Towards learning-based planning:The nuPlan benchmark for real-world
  autonomous driving
Napat Karnchanachari Dimitris Geromichalos Kok Seang Tan Nanxiang Li Christopher Eriksen Shakiba Yaghoubi Noushin Mehdipour Gianmarco Bernasconi Whye Kit Fong Yiluan Guo Holger Caesar
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.