適応的インタラクション様式探索によるマルチモーダル統合予測・意思決定
Multi-modal Integrated Prediction and Decision-making with Adaptive Interaction Modality Explorations
自動運転において、周囲との複雑な相互作用を考慮しつつ、複数のインタラクション様式を効率的に探索して統合的な予測と意思決定を行うフレームワークMINDを提案した。
著者: Tong Li, Lu Zhang, Sikang Liu, Shaojie Shen
分類: cs.RO
原文アブストラクト
Navigating dense and dynamic environments poses a significant challenge for autonomous driving systems, owing to the intricate nature of multimodal interaction, wherein the actions of various traffic participants and the autonomous vehicle are complex and implicitly coupled. In this paper, we propose a novel framework, Multi-modal Integrated predictioN and Decision-making (MIND), which addresses the challenges by efficiently generating joint predictions and decisions covering multiple distinctive interaction modalities. Specifically, MIND leverages learning-based scenario predictions to obtain integrated predictions and decisions with social-consistent interaction modality and utilizes a modality-aware dynamic branching mechanism to generate scenario trees that efficiently capture the evolutions of distinctive interaction modalities with low variation of interaction uncertainty along the planning horizon. The scenario trees are seamlessly utilized by the contingency planning under interaction uncertainty to obtain clear and considerate maneuvers accounting for multi-modal evolutions. Comprehensive experimental results in the closed-loop simulation based on the real-world driving dataset showcase superior performance to other strong baselines under various driving contexts.
