Autonomous driving in complex traffic requires reasoning under uncertainty. Common approaches rely on prediction-based planning or risk-aware control, but these are typically treated in isolation, limiting their ability to capture the coupled nature of action and inference in interactive settings. This gap becomes especially critical in uncertain scenarios, where simply reacting to predictions can lead to unsafe maneuvers or overly conservative behavior. Our central insight is that safe interaction requires not only estimating human behavior but also shaping it when ambiguity poses risks. To this end, we introduce a hierarchical belief model that structures human behavior across coarse discrete intents and fine motion modes, updated via Bayesian inference for interpretable multi-resolution reasoning. On top of this, we develop an active probing strategy that identifies when multimodal ambiguity in human predictions may compromise safety and plans disambiguating actions that both reveal intent and gently steer human decisions toward safer outcomes. Finally, a runtime risk-evaluation layer based on Conditional Value-at-Risk (CVaR) ensures that all probing actions remain within human risk tolerance during influence. Our simulations in lane-merging and unsignaled intersection scenarios demonstrate that our approach achieves higher success rates and shorter completion times compared to existing methods. These results highlight the benefit of coupling belief inference, probing, and risk monitoring, yielding a principled and interpretable framework for planning under uncertainty.

翻译：复杂交通环境下的自动驾驶需要在不确定性中进行推理。常见方法依赖于基于预测的规划或风险感知控制，但这些方法通常被孤立处理，限制了其在交互场景中捕捉行动与推断耦合本质的能力。这一差距在不确定场景中尤为关键，仅对预测做出反应可能导致不安全操作或过度保守行为。我们的核心见解是：安全的交互不仅需要估计人类行为，更需要在模糊性构成风险时主动塑造行为。为此，我们提出了一个分层置信模型，该模型在粗粒度离散意图和细粒度运动模式上构建人类行为框架，并通过贝叶斯推断进行更新，以实现可解释的多分辨率推理。在此基础上，我们开发了一种主动探测策略，能够识别人类预测中的多模态模糊性何时可能危及安全，并规划既能揭示意图又能温和引导人类决策趋向更安全结果的消歧行动。最后，基于条件风险价值（CVaR）的运行时风险评估层确保所有探测行动在施加影响时始终保持在人类风险承受范围内。我们在车道合并和无信号交叉口场景中的仿真实验表明，相较于现有方法，本方法实现了更高的成功率和更短的完成时间。这些结果凸显了耦合置信推断、主动探测与风险监控的优势，为不确定性下的规划提供了一个原则性强且可解释的框架。