With the integration of Autonomous Vehicles (AVs) into our transportation systems, their harmonious coexistence with Human-driven Vehicles (HVs) in mixed traffic settings becomes a crucial focus of research. A vital component of this coexistence is the capability of AVs to mimic human-like interaction intentions within the traffic environment. To address this, we propose a novel framework for Unprotected left-turn trajectory planning for AVs, aiming to replicate human driving patterns and facilitate effective communication of social intent. Our framework comprises three stages: trajectory generation, evaluation, and selection. In the generation stage, we use real human-driving trajectory data to define constraints for an anticipated trajectory space, generating candidate motion trajectories that embody intent expression. The evaluation stage employs maximum entropy inverse reinforcement learning (ME-IRL) to assess human trajectory preferences, considering factors such as traffic efficiency, driving comfort, and interactive safety. In the selection stage, we apply a Boltzmann distribution-based method to assign rewards and probabilities to candidate trajectories, thereby facilitating human-like decision-making. We conduct validation of our proposed framework using a real trajectory dataset and perform a comparative analysis against several baseline methods. The results demonstrate the superior performance of our framework in terms of human-likeness, intent expression capability, and computational efficiency. Limited by the length of the text, more details of this research can be found at https://shorturl.at/jqu35

翻译：随着自动驾驶车辆（AVs）融入交通系统，其与人类驾驶车辆（HVs）在混合交通环境中的和谐共存成为研究的关键焦点。这种共存的核心在于AVs在交通环境中模拟类似人类的交互意图能力。为此，我们提出了一种新型的无保护左转轨迹规划框架，旨在复现人类驾驶模式并促进社交意图的有效沟通。该框架包含三个阶段：轨迹生成、评估和选择。在生成阶段，我们利用真实人类驾驶轨迹数据定义预期轨迹空间的约束，生成体现意图表达的候选运动轨迹。评估阶段采用最大熵逆强化学习（ME-IRL）评估人类轨迹偏好，综合考虑交通效率、驾驶舒适度和交互安全性等因素。在选择阶段，我们应用基于玻尔兹曼分布的方法为候选轨迹分配奖励和概率，从而促进类似人类的决策过程。我们利用真实轨迹数据集对所提框架进行验证，并与多种基线方法进行对比分析。结果表明，该框架在人类相似度、意图表达能力及计算效率方面均表现出优越性能。受限于文本长度，本研究的更多细节可参见 https://shorturl.at/jqu35