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Human-Machine Interaction for Automated Vehicles: Driver Status Monitoring and the Takeover Process explains how to design an intelligent human-machine interface by characterizing driver behavior before and during the takeover process. Multiple solutions are presented to accommodate different sensing technologies, driving environments and driving styles. Depending on the availability and location of the camera, the recognition of driving and non-driving tasks can be based on eye gaze, head movement, hand gesture or a combination. Technical solutions to recognize drivers various behaviors in adaptive automated driving are described with associated implications to the driving quality. Finally, cutting-edge insights to improve the human-machine-interface design for safety and driving efficiency are also provided, based on the use of this sensing capability to measure drivers’ cognition capability.

Automated vehicles --- Automobile driving --- Human-computer interaction --- Automatic control. --- Automation. --- Industrial applications. --- Computer-human interaction --- Human factors in computing systems --- Interaction, Human-computer --- Human engineering --- User-centered system design --- User interfaces (Computer systems) --- Automobile operation --- Automobiles --- Automobiling --- Driving, Automobile --- Motor vehicle driving --- Automated motor vehicles --- Autonomous vehicles --- Driver-free cars --- Driverless cars --- Robot cars (Automated vehicles) --- Self-driving cars --- Motor vehicles --- Driving

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This book offers a systematic, comprehensive, and timely review on V-HAR, and it covers the related tasks, cutting-edge technologies, and applications of V-HAR, especially the deep learning-based approaches. The field of Human Activity Recognition (HAR) has become one of the trendiest research topics due to the availability of various sensors, live streaming of data and the advancement in computer vision, machine learning, etc. HAR can be extensively used in many scenarios, for example, medical diagnosis, video surveillance, public governance, also in human-machine interaction applications. In HAR, various human activities such as walking, running, sitting, sleeping, standing, showering, cooking, driving, abnormal activities, etc., are recognized. The data can be collected from wearable sensors or accelerometer or through video frames or images; among all the sensors, vision-based sensors are now the most widely used sensors due to their low-cost, high-quality, and unintrusive characteristics. Therefore, vision-based human activity recognition (V-HAR) is the most important and commonly used category among all HAR technologies. The addressed topics include hand gestures, head pose, body activity, eye gaze, attention modeling, etc. The latest advancements and the commonly used benchmark are given. Furthermore, this book also discusses the future directions and recommendations for the new researchers.

Mathematical statistics --- Programming --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- computervisie --- patroonherkenning --- factoranalyse --- informatica --- KI (kunstmatige intelligentie) --- interfaces --- AI (artificiële intelligentie)

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Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs.

Technology: general issues --- History of engineering & technology --- TROOP --- truck platooning --- path planning --- kalman filter --- V2V communication --- string stability --- off-tracking --- articulated cargo trucks --- kabsch algorithm --- potential field --- sigmoid curve --- autonomous vehicles --- connected and autonomous vehicles --- artificial neural networks --- end-to-end learning --- multi-task learning --- urban vehicle platooning --- simulation --- attention --- executive control --- simulated driving --- task-cuing experiment --- electroencephalogram --- fronto-parietal network --- object vehicle estimation --- radar accuracy --- data-driven --- radar latency --- weighted interpolation --- autonomous vehicle --- urban platooning --- vehicle-to-vehicle communication --- in-vehicle network --- analytic hierarchy architecture --- traffic scenes --- object detection --- multi-scale channel attention --- attention feature fusion --- collision warning system --- ultra-wideband --- dead reckoning --- time to collision --- vehicle dynamic parameters --- Unscented Kalman Filter --- multiple-model --- electric vehicle --- unified chassis control --- unsprung mass --- autonomous driving --- trajectory tracking --- real-time control --- model predictive control --- tyre blow-out --- yaw stability --- roll stability --- vehicle dynamics model --- n/a

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Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs.

Technology: general issues --- History of engineering & technology --- TROOP --- truck platooning --- path planning --- kalman filter --- V2V communication --- string stability --- off-tracking --- articulated cargo trucks --- kabsch algorithm --- potential field --- sigmoid curve --- autonomous vehicles --- connected and autonomous vehicles --- artificial neural networks --- end-to-end learning --- multi-task learning --- urban vehicle platooning --- simulation --- attention --- executive control --- simulated driving --- task-cuing experiment --- electroencephalogram --- fronto-parietal network --- object vehicle estimation --- radar accuracy --- data-driven --- radar latency --- weighted interpolation --- autonomous vehicle --- urban platooning --- vehicle-to-vehicle communication --- in-vehicle network --- analytic hierarchy architecture --- traffic scenes --- object detection --- multi-scale channel attention --- attention feature fusion --- collision warning system --- ultra-wideband --- dead reckoning --- time to collision --- vehicle dynamic parameters --- Unscented Kalman Filter --- multiple-model --- electric vehicle --- unified chassis control --- unsprung mass --- autonomous driving --- trajectory tracking --- real-time control --- model predictive control --- tyre blow-out --- yaw stability --- roll stability --- vehicle dynamics model