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.

机器学习已在自动驾驶的感知和预测任务中取代了传统的手工方法，但在规划任务中，基于机器学习的技术采纳速度较慢。本文介绍了世界上第一个真实世界的自动驾驶数据集和基准测试，旨在测试基于机器学习的规划器处理多样化行驶场景并做出安全高效决策的能力。数据集包括 1282 小时来自 4 个城市（拉斯维加斯、波士顿、匹兹堡和新加坡）的多样化驾驶场景，并且具有高质量的自动标记的物体轨迹和红绿灯数据。除了数据集之外，还提供了一个模拟和评估框架，使规划器的行动可以在闭环中模拟，以考虑与其他交通参与者的交互。该研究详细分析了多个基准测试，并研究了基于机器学习和传统方法之间的差距。在 nuplan.org 网站上可以找到 nuPlan 数据集和代码。