Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how the human driver intention mechanism will contribute to a more naturalistic on-board decision system for automated vehicles. Features examples of using machine learning/deep learning to build industry products Depicts future trends for driver behavior detection and driver intention inference Discuss traffic context perception techniques that predict driver intentions such as Lidar and GPS

The AVEC symposium is a leading international conference in the fields of vehicle dynamics and advanced vehicle control, bringing together scientists and engineers from academia and automotive industry. The first symposium was held in 1992 in Yokohama, Japan. Since then, biennial AVEC symposia have been established internationally and have considerably contributed to the progress of technology in automotive research and development. In 2016 the 13th International Symposium on Advanced Vehicle Control (AVEC’16) was held in Munich, Germany, from 13th to 16th of September 2016. The symposium was hosted by the Munich University of Applied Sciences. AVEC’16 puts a special focus on automatic driving, autonomous driving functions and driver assist systems, integrated control of interacting control systems, controlled suspension systems, active wheel torque distribution, and vehicle state and parameter estimation. 132 papers were presented at the symposium and are published in these proceedings as full paper contributions. The papers review the latest research developments and practical applications in highly relevant areas of vehicle control, and may serve as a reference for researchers and engineers.

This volume of the Lecture Notes in Mobility series contains papers written by speakers and poster presenters at the 21st International Forum on Advanced Microsystems for Automotive Applications (AMAA 2017) "Smart Systems Transforming the Automobile" that was held in Berlin, Germany in September 2017. The authors report about recent breakthroughs in electric and electronic components and systems, driver assistance and vehicle automation as well as safety and testing. Furthermore, legal aspects and impacts of connected and automated driving are covered. The target audience primarily comprises research experts and practitioners in industry and academia, but the book may also be beneficial for graduate students alike.

Human Factors in Transportation Proceedings of the 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022), July 24–28, 2022, New York, USA

This book provides relevant theoretical frameworks and the latest empirical research findings in Operations Research (OR) and Management Science (MS) as applied to sustainability. Its goal is to identify and describe ways in which OR and MS have been applied to and influenced the development of sustainability. Many of the issues we face today stem from the interconnectivity of the economy, society, and the environment, and from how both the economy and society are affecting the environment. In response, there have been a range of local and global efforts to advance society without harming the natural environment. The book showcases how OR/MS can help to address these issues, specifically with regard to renewable energy, smart industry, smart cities, transportation, smart homes and devices, etc. This book is intended for professionals in the fields of energy, engineering, information science, mathematics and economics, and for researchers who wish to develop new skills in connection with sustainability, or whose work involves sustainability.

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. Covers everything needed to design an effective driver monitoring system, including sensors, areas to monitor, computing devices, and data analysis algorithms Explores aspects of driver behavior that should be considered when designing an intelligent HMI Examines the L3 take-over process in detail