Implementation of a Driver Aware Vehicle Using Multimodal Information (original) (raw)
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Frontiers in Neuroscience, 2018
Drivers' hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25-50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while significant research has been done on detecting hazardous states, most studies have not tried to identify the causes of the hazardous states. Such information would be very useful, as it would allow intelligent vehicles to better respond to a detected hazardous state. Thus, this study examined whether the cause of a driver's hazardous state can be automatically identified using a combination of driver characteristics, vehicle kinematics, and physiological measures. Twenty-one healthy participants took part in four 45-min sessions of simulated driving, of which they were mildly sleep-deprived for two sessions. Within each session, there were eight different scenarios with different weather (sunny or snowy), traffic density and cell phone usage (with or without cell phone). During each scenario, four physiological (respiration, electrocardiogram, skin conductance, and body temperature) and eight vehicle kinematics measures were monitored. Additionally, three self-reported driver characteristics were obtained: personality, stress level, and mood. Three feature sets were formed based on driver characteristics, vehicle kinematics, and physiological signals. All possible combinations of the three feature sets were used to classify sleep deprivation (drowsy vs. alert), traffic density (low vs. high), cell phone use, and weather conditions (foggy/snowy vs. sunny) with highest accuracies of 98.8%, 91.4%, 82.3%, and 71.5%, respectively. Vehicle kinematics were most useful for classification of weather and traffic density while physiology and driver characteristics were useful for classification of sleep deprivation and cell phone use. Furthermore, a second classification scheme was tested that also incorporates information about whether or not other causes of hazardous states are present, though this did not result in higher classification accuracy. In the future, these classifiers could be used to identify both the presence and cause of a driver's hazardous state, which could serve as the basis for more intelligent intervention systems.
Physiological-based Driver Monitoring Systems: A Scoping Review
Civil Engineering Journal
A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk ...
Integrated Psychophysical Condition Monitoring System for Vehicle Drivers
Biuletyn Wojskowej Akademii Technicznej, 2013
The article presents a system designed for training and researching driver behaviour to improve road safety as well as to identify and eliminate potentially dangerous situations. The testing station has been equipped with a truck cabin, indicators, sensors for testing the driver's behaviour, and the necessary software. It is expected that research conducted in a strictly controlled environment will enable understanding the impact of the factors associated with driving (driving time, microclimate, noise, vibration, fatigue, stress), as well as other associated factors, such as pharmaceuticals, drugs, alcohol, etc. Understanding the impact of individual factors and their various configurations will enable developing methods to prevent negative effects of these factors, thus minimizing the risks in traffic.
On Driver Behavior Recognition for Increased Safety: A Roadmap
Safety
Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This co...
Advanced methodology for evaluation of driver's actual state with use of technical driving data
This paper presents experiences from several hundred measurements and analysis of experiments performed on our driving simulators. Besides the experiment setup, it describes several different methods used for the classification of driver behavior based mainly on analysis of technical outputs from simulated driving. Our goal is to find out the objective methodology for evaluation of driver's actual state and for assessment of IVIS (or further any HMI devices used in the cars) with respect to drivers' skills and personalities. Such methodology, if successfully found, could serve in future as a comprehensive compound of automobile development process.
A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements
Stress is a major concern in daily life, as it imposes significant and growing health and economic costs on society every year. Stress and driving are a dangerous combination and can lead to life-threatening situations, evidenced by the large number of road traffic crashes that occur every year due to driver stress. In addition, the rate of general health issues caused by work-related chronic stress in drivers who work in public and private transport is greater than in many other occupational groups. An in-vehicle warning system for driver stress levels is needed to continuously predict dangerous driving situations and proactively alert drivers to ensure safe and comfortable driving. As a result of the recent developments in ambient intelligence, such as sensing technologies, pervasive devices, context recognition, and communications, driver stress can be automatically detected using multimodal measurements. This critical review investigates the state of the art of techniques and achievements for automatic driver stress level detection based on multimodal sensors and data. In this work, the most widely used data followed by frequent and highly performed selected features to detect driver stress levels are analyzed and presented. This review also discusses key methodological issues and gaps that hinder the implementation of driver stress detection systems and offers insights into future research directions.
2009
We present research in progress to develop an “Aware” vehicle concept to improve operator performance, mobility and safety. The Aware Vehicle project, in part, seeks to develop systems that communicate with the infrastructure, monitor the operating environment and detect operator state. A further component of this system is informational feedback to the driver that will inform the driver of their relative wellbeing, enhancing their awareness of the operating environment, thus enhancing their capacity to self-regulate and improve their driving. Our approach emphasizes context aware technologies and their possible capacity to monitor driver performance, manage workload, and motivate safe driving behaviors of passenger car and commercial drivers across the lifespan are discussed. We conclude with questions and implications for future research and practice.
Transportation Research Procedia, 2020
The authors solve the issue of identifying changes in the psychophysical factors of drivers' health when driving without the use of special body sensors, which significantly expands the possibility of using the method for drivers of any vehicles. The method allows us to determine correlations between the parameters of vehicle movement and the psychophysical state of the driver in the process of preliminary testing. For that purpose, a special mobile application is used making it possible to determine the subject's reactions to dynamic images during a 1-2-minute test and compare the results with the parameters of movement. The processing results represent correlation functions used to identify the signs of the driver's state (fatigue, sickness, etc.), which affect traffic safety, by the parameters of movement. At the same time and in a similar way, factors related to the technical condition of the vehicle are identified. For that purpose, correlation functions between the indicators of movement, obtained at the current moment and during previous trips, are calculated. The method can be used to improve the safety of transportation with the use of both public and individual vehicles (regarding drivers of private cars, mini electric vehicles, etc.).
Frontiers in Human Neuroscience
INTRODUCTION: THE PROMISE OF AUTOMATED VEHICLES Road traffic crashes represent a leading cause of death worldwide , more than 1.35 million lives each year, 48% of them in four-wheeled vehicles in Europe (World Health Organization, Global Status Report on Road Safety-Summary, 2018, pp. 2 and 6). Driving is a highly complex activity requiring considerable perceptual, physical, and cognitive demands on the driver (Sawyer et al., 2012) despite each of us has learned to drive a car. The human nervous system shows limitations
Towards Driver's State Recognition on Real Driving Conditions
International Journal of Vehicular Technology, 2011
In this work a methodology for detecting drivers' stress and fatigue and predicting driving performance is presented. The proposed methodology exploits a set of features obtained from three different sources: (i) physiological signals from the driver (ECG, EDA, and respiration), (ii) video recordings from the driver's face, and (iii) environmental information. The extracted features are examined in terms of their contribution to the classification of the states under investigation. The most significant indicators are selected and used for classification using various classifiers. The approach has been validated on an annotated dataset collected during real-world driving. The results obtained from the combination of physiological signals, video features, and driving environment parameters indicate high classification accuracy (88% using three fatigue scales and 86% using two stress scales). A series of experiments on a simulation environment confirms the association of fatigu...