108 PART | III ITS business models
financial industries, AI will become a key success factor. Companies like Uber
or Tesla, active in providing mobility, and Amazon in the area of logistics and
personal assistance, invest strongly in AI as they see an opportunity to revo-
lutionize the way they provide services to their customers. Here, too, the key
players are currently in the United States, ranging from IT companies such as
Google, Microsoft, and Apple to services providers such as Uber and Amazon.
At the same time, a significant part of the talent working in AI is educated in
Europe and has its origins in Europe. Many graduates in computer science, ma-
chine learning, computer vision, math, and statistics get an excellent foundation
at Universities across Europe on BSc, MSc, and PhD level. They are equipped
with excellent skills to work in the area of AI, do research in machine learning,
and create new intelligent systems. With little exciting research and develop-
ment efforts in Europe and a few of the driving companies in Europe, there is
a significant ITS drain. Many of the best students move after completing their
degrees to work with companies such as Google, Microsoft, Amazon, Tesla,
Apple, or Facebook on challenging AI problems in environments where AI is
moved forward and not just applied.
In order to make Europe more attractive as a place to conduct research in AI,
ITS needs to cater for the creation of an exciting research environment that is
well linked to major companies. Such concerted European activity, if success-
ful, has the clear potential not only to keep top talent in Europe but to even at-
tract the best international students to come to Europe to do research and found
new companies in the vicinity of where they find the best research.
9.6 Generating user acceptance by functional-safe algorithms
and methodologies for ITS
User acceptance is essential for the successful implementation and later ex-
ploitation of any automated system. This holds true in particular for highly au-
tomated and autonomous driving as well as for the new, environment-friendly
propulsion systems. In the case of autonomous vehicles, user acceptance mainly
resides on the increase of driver’s trust toward the self-driving automation. Trust
can first be achieved through extensive testing that can be verifiable and under-
standable by the user. It can also be established through the good performance
of the vehicle over time, which must ensure driver’s and passengers’ safety and
comfort in all conditions that the self-driving module is active. Finally, in order
to maximize the acceptance of each individual driver, it is also helpful to pro-
vide personalized systems that adapt to individual driving behaviors. It is im-
portant to understand that when the driving responsibility passes from the driver
to the vehicle controller and the decisions differ significantly from those of the
driver, the result can be user disappointment and disapproval of the technology.
Since autonomous driving systems use AI algorithms for predicting forth-
coming situations and make better decision making, it is important that as many
situations as possible have been used at training time and that the deployed