A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)

Driver monitoring system based on eye tracking

Dissertação de mestrado integrado em Engenharia Electrónica Industrial e ComputadoresRecent statistics indicate that driver drowsiness is one of the major causes of road accidents and deaths behind the wheel. This reveals the need of reliable systems capable of predict when drivers are in this state and warn them in order to avoid crashes with other vehicles or stationary objects. Therefore, the purpose of this dissertation is to develop a driver’s monitoring system based on eye tracking that will be able to detect driver’s drowsiness level and actuate accordingly. The alert to the driver may vary from a message on the cluster to a vibration on the seat. The proposed algorithm to estimate driver’s state only requires one variable: eyelid opening. Through this variable the algorithm computes several eye parameters used to decide if the driver is drowsy or not, namely: PERCLOS, blink frequency and blink duration. Eyelid opening is obtained over a software and hardware platform called SmartEye Pro. This eye tracking system uses infrared cameras and computer vision software to gather eye’s state information. Additionally, since this dissertation is part of the project "INNOVATIVE CAR HMI", from Bosch and University of Minho partnership, the driver monitoring system will be integrated in the Bosch DSM (Driver Simulator Mockup).Estatísticas recentes indicam que a sonolência do condutor é uma das principais causas de acidentes e mortes nas estradas. Isto revela a necessidade de sistemas fiáveis capazes de prever quando um condutor está sonolento e avisá-lo, de modo a evitar colisões com outros veículos ou objetos estacionários. Portanto, o propósito desta dissertação é desenvolver um sistema de monitorização do condutor baseado em eye tracking que será capaz de detetar o nível de sonolência do condutor e atuar em conformidade. O alerta para o condutor pode variar entre uma mensagem no painel de instrumentos ou uma vibração no assento. O algoritmo proposto para estimar o estado do condutor apenas requer a aquisição de uma variável: abertura da pálpebra. Através desta variável, o algoritmo computa alguns parâmetros utilizados para verificar se o condutor está sonolento ou não, nomeadamente: PERCLOS, frequência do pestanejar e duração do pestanejar. A abertura da pálpebra é obtida através de uma plataforma de hardware e software chamada SmartEye Pro. Esta plataforma de eye tracking utiliza câmaras infravermelho e software de visão por computador para obter informação sobre o estado dos olhos. Adicionalmente, uma vez que esta dissertação está inserida projeto: "INNOVATIVE CAR HMI", da parceria entre a Bosch e a Universidade do Minho, o sistema desenvolvido será futuramente integrado no Bosch DSM (Driver Simulator Mockup)

IMPROVED PSO BASED DRIVER’S DROWSINESS DETECTION USING FUZZY CLASSIFIER

In this drowsiness detection framework two actions including brain and visual features are utilised to distinguish the various levels of drowsiness. These actions are provided by the EEG and EOG signal brain actions. From the EEG and EOG signals the peculiarities like mean, peak, pitch, maximum, minimum, standard deviation are assessed . In these peculiarities we decide on some best attributes - peak and pitch employing an IPSO strategy that picks up the best threshold esteem. These signals are then offered into the STFT which is employed to discover the signal length, producing a STFT network from the intermittent hamming window,the output of which are energy signals alpha and beta. These energy signals are offered into the MCT to get an alpha mean and a beta mean -the most chosen and outstanding attributes. These are then subjected to fuzzy based classification to give a precise result checking over the maximum values in the alpha and the beta series . &nbsp

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-97% on an average across all subjects. © 2011 IEEE

EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features

Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for Electroencephalogram (EEG) based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291