Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving
<|reference_start|>Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving: 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.<|reference_end|>
@article{karnchanachari2024towards,
    title={Towards learning-based planning:The nuPlan benchmark for real-world
  autonomous driving},
    author={Napat Karnchanachari, Dimitris Geromichalos, Kok Seang Tan, Nanxiang
  Li, Christopher Eriksen, Shakiba Yaghoubi, Noushin Mehdipour, Gianmarco
  Bernasconi, Whye Kit Fong, Yiluan Guo, Holger Caesar},
    journal={arXiv preprint arXiv:2403.04133},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04133},
    primaryClass={cs.CV cs.RO}
}