This paper addresses the trajectory planning problem for automated vehicle on-ramp highway merging. To tackle this challenge, we extend our previous work on trajectory planning at unsignalized intersections using Partially Observable Markov Decision Processes (POMDPs). The method utilizes the Adaptive Belief Tree (ABT) algorithm, an approximate sampling-based approach to solve POMDPs efficiently. We outline the POMDP formulation process, beginning with discretizing the highway topology to reduce problem complexity. Additionally, we describe the dynamics and measurement models used to predict future states and establish the relationship between available noisy measurements and predictions. Building on our previous work, the dynamics model is expanded to account for lateral movements necessary for lane changes during the merging process. We also define the reward function, which serves as the primary mechanism for specifying the desired behavior of the automated vehicle, combining multiple goals such as avoiding collisions or maintaining appropriate velocity. Our simulation results, conducted on three scenarios based on real-life traffic data from German highways, demonstrate the method's ability to generate safe, collision-free, and efficient merging trajectories. This work shows the versatility of this POMDP-based approach in tackling various automated driving problems.

翻译：本文针对自动驾驶车辆在高速公路匝道汇入场景中的轨迹规划问题展开研究。为应对这一挑战，我们在先前基于部分可观测马尔可夫决策过程（POMDP）的无信号交叉口轨迹规划工作基础上进行了扩展。该方法采用自适应置信树（ABT）算法——一种高效的近似采样式POMDP求解方法。我们系统阐述了POMDP建模过程：首先通过离散化高速公路拓扑结构以降低问题复杂度；继而详细说明了用于预测未来状态的动力学模型，以及建立带噪声观测数据与预测值关联关系的测量模型。在先前工作基础上，动力学模型进一步扩展以涵盖汇入过程中换道所需的横向运动。我们还定义了奖励函数，该函数作为规范自动驾驶车辆预期行为的主要机制，融合了避免碰撞、保持合理车速等多重目标。基于德国高速公路实际交通数据构建的三种场景仿真结果表明，本方法能够生成安全、无碰撞且高效的汇入轨迹。本研究证明了这种基于POMDP的方法在解决各类自动驾驶问题方面具有广泛适用性。