This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.
This book examines distributed video coding (DVC) and multiple description coding (MDC), two novel techniques designed to address the problems of conventional image and video compression coding. Covering all fundamental concepts and core technologies, the chapters can also be read as independent and self-sufficient, describing each methodology in sufficient detail to enable readers to repeat the corresponding experiments easily. Topics and features: provides a broad overview of DVC and MDC, from the basic principles to the latest research; covers sub-sampling based MDC, quantization based MDC, transform based MDC, and FEC based MDC; discusses Sleplian-Wolf coding based on Turbo and LDPC respectively, and comparing relative performance; includes original algorithms of MDC and DVC; presents the basic frameworks and experimental results, to help readers improve the efficiency of MDC and DVC; introduces the classical DVC system for mobile communications, providing the developmental environment in detail.
Almost half a century ago, policy leaders issued the Declaration of Alma Ata and embraced the promise of health for all through primary health care (PHC). That vision has inspired generations. Countries throughout the world—rich and poor—have struggled to build health systems anchored in strong PHC where they were needed most. The world has waited long enough for high-performing PHC to become more than an aspiration; it is now time to deliver. The COVID-19 (Coronavirus) pandemic has facilitated the reckoning for that shared failure—but it has also created a once-in-a-generation opportunity for transformational health system changes. The pandemic has shown policy makers and ordinary citizens why health systems matter and what happens when they fail. Bold reforms now can prepare health systems for future crises and bring goals such as universal health coverage within reach. PHC holds the key to these transformations. To fulfill that promise, however, the walk has to finally match the talk. Walking the Talk: Reimagining Primary Health Care after COVID-19 outlines how to get there. It charts an agenda to reimagined, fit-for-purpose PHC. It asks three questions about health systems reform built around PHC: Why? What? How? The characteristics of high-performing PHC are precisely those that are most critical for managing the pressures coming to bear on health systems in the post-COVID world. The challenges include future outbreaks and other emergent threats, as well as long-term structural trends that are reshaping the environments in which systems operate in noncrisis times. Walking the Talk highlights three sets of megatrends that will increasingly affect health systems in the coming decades: • Demographic and epidemiological shifts • Changes in technology • Citizens’ evolving expectations for health care. Reimagined PHC systems will be equipped through optimized system design, financing, and delivery to ensure high-quality services, care to address patients’ needs, fairness and accountability, and resilient systems.
This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.
This will help us customize your experience to showcase the most relevant content to your age group
Please select from below
Login
Not registered?
Sign up
Already registered?
Success – Your message will goes here
We'd love to hear from you!
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.