Regarding the development of automated driving, manufacturers, technology
startups, and systems developers have taken some different approaches. Some are
on the path toward stand-alone vehicles, mostly relying on onboard sensors and
intelligence. On the other hand, the connected, cooperative, and automated
mobility (CCAM) approach relies on additional communication and information
exchange to ensure safe and secure operation. CCAM holds great potential to
improve traffic management, road safety, equity, and convenience. In both
approaches, there are increasingly large amounts of data generated and used
functions in perception, situational awareness, path prediction, and
decision-making. The use of artificial intelligence is instrumental in
processing such data; and in that context, “edge AI” is a more recent type of
implementation.
Edge Artificial Intelligence in Cooperative, Connected, and Automated
Mobility explores perspectives on edge AI for CCAM, explores primary
applications, and presents an outlook on further advancements. The report
includes a discussion on the benefits, risks, and challenges related to the use
of edge AI in this domain. Major issues such as privacy and cybersecurity are
considered, as are misconceptions.