nuCarla：用于CARLA仿真的nuScenes风格鸟瞰图（BEV）感知数据集
nuCarla: A nuScenes-Style Bird's-Eye View Perception Dataset for CARLA Simulation
AI总结：
针对端到端自动驾驶闭环仿真缺乏标准化大规模BEV感知数据集的问题，提出nuCarla数据集，其兼容nuScenes格式、类别分布均衡且支持闭环部署，还提供高性能BEV主干，以加速闭环E2E模型开发。
摘要：
端到端（E2E）自动驾驶严重依赖闭环仿真，其中感知、规划和控制在交互式环境中联合训练和评估。然而，大多数现有数据集是在非交互条件下从现实世界收集的，主要支持开环学习，对闭环测试的价值有限。由于缺乏标准化、大规模且经过彻底验证的数据集来促进有意义的中间表示（如鸟瞰图（BEV）特征）的学习，闭环E2E模型甚至远落后于简单的基于规则的基线。为应对这一挑战，我们引入nuCarla，一个在CARLA仿真中构建的nuScenes风格鸟瞰图（BEV）感知数据集。nuCarla具有：（1）与nuScenes格式完全兼容，支持现实世界感知模型的无缝迁移；（2）规模与nuScenes相当，但类别分布更均衡；（3）可直接用于闭环仿真部署；（4）高性能BEV主干网络，可实现最先进的检测结果。通过提供数据和模型作为开放基准，nuCarla大幅加速闭环E2E开发，为自动驾驶中可靠且具安全意识的研究铺平道路。
英文摘要：
End-to-end (E2E) autonomous driving heavily relies on closed-loop simulation, where perception, planning, and control are jointly trained and evaluated in interactive environments. Yet, most existing datasets are collected from the real world under non-interactive conditions, primarily supporting open-loop learning while offering limited value for closed-loop testing. Due to the lack of standardized, large-scale, and thoroughly verified datasets to facilitate learning of meaningful intermediate representations, such as bird's-eye-view (BEV) features, closed-loop E2E models remain far behind even simple rule-based baselines. To address this challenge, we introduce nuCarla, a large-scale, nuScenes-style BEV perception dataset built within the CARLA simulator. nuCarla features (1) full compatibility with the nuScenes format, enabling seamless transfer of real-world perception models; (2) a dataset scale comparable to nuScenes, but with more balanced class distributions; (3) direct usability for closed-loop simulation deployment; and (4) high-performance BEV backbones that achieve state-of-the-art detection results. By providing both data and models as open benchmarks, nuCarla substantially accelerates closed-loop E2E development, paving the way toward reliable and safety-aware research in autonomous driving.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
