nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

By Holger Caesar, Juraj Kabzan, Kok Seang Tan, Whye Kit Fong, Eric M. Wolff, A. Lang, L. Fletcher, Oscar Beijbom, Sammy Omari. arXiv.org, 2021

620 citations recorded by the source corpus.

In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving. While there is a growing body of ML-based motion planners, the lack of established datasets and metrics has limited the progress in this area. Existing benchmarks for autonomous vehicle motion prediction have focused on short-term motion forecasting, rather than long-term planning. This has led previous works to use open-loop evaluation with L2-based metrics, which are not suitable for fairly evaluating long-term planning. Our benchmark overcomes these limitations by introducing a large-scale driving dataset, lightweight closed-loop simulator, and motion-planning-specific metrics. We provide a high-quality dataset with 1500h of human driving data from 4 cities across the US and Asia with widely varying traffic patterns (Boston, Pittsburgh, Las Vegas and Singapore). We will provide a closed-loop simulation framework with reactive agents and provide a large set of both general and scenario-specific planning metrics. We plan to release the dataset at NeurIPS 2021 and organize benchmark challenges starting in early 2022.

Subfields: Automotive Engineering, Building and Construction, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/b88b38ec61a4881173ab94647d1e97500f4af15b

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Citations in the corpus

DriveLM: Driving with Graph Visual Question Answering (2023, neutral): “Thus, it is possible to perform this functionality using a lightweight LLM [56] or driving-specific architecture that accepts a command as an input [25, 80].”
DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes (2023, neutral): “We compare our method with the latest SOTA approaches trained on the KITTI360, including NeRF-based PNF [6] and 3D Gaussian-based 3DGS [4].”
End-to-End Autonomous Driving: Challenges and Frontiers (2023, background): “We also display the score for each year’s top entry in the CARLA leaderboard [13] (DS, ranging from 0 to 100) and the recent nuPlan challenge [14] (Score ranging from 0 to 1).”
Parting with Misconceptions about Learning-based Vehicle Motion Planning (2023, background): “In this section, we provide the relevant background regarding the data-driven simulator nuPlan [9].”
Planning-oriented Autonomous Driving (2022, methodology): “To tackle this, we adopt a non-linear smoother [7] to adjust the target trajectories and make them physically feasible given an imprecise starting point predicted by the upstream module.”
Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability (2024, neutral): “Incorporating more datasets with action annotations [11, 112, 129] for collaborative training could be beneficial.”