専門家の連鎖：時間的交替による自動運転模倣プランナーの強化
Sequence of Expert: Boosting Imitation Planners for Autonomous Driving through Temporal Alternation
自動運転の模倣学習において、時間軸に沿って複数の専門家モデルを交互に切り替えることで誤差蓄積を抑え、モデル規模やデータを増やさずに閉ループ性能を向上させる手法を提案。
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: Xiang Li, Gang Liu, Weitao Zhou, Hongyi Zhu, Zhong Cao
分類: cs.RO, cs.AI
原文アブストラクト
Imitation learning (IL) has emerged as a central paradigm in autonomous driving. While IL excels in matching expert behavior in open-loop settings by minimizing per-step prediction errors, its performance degrades unexpectedly in closed-loop due to the gradual accumulation of small, often imperceptible errors over time.Over successive planning cycles, these errors compound, potentially resulting in severe failures.Current research efforts predominantly rely on increasingly sophisticated network architectures or high-fidelity training datasets to enhance the robustness of IL planners against error accumulation, focusing on the state-level robustness at a single time point. However, autonomous driving is inherently a continuous-time process, and leveraging the temporal scale to enhance robustness may provide a new perspective for addressing this issue.To this end, we propose a method termed Sequence of Experts (SoE), a temporal alternation policy that enhances closed-loop performance without increasing model size or data requirements. Our experiments on large-scale autonomous driving benchmarks nuPlan demonstrate that SoE method consistently and significantly improves the performance of all the evaluated models, and achieves state-of-the-art performance.This module may provide a key and widely applicable support for improving the training efficiency of autonomous driving models.
