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.

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.

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.