用真实交通场景构建高保真仿真，评测端到端自动驾驶
DriveE2E: Closed-Loop Benchmark for End-to-End Autonomous Driving through Real-to-Simulation
- 从100小时监控视频中提取800个真实动态交通场景
- 创建15个视觉一致的数字孪生路口，还原真实环境特征
- 支持复杂城市路口多条件测试，适合评估真实驾驶表现
闭环评估对端到端自动驾驶愈发关键。现有基于CARLA模拟器的闭环基准依赖人工配置交通场景，易偏离真实情况，难以反映实际驾驶性能。为此，我们提出一个简单但具有挑战性的闭环评估框架，通过基础设施协作将真实世界驾驶场景深度融入CARLA模拟器。方法包括：从100小时高架传感器拍摄的视频数据中选取800个动态交通场景，并为15个真实路口创建视觉一致的静态数字孪生体。这些数字孪生体精准复现了对应路口的交通与环境特征，使CARLA中的仿真更加真实。该评估任务因复杂城市路口中驾驶行为、位置、天气和时段的多样性而具有挑战性。此外，我们提供了全面的闭环基准，用于评估端到端自动驾驶模型。项目地址：https://github.com/AIR-THU/DriveE2E。
原文摘要 · Abstract (English)
Closed-loop evaluation is increasingly critical for end-to-end autonomous driving. Current closed-loop benchmarks using the CARLA simulator rely on manually configured traffic scenarios, which can diverge from real-world conditions, limiting their ability to reflect actual driving performance. To address these limitations, we introduce a simple yet challenging closed-loop evaluation framework that closely integrates real-world driving scenarios into the CARLA simulator with infrastructure cooperation. Our approach involves extracting 800 dynamic traffic scenarios selected from a comprehensive 100-hour video dataset captured by high-mounted infrastructure sensors, and creating static digital twin assets for 15 real-world intersections with consistent visual appearance. These digital twins accurately replicate the traffic and environmental characteristics of their real-world counterparts, enabling more realistic simulations in CARLA. This evaluation is challenging due to the diversity of driving behaviors, locations, weather conditions, and times of day at complex urban intersections. In addition, we provide a comprehensive closed-loop benchmark for evaluating end-to-end autonomous driving models. Project URL: \href{https://github.com/AIR-THU/DriveE2E}{https://github.com/AIR-THU/DriveE2E}.
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