  We present PLUTO, a powerful framework that pushes the limit of imitation
learning-based planning for autonomous driving. Our improvements stem from
three pivotal aspects: a longitudinal-lateral aware model architecture that
enables flexible and diverse driving behaviors; An innovative auxiliary loss
computation method that is broadly applicable and efficient for batch-wise
calculation; A novel training framework that leverages contrastive learning,
augmented by a suite of new data augmentations to regulate driving behaviors
and facilitate the understanding of underlying interactions. We assessed our
framework using the large-scale real-world nuPlan dataset and its associated
standardized planning benchmark. Impressively, PLUTO achieves state-of-the-art
closed-loop performance, beating other competing learning-based methods and
surpassing the current top-performed rule-based planner for the first time.
Results and code are available at https://jchengai.github.io/pluto.
