自動運転における相互作用を考慮した注意機構ベースの高レベル意思決定学習
Learning High-Level Decision Making with an Interaction-Aware Attention-Based Network in Autonomous Driving
自動運転の車線変更や速度制御の高レベル意思決定を、動的な交通状況に応じて可変長の入力に対応しつつ、相互作用を明示的にモデル化する注意機構ベースのアーキテクチャDecisionPerceiverを提案した。固定サイズの潜在空間への投影と細かい行動離散化により、性能とスケーラビリティを向上させた。
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？
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5. 議論はある？
6. 次に読むべき論文は？
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著者: Marcelo Contreras, Willi Poh, Christoph Stiller, Ehsan Hashemi
分類: cs.RO
原文アブストラクト
Reliable learning-based high-level decision making for lane changes and speed control in automated driving must accommodate dynamically sized inputs due to varying scene traffic flow. DeepSet and its variants represent the state of the art among shared-encoder approaches; however, they neglect explicit traffic interaction modeling, limiting performance in negotiation-intensive scenarios such as intersections. Attention-based methods capture interactions among static and dynamic agents, but incur quadratic memory and computational complexity and provide limited control over representation granularity. Inspired by Perceiver IO, an attention-based architecture, DecisionPerceiver, is proposed to project dynamic agent features into a fixed-size latent space, where feature granularity is regulated by the number of latent queries, improving scalability for larger networks. A finer discretization of the action set is further proposed to increase the performance gain due to interaction awareness. Extensive evaluations across three driving scenarios that require different levels of interaction awareness demonstrate consistent performance gains and generalization across various navigation objectives. In addition, the proposed architecture is assessed in scenarios with an increasing number of vehicles to demonstrate scalability.
