用贝叶斯博弈统一决策与轨迹规划，应对交通中多模态不确定性。
Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach
- 将人类驾驶行为建模为博弈中的类型，考虑多模态不确定性
- 通过无悔学习求解贝叶斯粗相关均衡，实现交互策略最优
- 框架在多种场景下闭环仿真验证，兼具通用性与安全性
自动驾驶中建模交通参与者交互是实现安全且非保守行为的关键挑战，尤其在多模态与行为不确定性并存时。现有方法或无法交互式规划，或仅假设单一行为模式，可能导致灾难性后果。本文提出基于贝叶斯博弈（不完全信息博弈）的集成决策与轨迹规划框架。人类决策具有离散特征，被建模为博弈中的玩家类型。引入基于无悔学习的通用求解器，获得对应的贝叶斯粗相关均衡，捕捉多模态情境下的交互关系。利用该均衡，决策与轨迹规划同步进行，所得交互策略在对手驾驶意图期望下达到最优。在多种交通场景的闭环仿真中验证了该框架的通用性与有效性。
原文摘要 · Abstract (English)
Modeling the interaction between traffic agents is a key issue in designing safe and non-conservative maneuvers in autonomous driving. This problem can be challenging when multi-modality and behavioral uncertainties are engaged. Existing methods either fail to plan interactively or consider unimodal behaviors that could lead to catastrophic results. In this paper, we introduce an integrated decision-making and trajectory planning framework based on Bayesian game (i.e., game of incomplete information). Human decisions inherently exhibit discrete characteristics and therefore are modeled as types of players in the game. A general solver based on no-regret learning is introduced to obtain a corresponding Bayesian Coarse Correlated Equilibrium, which captures the interaction between traffic agents in the multimodal context. With the attained equilibrium, decision-making and trajectory planning are performed simultaneously, and the resulting interactive strategy is shown to be optimal over the expectation of rivals' driving intentions. Closed-loop simulations on different traffic scenarios are performed to illustrate the generalizability and the effectiveness of the proposed framework.
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