MTA-RL: マルチモーダルトランスフォーマーによる3Dアフォーダンスと強化学習を用いた堅牢な都市運転
MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning
RGB画像とLiDAR点群をトランスフォーマーで融合し、幾何学的な3Dアフォーダンスを予測して強化学習の観測空間とすることで、解釈可能でサンプル効率の高い都市自動運転を実現した。CARLAシミュレーションでゼロショット汎化性能が向上することを示した。
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著者: Guangli Chen, Dianzhao Li, Wenjian Zhong, Bangquan Xie, Ostap Okhrin
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Robust urban autonomous driving requires reliable 3D scene understanding and stable decision-making under dense interactions. However, existing end-to-end models lack interpretability, while modular pipelines suffer from error propagation across brittle interfaces. This paper proposes MTA-RL, the first framework that bridges perception and control through Multi-modal Transformer-based 3D Affordances and Reinforcement Learning (RL). Unlike previous fusion models that directly regress actions, RGB images and LiDAR point clouds are fused using a transformer architecture to predict explicit, geometry-aware affordance representations. These structured representations serve as a compact observation space, enabling the RL policy to operate purely on predicted driving semantics, which significantly improves sample efficiency and stability. Extensive evaluations in CARLA Town01-03 across varying densities (20-60 background vehicles) show that MTA-RL consistently outperforms state-of-the-art baselines. Trained solely on Town03, our method demonstrates superior zero-shot generalization in unseen towns, achieving up to a 9.0% increase in Route Completion, an 11.0% increase in Total Distance, and an 83.7% improvement in Distance Per Violation. Furthermore, ablation studies confirm that our multi-modal fusion and reward shaping are critical, significantly outperforming image-only and unshaped variants, demonstrating the effectiveness of MTA-RL for robust urban autonomous driving.
