When autonomous vehicles still struggle to solve challenging situations during on-road driving, humans have long mastered the essence of driving with efficient transferable and adaptable driving capability. By mimicking humans' cognition model and semantic understanding during driving, we present HATN, a hierarchical framework to generate high-quality driving behaviors in multi-agent dense-traffic environments. Our method hierarchically consists of a high-level intention identification and low-level action generation policy. With the semantic sub-task definition and generic state representation, the hierarchical framework is transferable across different driving scenarios. Besides, our model is also able to capture variations of driving behaviors among individuals and scenarios by an online adaptation module. We demonstrate our algorithms in the task of trajectory prediction for real traffic data at intersections and roundabouts, where we conducted extensive studies of the proposed method and demonstrated how our method outperformed other methods in terms of prediction accuracy and transferability.

翻译：当自主车辆仍然在努力解决公路驾驶期间的棘手情况时,人类早就掌握了驾驶的精髓,具有高效的可转移和可调整驾驶能力。通过模仿人的认知模型和驾驶过程中的语义理解,我们展示了HATN,这是一个在多剂密集交通环境中产生高质量驾驶行为的等级框架。我们的方法按等级分级由高层次的意向识别和低层次的行动生成政策组成。有了语义的子任务定义和通用的国家代表制,等级框架可以跨越不同的驾驶方案。此外,我们的模型还能够通过在线适应模块捕捉个人驾驶行为和情景的变异。我们展示了我们在交叉点和环绕路实际交通数据的轨迹预测任务中的算法,我们在那里对拟议方法进行了广泛的研究,并展示了我们的方法在预测准确性和可转移性方面如何优于其他方法。