Decision-making stands as a pivotal component in the realm of autonomous vehicles (AVs), playing a crucial role in navigating the intricacies of autonomous driving. Amidst the evolving landscape of data-driven methodologies, enhancing decision-making performance in complex scenarios has emerged as a prominent research focus. Despite considerable advancements, current learning-based decision-making approaches exhibit potential for refinement, particularly in aspects of policy articulation and safety assurance. To address these challenges, we introduce DDM-Lag, a Diffusion Decision Model, augmented with Lagrangian-based safety enhancements. This work conceptualizes the sequential decision-making challenge inherent in autonomous driving as a problem of generative modeling, adopting diffusion models as the medium for assimilating patterns of decision-making. We introduce a hybrid policy update strategy for diffusion models, amalgamating the principles of behavior cloning and Q-learning, alongside the formulation of an Actor-Critic architecture for the facilitation of updates. To augment the model's exploration process with a layer of safety, we incorporate additional safety constraints, employing a sophisticated policy optimization technique predicated on Lagrangian relaxation to refine the policy learning endeavor comprehensively. Empirical evaluation of our proposed decision-making methodology was conducted across a spectrum of driving tasks, distinguished by their varying degrees of complexity and environmental contexts. The comparative analysis with established baseline methodologies elucidates our model's superior performance, particularly in dimensions of safety and holistic efficacy.

翻译：决策是自主车辆（AV）领域的核心组成部分，在应对复杂自动驾驶场景中发挥着关键作用。随着基于数据驱动方法的不断发展，提升复杂场景下的决策性能已成为研究热点。尽管已取得显著进展，现有基于学习的决策方法在策略表达与安全保障方面仍有优化空间。为应对这些挑战，本文提出DDM-Lag——一种融合拉格朗日安全增强机制的扩散决策模型。该工作将自动驾驶中的序列决策问题概念化为生成式建模问题，采用扩散模型作为吸收决策模式的技术媒介。我们引入了一种混合策略更新机制用于扩散模型，融合了行为克隆与Q学习原理，并构建了用于更新优化的Actor-Critic架构。为在模型探索过程中融入安全层，我们额外纳入了安全约束，采用基于拉格朗日松弛的先进策略优化技术全面优化策略学习过程。我们在一系列具有不同复杂程度和环境背景的驾驶任务上对所提决策方法进行了实证评估。与现有基准方法的对比分析表明，本模型在安全性与综合效能维度均展现出优越性能。