and machine learning and deep learning algorithms are used for further process in building a fatigue detection model

Multi-Attention Fusion Drowsy Driving Detection Model

Drowsy driving represents a major contributor to traffic accidents, and the implementation of driver drowsy driving detection systems has been proven to significantly reduce the occurrence of such accidents. Despite the development of numerous drowsy driving detection algorithms, many of them impose specific prerequisites such as the availability of complete facial images, optimal lighting conditions, and the use of RGB images. In our study, we introduce a novel approach called the Multi-Attention Fusion Drowsy Driving Detection Model (MAF). MAF is aimed at significantly enhancing classification performance, especially in scenarios involving partial facial occlusion and low lighting conditions. It accomplishes this by capitalizing on the local feature extraction capabilities provided by multi-attention fusion, thereby enhancing the algorithm's overall robustness. To enhance our dataset, we collected real-world data that includes both occluded and unoccluded faces captured under nighttime and daytime lighting conditions. We conducted a comprehensive series of experiments using both publicly available datasets and our self-built data. The results of these experiments demonstrate that our proposed model achieves an impressive driver drowsiness detection accuracy of 96.8%.Comment: 8 pages, 6 figure