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Driver assistance systems.

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This dissertation explores advancements in vision-based autonomous systems, specifically focusing on adaptive supervision through online learning. The research addresses the limitations of traditional lane assist systems that rely heavily on visible lane markers, proposing an approach that allows autonomous road following on various road types using a single camera. The system learns in real-time by observing human drivers, enabling it to take over after brief demonstrations. It handles multiple plausible options in complex situations, such as intersections or obstacles, by employing a probabilistic framework to predict the most appropriate actions. The work includes a detailed analysis of the learning system's structure and introduces methods to address common decoding biases. Practical applications discussed include autonomous driving and head pose estimation, showcasing the system's versatility. This research primarily targets professionals and academics in the fields of electrical engineering and computer vision, aiming to contribute to the development of more reliable autonomous systems.

Computer vision. --- Driver assistance systems. --- Computer vision --- Driver assistance systems

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Driver assistance systems. --- Computer architecture.

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This book, authored by Ignacio Solís Marcos, explores the human factors involved in partially automated driving, specifically at Level 2 of automation. It examines the attentional effects and challenges drivers face when monitoring automated systems that allow for feet-free and short periods of hands-free driving. The research investigates how drivers integrate additional tasks while using such systems, considering factors like automation trust and experience. The book also evaluates the use of event-related potentials (ERPs) to detect changes in drivers' attention and cognitive resource allocation. It is intended for researchers, professionals, and students interested in the interface between human behavior and advanced driver assistance systems (ADAS).

Driver assistance systems. --- Human-machine systems. --- Driver assistance systems --- Human-machine systems

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A novel navigation assistance for extended range telepresence is presented. The haptic information from the target environment is augmented with guidance commands to assist the user in reaching desired goals in the arbitrarily large target environment from the spatially restricted user environment. Furthermore, a semi-mobile haptic interface was developed, one whose lightweight design and setup configuration atop the user provide for an absolutely safe operation and high force display quality.

Telepresence --- Haptic Interface --- Assistance Systems --- Teleoperation --- Haptic Guidance