Radar2Plan: 4Dレーダによるエンドツーエンド自己軌道計画のベンチマーク
Radar2Plan: Benchmarking 4D Radar for End-to-End Open-Loop Ego-Trajectory Planning
実世界の4Dレーダデータを用いて自動運転の開ループ自己軌道計画を評価する初のモジュラーベンチマークを提案し、悪天候・低照度下での4Dレーダの有効性と他センサとの相補性を検証した。
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著者: Ling Yao, Yichun Xiao, Jin Jin, Yihan Zhang, Fangqiang Ding
分類: cs.RO
原文アブストラクト
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.
