  As a part of the digital transformation, we interact with more and more
intelligent gadgets. Today, these gadgets are often mobile devices, but in the
advent of smart cities, more and more infrastructure---such as traffic and
buildings---in our surroundings becomes intelligent. The intelligence, however,
does not emerge by itself. Instead, we need both design techniques to create
intelligent systems, as well as approaches to validate their correct behavior.
An example of intelligent systems that could benefit smart cities are
self-driving vehicles. Self-driving vehicles are continuously becoming both
commercially available and common on roads. Accidents involving self-driving
vehicles, however, have raised concerns about their reliability. Due to these
concerns, the safety of self-driving vehicles should be thoroughly tested
before they can be released into traffic. To ensure that self-driving vehicles
encounter all possible scenarios, several millions of hours of testing must be
carried out; therefore, testing self-driving vehicles in the real world is
impractical. There is also the issue that testing self-driving vehicles
directly in the traffic poses a potential safety hazard to human drivers. To
tackle this challenge, validation frameworks for testing self-driving vehicles
in simulated scenarios are being developed by academia and industry. In this
chapter, we briefly introduce self-driving vehicles and give an overview of
validation frameworks for testing them in a simulated environment. We conclude
by discussing what an ideal validation framework at the state of the art should
be and what could benefit validation frameworks for self-driving vehicles in
the future.
