One of the key factors determining whether autonomous vehicles (AVs) can be seamlessly integrated into existing traffic systems is their ability to interact smoothly and efficiently with human drivers and communicate their intentions. While many studies have focused on enhancing AVs' human-like interaction and communication capabilities at the behavioral decision-making level, a significant gap remains between the actual motion trajectories of AVs and the psychological expectations of human drivers. This discrepancy can seriously affect the safety and efficiency of AV-HV (Autonomous Vehicle-Human Vehicle) interactions. To address these challenges, we propose a motion planning method for AVs that incorporates implicit intention expression. First, we construct a trajectory space constraint based on human implicit intention priors, compressing and pruning the trajectory space to generate candidate motion trajectories that consider intention expression. We then apply maximum entropy inverse reinforcement learning to learn and estimate human trajectory preferences, constructing a reward function that represents the cognitive characteristics of drivers. Finally, using a Boltzmann distribution, we establish a probabilistic distribution of candidate trajectories based on the reward obtained, selecting human-like trajectory actions. We validated our approach on a real trajectory dataset and compared it with several baseline methods. The results demonstrate that our method excels in human-likeness, intention expression capability, and computational efficiency.

翻译：决定自动驾驶车辆能否无缝融入现有交通系统的关键因素之一，是其与人类驾驶员顺畅高效交互并传达意图的能力。尽管许多研究致力于在行为决策层面提升自动驾驶车辆的人性化交互与沟通能力，但自动驾驶车辆的实际运动轨迹与人类驾驶员的心理预期之间仍存在显著差距。这种差异会严重影响自动驾驶车辆与人类驾驶车辆交互的安全性和效率。为应对这些挑战，我们提出一种融合隐式意图表达的自动驾驶车辆运动规划方法。首先，我们基于人类隐式意图先验构建轨迹空间约束，通过对轨迹空间的压缩与剪枝，生成考虑意图表达的候选运动轨迹。随后，应用最大熵逆强化学习来学习并估计人类轨迹偏好，构建表征驾驶员认知特性的奖励函数。最后，利用玻尔兹曼分布，基于所获奖励建立候选轨迹的概率分布，从而选择类人的轨迹动作。我们在真实轨迹数据集上验证了所提方法，并与多种基线方法进行了比较。结果表明，我们的方法在类人性、意图表达能力及计算效率方面均表现优异。