  As the automotive industry focuses its attention more and more towards the
software functionality of vehicles, techniques to deliver new software value at
a fast pace are needed. Continuous Experimentation, a practice coming from the
web-based systems world, is one of such techniques. It enables researchers and
developers to use real-world data to verify their hypothesis and steer the
software evolution based on performances and user preferences, reducing the
reliance on simulations and guesswork. Several challenges prevent the verbatim
adoption of this practice on automotive cyber-physical systems, e.g., safety
concerns and limitations from computational resources; nonetheless, the
automotive field is starting to take interest in this technique. This work aims
at demonstrating and evaluating a prototypical Continuous Experimentation
infrastructure, implemented on a distributed computational system housed in a
commercial truck tractor that is used in daily operations by a logistic company
on public roads. The system comprises computing units and sensors, and software
deployment and data retrieval are only possible remotely via a mobile data
connection due to the commercial interests of the logistics company. This study
shows that the proposed experimentation process resulted in the development
team being able to base software development choices on the real-world data
collected during the experimental procedure. Additionally, a set of previously
identified design criteria to enable Continuous Experimentation on automotive
systems was discussed and their validity confirmed in the light of the
presented work.
