Radar2Plan：面向端到端开环自我轨迹规划的四维雷达基准测试
Radar2Plan: Benchmarking 4D Radar for End-to-End Open-Loop Ego-Trajectory Planning
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中文总结 AI 辅助
Radar2Plan是首个面向自动驾驶开环自我轨迹规划的四维雷达基准，通过模块化接口比较七种传感器组合和四种规划基线，证明四维雷达单独即可实现竞争性规划且恶劣天气下鲁棒，并凸显其互补价值。
中文摘要 AI 辅助
恶劣天气和光照不足仍然是移动自主系统中鲁棒自我轨迹规划面临的主要挑战。四维雷达在恶劣条件下提供可靠的感知能力，并可直接测量径向速度。然而，感知的鲁棒性并不一定能转化为鲁棒的下游规划能力，而现有的四维雷达基准主要关注感知而非轨迹规划。我们提出了Radar2Plan，一个使用真实世界四维雷达数据进行开环自我轨迹规划的模块化基准。Radar2Plan通过通用接口连接传感器编码器、场景表示和规划头，使得不同传感器和规划器配置之间能够进行受控比较。利用DSERT-RoLL和MAN TruckScenes数据集，我们在统一协议下，对四种代表性规划基线中的七种相机、四维雷达和激光雷达组合以及12种天气和光照条件进行了评估。据我们所知，Radar2Plan是首个专门评估真实世界四维雷达用于自动驾驶规划的基准。实验表明，仅使用四维雷达即可支持具有竞争力的自我轨迹规划，并在恶劣条件下保持鲁棒性能。传感器配置比较进一步证明了其相对于其他模态的互补价值，同时揭示了其对传感器组合和规划架构的依赖性。模块化设计还支持额外的数据集、感知模态和规划器，为未来基于雷达的自动驾驶规划研究提供了灵活的基础。
英文摘要
Adverse weather and poor illumination remain major challenges for robust ego-trajectory planning in mobile autonomy. 4D radar offers reliable sensing under adverse conditions and direct radial-velocity measurements. However, sensing robustness does not necessarily translate into robust downstream planning, while existing 4D radar benchmarks focus primarily on perception rather than trajectory planning. We present Radar2Plan, a modular benchmark for open-loop ego-trajectory planning using real-world 4D radar data. Radar2Plan connects sensor encoders, scene representations, and planning heads through common interfaces, enabling controlled comparisons between different sensor and planner configurations. Using DSERT-RoLL and MAN TruckScenes, we evaluated seven combinations of camera, 4D radar, and LiDAR in four representative planning baselines and 12 weather and illumination conditions under a unified protocol. To our knowledge, Radar2Plan is the first benchmark dedicated to evaluating real-world 4D radars for autonomous driving planning. Experiments show that 4D radar alone supports competitive ego-trajectory planning and robust performance under adverse conditions. Sensor-configuration comparisons further demonstrate its complementary value to other modalities, while revealing dependencies on sensor combination and planning architecture. The modular design also supports additional datasets, sensing modalities, and planners, providing a flexible foundation for future research on radar-based autonomous driving planning.
发表机构
- HKUST(GZ)（香港科技大学（广州））
- Shanghai Jiao Tong University（上海交通大学）
- University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。
