nuCarla: A nuScenes-Style Bird's-Eye View Perception Dataset for CARLA Simulation
Zhijie Qiao, Zhong Cao, Henry X. Liu

TL;DR
nuCarla is a large-scale, simulation-based bird's-eye view perception dataset for autonomous driving in CARLA, designed to facilitate closed-loop end-to-end learning and testing with compatibility to real-world models.
Contribution
It introduces nuCarla, a nuScenes-style BEV perception dataset within CARLA, enabling seamless transfer of perception models and supporting closed-loop autonomous driving research.
Findings
Achieves state-of-the-art detection results with BEV backbones.
Supports closed-loop simulation deployment for end-to-end autonomous driving.
Provides a large-scale, balanced dataset compatible with nuScenes format.
Abstract
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…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Human-Automation Interaction and Safety · Social Robot Interaction and HRI
