Archana Naganathan – författare

This reference book explores the integration of cognitive computing technologies in the automotive industry to enhance smart transportation systems. It focuses on how AI, machine learning, and data analytics can improve vehicle automation, safety, and efficiency. Automation can support driverless vehicle transportation and bridge the gap between manual control and fully automated navigation systems. The text introduces a discussion on numerous applications of cognitive computing in smart transportation, motion planning, situation awareness, dynamic driving, adaptive behavior, human intent measurement, and predictive analysis.Key Features:• Discusses basic concepts and architecture of cognitive computing for vehicular systems.• Presents technologies to measure human intent for vehicle safety, including emergency management systems (EMS).• Covers the perception and localization processes in autonomous driving through LiDAR, GPS, and Stereo vision data with critical decision‑making and simulation results.• Elucidates the application of motion planning for smart transportation.• Covers visual perception technologies for advanced driver assistance systems (ADAS) through deep learning.The text is primarily written for graduate students, academic researchers, and professionals in the fields of computer science, electrical engineering, automotive engineering, and civil engineering.

Position yourself at the forefront of the transportation revolution with this guide to mastering computational intelligence that serves as the essential linchpin for the safe, sustainable, and hyper-connected Internet of Vehicular Things. The rapid adoption of the Internet of Things has transformed the way we interact with our environment, ushering in an era of unprecedented connectivity and data sharing. One of the most dynamic and influential manifestations of this technological revolution is the Internet of Vehicular Things, a paradigm that connects the digital and physical worlds through the integration of intelligent vehicles, smart infrastructure, and advanced communication networks. In this fast-evolving landscape, computational intelligence emerges as the linchpin that enables IoVT to realize its full potential. This book addresses the fascinating intersection of vehicle technology and computational intelligence. It explores the transformative power of algorithms, machine learning, artificial intelligence, and data analytics shaping the future of transportation. As vehicles become smarter, safer, and more efficient, the opportunities for innovation and optimization are limitless. The IoVT ecosystem encompasses a wide range of applications, from autonomous vehicles and traffic management systems to driver assistance technologies and predictive maintenance. By harnessing the collective intelligence of vehicles and infrastructures, IoVT promises to revolutionize not only our daily commute, but the broader transportation landscape, paving the way for sustainable, efficient, and safe mobility solutions. This book is a comprehensive guide for researchers, engineers, practitioners, and policymakers looking to navigate the complex terrain of IoVT using computational intelligence. It provides a multidisciplinary perspective and draws on recent advances in computer science, data science, electrical engineering, and transportation science to facilitate a deep understanding of the key concepts, challenges, and opportunities associated with IoVT and computational intelligence.

This book presents various application areas of computing in the automotive sector. The authors explain how computing enhances the performance of vehicles, covering the applications of computing in smart transportation and the future scope. The authors focus on computing for vehicle safety in conjunction with the latest technologies in Internet of Things (IoT). The book provides a holistic approach to computing in an inter-disciplinary and unified view. Topics covered include driverless automated navigation systems, smart transportation, self-learning systems, in-vehicle intelligent systems, and off-road vehicle diagnosis and maintenance, among others. The authors include simulated examples and case studies for better understanding of the technologies and applications. The book is intended for a wide range of readers from students to researchers and industry practitioners and is a useful resource for those planning to pursue research in the area of computing and autonomous driving vehicles.