EagleVision: 高速自動運転レースにおけるクロスドメイン知覚のためのマルチタスクベンチマーク
EagleVision: A Multi-Task Benchmark for Cross-Domain Perception in High-Speed Autonomous Racing
高速自動運転レース向けに、LiDARベースの3D検出と軌道予測を統合したマルチタスクベンチマークを新たに構築し、都市・シミュレータ・実レース間のドメイン転移性能を評価した。
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2. 先行研究と比べてどこがすごい？
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著者: Zakhar Yagudin, Murad Mebrahtu, Ren Jin, Jiaqi Huang, Yujia Yue, Dzmitry Tsetserukou, Jorge Dias, Majid Khonji
分類: cs.RO, cs.CV
原文アブストラクト
High-speed autonomous racing presents extreme perception challenges, including large relative velocities and substantial domain shifts from conventional urban-driving datasets. Existing benchmarks do not adequately capture these high-dynamic conditions. We introduce EagleVision, a unified LiDAR-based multi-task benchmark for 3D detection and trajectory prediction in high-speed racing, providing newly annotated 3D bounding boxes for the Indy Autonomous Challenge dataset (14,893 frames) and the A2RL Real competition dataset (1,163 frames), together with 12,000 simulator-generated annotated frames, all standardized under a common evaluation protocol. Using a dataset-centric transfer framework, we quantify cross-domain generalization across urban, simulator, and real racing domains. Urban pretraining improves detection over scratch training (NDS 0.72 vs. 0.69), while intermediate pretraining on real racing data achieves the best transfer to A2RL (NDS 0.726), outperforming simulator-only adaptation. For trajectory prediction, Indy-trained models surpass in-domain A2RL training on A2RL test sequences (FDE 0.947 vs. 1.250), highlighting the role of motion-distribution coverage in cross-domain forecasting. EagleVision enables systematic study of perception generalization under extreme high-speed dynamics. The dataset and benchmark are publicly available at https://avlab.io/EagleVision
