Abstract
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 this https URL.
Abstract (translated)
我们提出了PLUTO，一个强大的框架，可以将自动驾驶中基于模仿学习的规划极限推向更高。我们的改进源于三个关键方面：一个纵向-横向感知模型架构，实现灵活多样且和谐的驾驶行为；一种适用于批量计算的创新辅助损失计算方法；一种利用对比学习的新颖训练框架，通过一系列新的数据增强方法调节驾驶行为，并促进底层交互的理解。我们对PLUTO框架进行了评估，使用了大规模现实世界nuPlan数据集及其相关的标准化规划基准。令人印象深刻的是，PLUTO实现了最先进的闭环性能，超越了其他竞争性的基于学习的方法和当前最高表现的基于规则的规划器，这是第一次实现的。结果和代码可在此链接中查看：https://url.org/
URL
https://arxiv.org/abs/2404.14327
