Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909130434805760 |
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| author | Karnchanachari, Napat Geromichalos, Dimitris Tan, Kok Seang Li, Nanxiang Eriksen, Christopher Yaghoubi, Shakiba Mehdipour, Noushin Bernasconi, Gianmarco Fong, Whye Kit Guo, Yiluan Caesar, Holger |
| author_facet | Karnchanachari, Napat Geromichalos, Dimitris Tan, Kok Seang Li, Nanxiang Eriksen, Christopher Yaghoubi, Shakiba Mehdipour, Noushin Bernasconi, Gianmarco Fong, Whye Kit Guo, Yiluan Caesar, Holger |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04133 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving Karnchanachari, Napat Geromichalos, Dimitris Tan, Kok Seang Li, Nanxiang Eriksen, Christopher Yaghoubi, Shakiba Mehdipour, Noushin Bernasconi, Gianmarco Fong, Whye Kit Guo, Yiluan Caesar, Holger Computer Vision and Pattern Recognition Robotics 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. |
| title | Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2403.04133 |