  Motion prediction and cost evaluation are vital components in the
decision-making system of autonomous vehicles. However, existing methods often
ignore the importance of cost learning and treat them as separate modules. In
this study, we employ a tree-structured policy planner and propose a
differentiable joint training framework for both ego-conditioned prediction and
cost models, resulting in a direct improvement of the final planning
performance. For conditional prediction, we introduce a query-centric
Transformer model that performs efficient ego-conditioned motion prediction.
For planning cost, we propose a learnable context-aware cost function with
latent interaction features, facilitating differentiable joint learning. We
validate our proposed approach using the real-world nuPlan dataset and its
associated planning test platform. Our framework not only matches
state-of-the-art planning methods but outperforms other learning-based methods
in planning quality, while operating more efficiently in terms of runtime. We
show that joint training delivers significantly better performance than
separate training of the two modules. Additionally, we find that
tree-structured policy planning outperforms the conventional single-stage
planning approach.
