The Future of the Automotive Industry: The Disruptive Forces of AI,Data Analytics,and Digitization
by: Inma Martínez
Publisher finelybook 出版社: Apress; 1st ed. edition (23 Jun. 2021)
Language 语言: English
Print Length 页数: 227 pages
ISBN-10: 1484270258
ISBN-13: 9781484270257
Book Description
Nothing defined the 20th century more than the evolution of the car industry. The 2020 decade will see the automotive industry leap forward beyond simply moving people geographically toward a new purpose: to become a services industry. This book takes readers on a journey where cars will evolve towards becoming “computers on wheels.”
The automotive industry is one of the sectors most profoundly changed by: digitalization and the 21st century energy needs. You’ll explore the shifting paradigms and how cars today represent a new interpretation of what driving should be and what cars should offer. This book presents exciting case studies on how artificial intelligence (AI) and data analytics are used to design future cars,predict car efficiency,ensure safety and simulate engineering dynamics for its design,as well as a new arena for IoT and human data. It opens a window into the origins of cars becoming software-run machines,first to run internal diagnostics,and then to become machines connected to other external machines via Bluetooth,to finally the Internet via 5G.
From transportation to solving people’s problems,The Future of the Automotive Industry is less about the technology itself,but more about the outcomes of technology in the future,and the transformative power it has over a much beloved item: cars.
What You’ll Learn
Explore smart cities and their evolution when it comes to traffic and vehicles
Gain a new perspective on the future of cars and transportation based on how digital technologies will transform vehicles
Examine how AI and IoT will create new contexts of interactions with drivers and passengers alike
Review concepts such as personalizing the driving experience and how this will take form
See how self-driving cars impact data mining of personal data
