标题： 基于多模态预测的主动探测用于运动规划 显示英文标题

标题： Active Probing with Multimodal Predictions for Motion Planning

摘要： 导航在动态环境中要求自主系统推理其他代理行为的不确定性。 在本文中，我们引入了一个统一的框架，将轨迹规划与多模态预测和主动探测相结合，以在不确定性下增强决策能力。 我们开发了一个新的风险度量标准，通过混合模型无缝整合多模态预测的不确定性。 当这些不确定性遵循高斯混合分布时，我们证明了我们的风险度量标准具有闭式解，并且总是有限的，从而确保了分析上的可处理性。 为了减少预测的模糊性，我们结合了一个主动探测机制，该机制战略性地选择动作以改进对其他代理行为参数的估计，同时处理多模态不确定性。 我们在使用MetaDrive仿真环境的自主导航场景中对我们的框架进行了广泛评估。 结果表明，我们的主动探测方法成功地在具有不确定预测的复杂交通场景中导航。 此外，我们的框架在各种交通代理行为模型中表现出稳健的性能，表明其在现实世界自主导航挑战中的广泛应用潜力。 代码和视频可在https://darshangm.github.io/papers/active-probing-multimodal-predictions/获取。 显示英文摘要

摘要： Navigation in dynamic environments requires autonomous systems to reason about uncertainties in the behavior of other agents. In this paper, we introduce a unified framework that combines trajectory planning with multimodal predictions and active probing to enhance decision-making under uncertainty. We develop a novel risk metric that seamlessly integrates multimodal prediction uncertainties through mixture models. When these uncertainties follow a Gaussian mixture distribution, we prove that our risk metric admits a closed-form solution, and is always finite, thus ensuring analytical tractability. To reduce prediction ambiguity, we incorporate an active probing mechanism that strategically selects actions to improve its estimates of behavioral parameters of other agents, while simultaneously handling multimodal uncertainties. We extensively evaluate our framework in autonomous navigation scenarios using the MetaDrive simulation environment. Results demonstrate that our active probing approach successfully navigates complex traffic scenarios with uncertain predictions. Additionally, our framework shows robust performance across diverse traffic agent behavior models, indicating its broad applicability to real-world autonomous navigation challenges. Code and videos are available at https://darshangm.github.io/papers/active-probing-multimodal-predictions/.