This work addresses the environmental recognition of autonomous off-road vehicles. Algorithms, like deep learning, offer impressive performance regarding the classification and segmentation of a scene. However, context changes, scene variabilities, or disturbances pose significant challenges to these approaches and cause perception failures. A challenge is achieving the universal applicability of perception algorithms. Usually, an algorithm fails in particular situations due to unconsidered circumstances in the design phase, and complexity prevents fully considering all details. Accordingly, this thesis aims to increase the perception’s robustness through context and data incorporation. Furthermore, it derives concepts for transferring methods to other robots and scenes. A hint that such a task is achievable provides human cognition, which is remarkably skillful and adjusts to arbitrary situations. Biologically motivated perception and cognitive research indicate how an achievable perception design might function, leading to guidelines for artificial perception conception. The paradigm of behavior-based systems suits these criteria due to modularity, reactivity, and robustness. It allows realizing robust and transferable perception and control systems. Consequently, the thesis proposes a novel and reconfigurable behavior-based top-down and bottom-up perception approach. Quality assessment for data filtering and deviation control is a central aspect, resulting in improved perception and data fusion results. Attentional processing allows for selecting data based on attractiveness, task, environmental context, and history. Further, context assessment of classification results enables reasoning according to the robot’s memories and knowledge. Validation uses five demonstrator vehicles operating in diverse environments and fulfilling distinct tasks. Here, a robust performance was achievable, and perception adjusted well to the tested scenes and hardware layouts.

Rapid developments in electronics over the past two decades have induced a move from purely mechanical vehicles to mechatronics design. Recent advances in computing, sensors, and information technology are pushing mobile equipment design to incorporate higher levels of automation under the novel concept of intelligent vehicles. Mechatronics and Intelligent Systems for Off-road Vehicles introduces this concept, and provides an overview of recent applications and future approaches within this field. Several case studies present real examples of vehicles designed to navigate in off-road environments typically encountered by agriculture, forestry, and construction machines. The examples analyzed describe and illustrate key features for agricultural robotics, such as automatic steering, safeguarding, mapping, and precision agriculture applications. The eight chapters include numerous figures, each designed to improve the reader’s comprehension of subjects such as: • automatic steering systems; • navigation systems; • vehicle architecture; • image processing and vision; and • three-dimensional perception and localization. Mechatronics and Intelligent Systems for Off-road Vehicles will be of great interest to professional engineers and researchers in vehicle automation, robotics, and the application of artificial intelligence to mobile equipment; as well as to graduate students of mechanical, electrical, and agricultural engineering.

All biological systems with vision move about their environments and successfully perform many tasks. The same capabilities are needed in the world of robots. To that end, recent results in empirical fields that study insects and primates, as well as in theoretical and applied disciplines that design robots, have uncovered a number of the principles of navigation. To offer a unifying approach to the situation, this book brings together ideas from zoology, psychology, neurobiology, mathematics, geometry, computer science, and engineering. It contains theoretical developments that will be essential in future research on the topic -- especially new representations of space with less complexity than Euclidean representations possess. These representations allow biological and artificial systems to compute from images in order to successfully deal with their environments. In this book, the barriers between different disciplines have been smoothed and the workings of vision systems of biological organisms are made clear in computational terms to computer scientists and engineers. At the same time, fundamental principles arising from computational considerations are made clear both to empirical scientists and engineers. Empiricists can generate a number of hypotheses that they could then study through various experiments. Engineers can gain insight for designing robotic systems that perceive aspects of their environment. For the first time, readers will find: * the insect vision system presented in a way that can be understood by computational scientists working in computer vision and engineering; * three complete, working robotic navigation systems presented with all the issues related to their design analyzed in detail; * the beginning of a computational theory of direct perception, as advocated by Gibson, presented in detail with applications for a variety of problems; and * the idea that vision systems could compute space representations different from perfect metric descriptions -- and be used in robotic tasks -- advanced for both artificial and biological systems.

The 3 volume-set LNCS 10901, 10902 + 10903 constitutes the refereed proceedings of the 20th International Conference on Human-Computer Interaction, HCI 2018, which took place in Las Vegas, Nevada, in July 2018. The total of 1171 papers and 160 posters included in the 30 HCII 2018 proceedings volumes was carefully reviewed and selected from 4346 submissions. HCI 2018 includes a total of 145 papers; they were organized in topical sections named: Part I: HCI theories, methods and tools; perception and psychological issues in HCI; emotion and attention recognition; security, privacy and ethics in HCI. Part II: HCI in medicine; HCI for health and wellbeing; HCI in cultural heritage; HCI in complex environments; mobile and wearable HCI. Part III: input techniques and devices; speech-based interfaces and chatbots; gesture, motion and eye-tracking based interaction; games and gamification.

In the near future, we will witness vehicles with the ability to provide drivers with several advanced safety and performance assistance features. Autonomous technology in ground vehicles will afford us capabilities like intersection collision warning, lane change warning, backup parking, parallel parking aids, and bus precision parking. Providing you with a practical understanding of this technology area, this innovative resource focuses on basic autonomous control and feedback for stopping and steering ground vehicles.Covering sensors, estimation, and sensor fusion to percept the vehicle motion and surrounding objects, this unique book explains the key aspects that makes autonomous vehicle behavior possible. Moreover, you find detailed examples of fusion and Kalman filtering. From maps, path planning, and obstacle avoidance scenarios...to cooperative mobility among autonomous vehicles, vehicle-to-vehicle communication, and vehicle-to-infrastructure communication, this forward-looking book presents the most critical topics in the field today.

This book is dedicated to user experience design for automated driving to address humane aspects of automated driving, e.g., workload, safety, trust, ethics, and acceptance. Automated driving has experienced a major development boost in recent years. However, most of the research and implementation has been technology-driven, rather than human-centered. The levels of automated driving have been poorly defined and inconsistently used. A variety of application scenarios and restrictions has been ambiguous. Also, it deals with human factors, design practices and methods, as well as applications, such as multimodal infotainment, virtual reality, augmented reality, and interactions in and outside users. This book aims at 1) providing engineers, designers, and practitioners with a broad overview of the state-of-the-art user experience research in automated driving to speed-up the implementation of automated vehicles and 2) helping researchers and students benefit from various perspectives and approaches to generate new research ideas and conduct more integrated research.