  Conventional trajectory planning approaches for autonomous racing are based
on the sequential execution of prediction of the opposing vehicles and
subsequent trajectory planning for the ego vehicle. If the opposing vehicles do
not react to the ego vehicle, they can be predicted accurately. However, if
there is interaction between the vehicles, the prediction loses its validity.
For high interaction, instead of a planning approach that reacts exclusively to
the fixed prediction, a trajectory planning approach is required that
incorporates the interaction with the opposing vehicles. This paper
demonstrates the limitations of a widely used conventional sampling-based
approach within a highly interactive blocking scenario. We show that high
success rates are achieved for less aggressive blocking behavior but that the
collision rate increases with more significant interaction. We further propose
a novel Reinforcement Learning (RL)-based trajectory planning approach for
racing that explicitly exploits the interaction with the opposing vehicle
without requiring a prediction. In contrast to the conventional approach, the
RL-based approach achieves high success rates even for aggressive blocking
behavior. Furthermore, we propose a novel safety layer (SL) that intervenes
when the trajectory generated by the RL-based approach is infeasible. In that
event, the SL generates a sub-optimal but feasible trajectory, avoiding
termination of the scenario due to a not found valid solution.
