DRIVING STRESS OF DRIVERS ON NARROWED LANE AND HARD SHOULDER OF MOTORWAYS

In order to reduce congestion on a section of motorway, a new type of hard shoulder running (HSR) scheme or a tentative 3-lane operation scheme had been in use since October 21, 2011 for the first time in Japan. It is different from those that have been applied in other countries in that it operates all day long, while the latter operates only during peak periods. As a result, traffic congestion was reduced significantly. Nevertheless, before the operation, road operator was slightly worried about the impact of narrowed widths of lanes and hard shoulder on drivers’ stress while driving. The paper evaluates the impact of narrowed lanes and hard shoulder on young and senior drivers’ stress while driving during the operation of the tentative 3-lane scheme by comparing it among normal 3-lane, 2-lane and tentative 3-lane sections.&nbsp

Multimodal Subspace Support Vector Data Description

In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. We also propose different regularization strategies for the proposed method and provide both linear and non-linear formulations. The proposed Multimodal Subspace Support Vector Data Description outperforms all the competing methods using data from a single modality or fusing data from all modalities in four out of five datasets.Comment: 26 pages manuscript (6 tables, 2 figures), 24 pages supplementary material (27 tables, 10 figures). The manuscript and supplementary material are combined as a single .pdf (50 pages) fil

Evaluation of stress loading for logging truck drivers by monitoring changes in muscle tension during a work shift

Our research aimed to quantify and evaluate the stress loading of drivers by monitoring the loading of the radial extensor muscle at the wrist joint (musculus extensor carpi radialis) when they drove different types of timber trucks. We monitored changes in the electric potential of skeletal muscles with electromyographic measurements and measurements of changes of heart rate using the Biofeedback 2000 x-pert device. The drivers were observed throughout their work shifts during normal operation of logging trucks and logging trucks with trailers. As a reference, muscle load was measured when driving a passenger car. We evaluated the normality of the measured data and obtained descriptive statistics from the individual measurements. The differences in stress load associated with driving the different types of vehicles increased whilst driving on lower-class roads. Results showed a high stress load for drivers of loaded vehicles when driving on narrow roads. It was more challenging to control a loaded logging truck with a trailer than driving a logging truck, with the difference in muscular loading reaching 22.5%. Driving a logging truck with a trailer produced 46.5% more muscle loading compared to driving a loaded passenger car. For preventive health and safety reasons, it would be reasonable to alternate between drivers when operating various vehicles, thus minimizing the development of possible health issues

Feature selection model based on EEG signals for assessing the cognitive workload in drivers

In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.This work has been funded by the Ministry of Science, Innovation and Universities of Spain under grant number TRA2016-77012-RPeer ReviewedPostprint (published version

Characterization and Identification of Distraction During Naturalistic Driving Using Wearable Non-Intrusive Physiological Measure of Galvanic Skin Responses

Fatalities due to road accidents are mainly caused by distracted driving. Driving demands continuous attention of the driver. Certain levels of distraction while driving can cause the driver to lose his/her attention which might lead to a fatal accident. Thus, early detection of distraction will help reduce the number of accidents. Several researches have been conducted for automatic detection of driver distraction. Many previous approaches have employed camera-based techniques. However, these methods might detect the distraction rather late to warn the drivers. Although neurophysiological signals using Electroencephalography (EEG) have shown to be another reliable indicator of distraction, EEG signals are very complex, and the technology is intrusive to the drivers, which creates serious doubt for its implementation. In this thesis we investigate a non-intrusive physiological measure-Galvanic Skin Responses (GSR) using a wrist band wearable and conduct an empirical characterization of driver GSR signals during a naturalistic driving experiment. The proposed method is used to evaluate and extract statistical, frequency and time domain features to identify distraction. Also, several data mining techniques such as feature selection, feature-ranking, dimensionality reduction and feature space analysis are performed to generate discriminative bases that reduce the computational complexity for efficient identification of distraction using supervised learning. A signal processing technique: continuous decomposition analysis, exclusive for skin conductance signal was investigated to better understand the behavior of raw signal during cognitive and visual over load from secondary tasks while driving. The proposed driver monitoring and identification system on the edge provided evident results using GSR as a reliable indicator of driver distraction while meeting the requirement of early notification of distraction state to driver.Master of ScienceComputer and Information Science, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/143521/1/Vikas Final Text Embedded.pdfDescription of Vikas Final Text Embedded.pdf : Thesi

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems

Development of cognitive workload models to detect driving impairment

Tesi redactada en castellàDriving a vehicle is a complex activity exposed to continuous changes such as speed limits and vehicular traffic. Drivers require a high degree of concentration when performing this activity, increasing the amount of mental demand known as cognitive workload, causing vehicular accidents to the minimum negligence. In fact, human error is the leading contributing factor in over 90% of road accidents. In recent years, the subjects' cognitive workload levels while driving a vehicle have been predicted using subjective and vehicle performance tools. Other research has emphasized the use and analysis of physiological information, where electroencephalographic (EEG) signals are the most used to identify cognitive states due to their high precision. Although significant progress has been made in this area, these investigations have been based on traditional techniques or data analysis from a specific source due to the information's complexity. A new trend has been opened in the study of the internal behavior of subjects by implementing machine learning techniques to analyze information from various sources. However, there are still several challenges to face in this new line of research. This doctoral thesis presents a new model to predict the states of low and high cognitive workload of subjects when facing scenarios of driving a vehicle called GALoRSI-SVMRBF (Genetic Algorithms and Logistic Regression for the Structuring of Information-Support Vector Machine with Radial Basis Function Kernel). GALoRSI-SVMRBF is developed using machine learning algorithms based on information from EEG signals. Also, the information collected from NASA-TLX, instant online self-assessment and the error rate measure are implemented in the model. First, GALoRSI-SVMRBF proposes a new method for pattern recognition based on feature selection that combines statistical tests, genetic algorithms, and logistic regression. This method consists mainly of selecting an EEG dataset and exploring the information to identify the key features that recognize cognitive states. The selected data are defined as an index for pattern recognition and used to structure a new dataset capable of optimizing the model's learning and classification process. Second, the methodology and development of a classifier for the prediction model are presented, implementing machine learning algorithms. The classifier is developed mainly in two phases, defined as training and testing. Once the prediction model has been developed, this thesis presents the validation phase of GALoRSI-SVMRBF. The validation consists of evaluating the model's adaptability to new datasets, maintaining a high prediction rate. Finally, an analysis of the performance of GALoRSI-SVMRBF is presented. The objective is to know the model's scope and limitations, evaluating various performance metrics to find the optimal configuration for GALoRSI-SVMRBF. We found that GALoRSI-SVMRBF successfully predicts low and high cognitive workload of subjects while driving a vehicle. In general, it is observed that the model uses the information extracted from multiple EEG signals, reducing the original dataset by more than 50%, maximizing its predictive capacity, achieving a precision rate of >90% in the classification of the information. During this thesis, the experiments showed that obtaining a high percentage of prediction depends on several factors, from applying a useful collection technique data until the last step of the prediction model.La conducción de un vehículo es una actividad compleja que está expuesta a demandas que cambian continuamente por diferentes factores, tales como, el límite de velocidad, obstáculos en la vía, tráfico vehicular, entre otros. Al desempeñar esta actividad, los conductores requieren un alto grado de concentración incrementando la cantidad de demanda mental conocida como carga. En los últimos años, se han propuesto mecanismos para monitorear y/o predecir los niveles de carga cognitiva de los sujetos al conducir un vehículo, centrándose en el uso de herramientas subjetivas y de rendimiento vehicular. Otras investigaciones, han enfatizado en el uso y análisis de la información fisiológica, siendo las señales electroencefalográficas (EEG) las más utilizadas para identificar los estados cognitivos por su alta precisión. A pesar del gran avance realizado, estas investigaciones se han basado en técnicas tradicionales o en el análisis de la información proveniente de fuentes específicas para identificar el estado interno del sujeto, obteniendo modelos sobreentrenados o robustos, incrementando el tiempo de análisis afectando el desempeño del modelo. En esta tesis doctoral se presenta un nuevo modelo para predecir los estados de baja y alta carga cognitiva de los sujetos al enfrentarse a escenarios de la conducción de un vehículo denominado GALoRSI-SVMRBF (Genetic Algorithms and Logistic Regression for the Structuring of Information-Support Vector Machine with Radial Basis Function Kernel). GALoRSI-SVMRBF fue desarrollado utilizando los algoritmos de aprendizaje automático y técnicas estadísticas basado en la información proveniente de las señales EEG. Primero, GALoRSI-SVMRBF crea una base de datos extrayendo las características que serán utilizadas en el modelo a través de técnicas estadísticas. Posteriormente, propone un nuevo método para el reconocimiento de patrones basado en la selección de características que combina pruebas estadísticas, algoritmos genéticos y regresión logística. Este método consiste principalmente en seleccionar un conjunto de datos EEG y explorar la combinación de la información para identificar las características claves que contribuyan al reconocimiento de dos estados cognitivos. Después, la información seleccionada es definida como un índice para el reconocimiento de patrones y utilizada para estructurar un nuevo conjunto de datos que soporta información de uno o múltiples canales para optimizar el proceso de aprendizaje y clasificación del modelo. Por último, es desarrollado el clasificador del modelo de predicciones el cual consiste en dos etapas definidas como entrenamiento y prueba. Nosotros encontramos que GALoRSI-SVMRBF predice de manera exitosa la carga cognitiva baja y alta de los sujetos durante la conducción de un vehículo. En general, se observó que el modelo utiliza la información extraída de una o múltiples señales EEG y logrando una tasa de precisión >90% en la clasificación de la informaciónPostprint (published version