GSDrive: 3Dガウススプラッティング環境によるマルチモード将来軌道探索で運転方針を強化
GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment
模倣学習と強化学習を組み合わせ、3Dガウススプラッティング環境で将来軌道を探索して密な報酬を生成し、エンドツーエンド自動運転のポリシーを改善するフレームワークを提案した。
詳しい要約
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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: Ziang Guo, Chen Min, Xuefeng Zhang, Yixiao Zhou, Shuo Wang, Sifa Zheng, Dzmitry Tsetserukou, Zufeng Zhang
分類: cs.RO
原文アブストラクト
End-to-end (E2E) autonomous driving aims to directly map sensory observations to driving actions, but its real-world deployment is hindered by evolving data distributions and the high cost of continual annotation. While combining imitation learning (IL) and reinforcement learning (RL) is a common strategy for policy improvement, conventional RL training relies on delayed, event-based rewards, where policies learn only from catastrophic outcomes such as collisions, leading to premature convergence to suboptimal behaviors. To address these limitations, we propose GSDrive, a framework that uses a differentiable 3D Gaussian Splatting (3DGS) environment for future-aware trajectory probing and reward shaping in E2E driving. GSDrive first learns a multi-mode trajectory probe via IL and then uses RL to evaluate multiple candidate futures in the 3DGS environment, converting their simulated returns into dense shaping rewards for policy optimization. This yields a cyclic hybrid IL-RL training loop, where IL supplies structured future priors and RL provides interactive feedback for iterative refinement. Evaluated on the reconstructed nuScenes dataset, our method outperforms other simulation-based RL approaches in closed-loop experiments. Code is available at https://github.com/ZionGo6/GSDrive.
