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.

翻译：我们提出PLUTO，一个强大的框架，旨在推动基于模仿学习的自动驾驶规划极限。我们的改进源于三个关键方面：一种纵向-横向感知的模型架构，支持灵活多样的驾驶行为；一种创新且高效的辅助损失计算方法，适用于批量计算且具有广泛适用性；一种利用对比学习的新颖训练框架，辅以一系列新型数据增强技术，以调控驾驶行为并促进对底层交互的理解。我们利用大规模真实世界nuPlan数据集及其配套的标准化规划基准评估了该框架。令人印象深刻的是，PLUTO实现了最先进的闭环性能，首次超越了其他竞争性的基于学习方法，并击败了当前表现最佳的基于规则的规划器。结果和代码详见https://jchengai.github.io/pluto。