  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.
