RAD-LAD: ルールと言語に基づくリアルタイム自動運転
RAD-LAD: Rule and Language Grounded Autonomous Driving in Real-Time
リアルタイムで動作する言語・行動プランナーLADと、ルールベースのプランナーRADを提案し、それらを組み合わせたハイブリッドシステムが自動運転の性能を向上させることを示した論文。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Anurag Ghosh, Srinivasa Narasimhan, Manmohan Chandraker, Francesco Pittaluga
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
We present LAD, a real-time language--action planner with an interruptible architecture that produces a motion plan in a single forward pass (~20 Hz) or generates textual reasoning alongside a motion plan (~10 Hz). LAD is fast enough for real-time closed-loop deployment, achieving ~3x lower latency than prior driving language models while setting a new learning-based state of the art on nuPlan Test14-Hard and InterPlan. We also introduce RAD, a rule-based planner designed to address structural limitations of PDM-Closed. RAD achieves state-of-the-art performance among rule-based planners on nuPlan Test14-Hard and InterPlan. Finally, we show that combining RAD and LAD enables hybrid planning that captures the strengths of both approaches. This hybrid system demonstrates that rules and learning provide complementary capabilities: rules support reliable maneuvering, while language enables adaptive and explainable decision-making.
