  With the evolution of self-driving cars, autonomous racing series like
Roborace and the Indy Autonomous Challenge are rapidly attracting growing
attention. Researchers participating in these competitions hope to subsequently
transfer their developed functionality to passenger vehicles, in order to
improve self-driving technology for reasons of safety, and due to environmental
and social benefits. The race track has the advantage of being a safe
environment where challenging situations for the algorithms are permanently
created. To achieve minimum lap times on the race track, it is important to
gather and process information about external influences including, e.g., the
position of other cars and the friction potential between the road and the
tires. Furthermore, the predicted behavior of the ego-car's propulsion system
is crucial for leveraging the available energy as efficiently as possible. In
this paper, we therefore present an optimization-based velocity planner,
mathematically formulated as a multi-parametric Sequential Quadratic Problem
(mpSQP). This planner can handle a spatially and temporally varying friction
coefficient, and transfer a race Energy Strategy (ES) to the road. It further
handles the velocity-profile-generation task for performance and emergency
trajectories in real time on the vehicle's Electronic Control Unit (ECU).
