This paper investigates the problem of trajectory planning for autonomous vehicles at unsignalized intersections, specifically focusing on scenarios where the vehicle lacks the right of way and yet must cross safely. To address this issue, we have employed a method based on the Partially Observable Markov Decision Processes (POMDPs) framework designed for planning under uncertainty. The method utilizes the Adaptive Belief Tree (ABT) algorithm as an approximate solver for the POMDPs. We outline the POMDP formulation, beginning with discretizing the intersection's topology. Additionally, we present a dynamics model for the prediction of the evolving states of vehicles, such as their position and velocity. Using an observation model, we also describe the connection of those states with the imperfect (noisy) available measurements. Our results confirmed that the method is able to plan collision-free trajectories in a series of simulations utilizing real-world traffic data from aerial footage of two distinct intersections. Furthermore, we studied the impact of parameter adjustments of the ABT algorithm on the method's performance. This provides guidance in determining reasonable parameter settings, which is valuable for future method applications.

翻译：本文研究了自动驾驶车辆在无信号交叉口处的轨迹规划问题，特别关注车辆不享有通行权但仍需安全通过交叉口的场景。为解决该问题，我们采用了一种基于部分可观测马尔可夫决策过程（POMDP）框架的方法，该框架专为不确定性条件下的规划而设计。该方法采用自适应信念树（ABT）算法作为POMDP的近似求解器。我们首先通过交叉口拓扑结构的离散化阐述了POMDP的建模过程，并提出了用于预测车辆动态状态（如位置与速度）演化的动力学模型。借助观测模型，我们还描述了这些状态与不完善（含噪声）可用测量数据之间的关联。基于两处不同交叉口航拍视频的真实交通数据，我们在系列仿真中验证了该方法能够规划出无碰撞轨迹。此外，我们研究了ABT算法参数调整对方法性能的影响，这为确定合理的参数设置提供了指导，对未来的方法应用具有重要价值。