Machine Learning (ML) has replaced traditional handcrafted methods for perception and prediction in autonomous vehicles. Yet for the equally important planning task, the adoption of ML-based techniques is slow. We present nuPlan, the world's first real-world autonomous driving dataset, and benchmark. The benchmark is designed to test the ability of ML-based planners to handle diverse driving situations and to make safe and efficient decisions. To that end, we introduce a new large-scale dataset that consists of 1282 hours of diverse driving scenarios from 4 cities (Las Vegas, Boston, Pittsburgh, and Singapore) and includes high-quality auto-labeled object tracks and traffic light data. We exhaustively mine and taxonomize common and rare driving scenarios which are used during evaluation to get fine-grained insights into the performance and characteristics of a planner. Beyond the dataset, we provide a simulation and evaluation framework that enables a planner's actions to be simulated in closed-loop to account for interactions with other traffic participants. We present a detailed analysis of numerous baselines and investigate gaps between ML-based and traditional methods. Find the nuPlan dataset and code at nuplan.org.

翻译：机器学习（ML）已取代传统手工方法，用于自动驾驶车辆中的感知和预测。然而，在同样重要的规划任务中，基于ML的技术应用进展缓慢。我们提出nuPlan，这是全球首个真实世界自动驾驶数据集和基准。该基准旨在测试基于ML的规划器处理多样驾驶场景，并做出安全高效决策的能力。为此，我们引入了一个新的大规模数据集，包含来自4个城市（拉斯维加斯、波士顿、匹兹堡和新加坡）的1282小时多样化驾驶场景，并包括高质量自动标注的物体轨迹和交通灯数据。我们详尽地挖掘并分类了常见和罕见的驾驶场景，这些场景在评估中用于获取规划器性能和特性的细粒度洞察。除数据集外，我们还提供了一个模拟和评估框架，使规划器的行为能够在闭环中模拟，以考虑与其他交通参与者的交互。我们对众多基线进行了详细分析，并探讨了基于ML方法与传统方法之间的差距。请访问nuplan.org获取nuPlan数据集和代码。