让自动驾驶车在变道时实时感知周围车辆不确定性，更安全高效决策。
Uncertainty-Aware Decision-Making and Planning for Autonomous Forced Merging
- 在线估算周围车辆加速度范围，动态捕捉运动不确定性
- 预测未来轨迹占位区域，提升路径规划的安全性与适应性
- 适合复杂变道场景，尤其对安全要求高的自动驾驶系统
本文提出一种考虑周围车辆运动不确定性的自主车辆强制变道决策与规划方法。通过在线估计周围车辆的加速度边界，动态捕捉其运动不确定性，实现快速而响应式的环境理解。基于估计的边界，预测未来时间窗内周围车辆的非保守前向占位区域，并将其融入决策与路径规划过程，从而增强所生成参考轨迹的鲁棒性与安全性。该方法在具有挑战性的强制变道场景中表现优异，通过与多种替代方案对比验证了其有效性。
原文摘要 · Abstract (English)
In this paper, we develop an uncertainty-aware decision-making and motion-planning method for an autonomous ego vehicle in forced merging scenarios, considering the motion uncertainty of surrounding vehicles. The method dynamically captures the uncertainty of surrounding vehicles by online estimation of their acceleration bounds, enabling a reactive but rapid understanding of the uncertainty characteristics of the surrounding vehicles. By leveraging these estimated bounds, a non-conservative forward occupancy of surrounding vehicles is predicted over a horizon, which is incorporated in both the decision-making process and the motion-planning strategy, to enhance the resilience and safety of the planned reference trajectory. The method successfully fulfills the tasks in challenging forced merging scenarios, and the properties are illustrated by comparison with several alternative approaches.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。