  The field of motion prediction for automated driving has seen tremendous
progress recently, bearing ever-more mighty neural network architectures.
Leveraging these powerful models bears great potential for the closely related
planning task. In this letter we propose a novel goal-conditioning method and
show its potential to transform a state-of-the-art prediction model into a
goal-directed planner. Our key insight is that conditioning prediction on a
navigation goal at the behaviour level outperforms other widely adopted
methods, with the additional benefit of increased model interpretability. We
train our model on a large open-source dataset and show promising performance
in a comprehensive benchmark.
