  This report describes our approach to design and evaluate a software stack
for a race car capable of achieving competitive driving performance in the
different disciplines of the Formula Student Driverless. By using a 360{\deg}
LiDAR and optionally three cameras, we reliably recognize the plastic cones
that mark the track boundaries at distances of around 35 m, enabling us to
drive at the physical limits of the car. Using a GraphSLAM algorithm, we are
able to map these cones with a root-mean-square error of less than 15 cm while
driving at speeds of over 70 kph on a narrow track. The high-precision map is
used in the trajectory planning to detect the lane boundaries using Delaunay
triangulation and a parametric cubic spline. We calculate an optimized
trajectory using a minimum curvature approach together with a GGS-diagram that
takes the aerodynamics at different velocities into account. To track the
target path with accelerations of up to 1.6 g, the control system is split into
a PI controller for longitudinal control and model predictive controller for
lateral control. Additionally, a low-level optimal control allocation is used.
The software is realized in ROS C++ and tested in a custom simulation, as well
as on the actual race track.
