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nuPlan Overview nuPlan is the world's first large-scale planning benchmark for autonomous driving While there is a growing body of ML-based motion planners, the lack of established datasets, simulation frameworks and metrics has limited the progress in this area
Towards learning-based planning: The nuPlan benchmark for real-world . . . 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
nuPlan — ScenarioNet 0. 1. 1 documentation nuPlan is the world’s first large-scale planning benchmark for autonomous driving It provides a large-scale dataset with 1200h of human driving data from 4 cities across the US and Asia with widely varying traffic patterns (Boston, Pittsburgh, Las Vegas and Singapore)
nuPlan - Registry of Open Data on AWS NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles by Holger Caesar, Juraj Kabzan, Kok Seang Tan, Whye Kit Fong, Eric Wolff, Alex Lang, Luke Fletcher, Oscar Beijbom, Sammy Omari
nuplan_framework. ipynb - Colab - Google Colab nuPlan is the world’s first closed-loop ML-based planning benchmark for autonomous driving It provides a high quality dataset with 1500h of human driving data from 4 cities across the US and
nuPlan Dataset | motional nuplan-devkit | DeepWiki The nuPlan dataset is designed to support machine learning-based motion planning for autonomous vehicles It provides rich sensor data, high-definition maps, and diverse driving scenarios captured from real-world urban environments
NuPlan - Tactics2d NuPlan is the world's first large-scale planning benchmark for autonomous driving The data is recorded over 4 cities - Boston, Pittsburgh, Singapore and Las Vegas