Page 37 - EE Times Europe November 2021 final

EE|Times EUROPE 37

AI’s Impact on the Current and Future Automotive Industry

technology used in smartphones and consumer electronics for deploy-
ment in infotainment and human-machine interfaces. Alexa, CarPlay,
Android Auto, and similar products have been introduced in most new
models and model updates.
Remote diagnostics is a leading telematics application. The addition
of AI technology can help predict device failures, for example.
AI-based vision systems are used in driver-monitoring systems
(DMS) for ADAS-equipped cars. DMS is expected to see rapid growth
with improved AI technology.
Many ADAS functions also use AI technology, including adaptive
cruise control and multiple versions of parking assist. L1 and L2 ADAS
vehicles will use increasing amounts of AI technology in new models.

EMERGING AI USES
Limited driving pilots are emerging from multiple OEMs. They are
often called L2+, but that terminology is not included in current stan-
dards. Calling them autopilots is a mistake, as it confuses consumers
and implies more capability than exists. And they have caused crashes.
L3 vehicles have been available for several years, but regulatory
restrictions have limited their deployment. Regulations allowing L3 AVs
are emerging, and L3 vehicles use much AI technology.
Both OTA software and cybersecurity functions are adding AI tech-
nology via embedded software clients along with cloud-based services
and analytics software.
An emerging AI application is AV development and testing for mul-
tiple AV use cases. An estimated 5,000 AVs are in testing or pilot mode,
mostly in China and the U.S. They include goods AVs, autonomous
trucks, robotaxis, and fixed-route AVs.

FUTURE AI USES
AV use cases are the most valuable and difficult applications for AI
technology. The goal is a software driver that is better than the best
human drivers, with none of the drawbacks of human behavior.
Software development is ripe for AI-based technology improvements.
Identification and repair of software bugs are likely to happen in the
next decade via innovative AI technology.
Cybersecurity advances derived from AI technology are perhaps
the most pressing need for the automotive industry and others. The
requirements are attracting large, ongoing investments.

BOTTOM LINE
AI technology has become a major driving force in the automotive
industry (pun intended). So far, two companies have led in adopting AI
technology in automotive: Nvidia and Tesla. Nvidia is the clear leader
in providing chips and software standards for creating and using AI
models. Tesla is steadily deploying AI, particularly to its overly ambi-
tious Autopilot.
Meanwhile, many more companies are focused on automotive AI.
Mobileye is the leader in ADAS advances, with AVs on its drawing board;
Google-Waymo has pioneered development of software-based drivers.
As safety concerns grow, AI developers must heed caution signs, lest
unintended consequences stifle innovation. Topping this list is unlock-
ing AI black boxes that limit deployment of trust systems. Elsewhere,
bias in training data is a growing problem that is difficult to assess and
consequently hard to solve.
AI regulation is on the way from the EU, and other regions will
follow suit.
For the foreseeable future, AI developers must proceed with caution
in building safe, robust automated systems. ■

Egil Juliussen is the former director of research for infotainment and
ADAS at IHS Automotive; an independent auto industry analyst; and
EE Times’ “Egil’s Eye” columnist. This article was originally published on
EE Times and may be viewed at bit.ly/3kKUQbR.

www.eetimes.eu | NOVEMBER 2021