nuCarla是用于自动驾驶闭环仿真的大型鸟瞰图数据集，可直接提升模型训练效果。
nuCarla: A nuScenes-Style Bird's-Eye View Perception Dataset for CARLA Simulation
- 基于CARLA构建，兼容nuScenes格式，支持真实模型迁移。
- 规模与nuScenes相当，类别分布更均衡，适配闭环仿真测试。
- 提供高性能检测模型，助力端到端自动驾驶研发提速。
端到端（E2E）自动驾驶严重依赖闭环仿真，其中感知、规划与控制在交互环境中联合训练与评估。然而，现有大多数数据集来自真实世界，在非交互条件下采集，主要支持开环学习，对闭环测试价值有限。由于缺乏标准化、大规模且经过严格验证的数据集来促进有意义的中间表示（如鸟瞰图特征）的学习，闭环E2E模型仍远落后于简单规则基线。为此，我们引入nuCarla，一个在CARLA模拟器中构建的大规模、类nuScenes的鸟瞰图感知数据集。nuCarla具备：(1) 完全兼容nuScenes格式，支持真实感知模型无缝迁移；(2) 数据规模与nuScenes相当，但类别分布更均衡；(3) 可直接用于闭环仿真部署；(4) 高性能鸟瞰图骨干网络，实现顶尖检测效果。通过开放数据与模型作为基准，nuCarla显著加速闭环E2E开发，为可靠且安全的自动驾驶研究铺平道路。
原文摘要 · Abstract (English)
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.
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