PLUTO：突破基于模仿学习的自动驾驶规划极限
PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving
AI总结：
提出PLUTO框架，通过纵向-横向感知架构、创新辅助损失计算及对比学习训练框架，在nuPlan基准上首次实现超越规则方法的SOTA闭环性能。
摘要：
我们提出了PLUTO，一个突破基于模仿学习的自动驾驶规划极限的强大框架。我们的改进源于三个关键方面：一个纵向-横向感知的模型架构，能够实现灵活多样的驾驶行为；一种创新的辅助损失计算方法，广泛适用且高效支持批量计算；一个利用对比学习并结合一系列新数据增强的训练框架，以规范驾驶行为并促进对底层交互的理解。我们使用大规模真实世界nuPlan数据集及其相关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，击败了其他竞争的基于学习的方法，并首次超越了当前表现最佳的基于规则的规划器。结果和代码可在 https://jchengai.github.io/pluto 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
