Autonomous driving systems have a pipeline of perception, decision, planning, and control. The decision module processes information from the perception module and directs the execution of downstream planning and control modules. On the other hand, the recent success of deep learning suggests that this pipeline could be replaced by end-to-end neural control policies, however, safety cannot be well guaranteed for the data-driven neural networks. In this work, we propose a hybrid framework to learn neural decisions in the classical modular pipeline through end-to-end imitation learning. This hybrid framework can preserve the merits of the classical pipeline such as the strict enforcement of physical and logical constraints while learning complex driving decisions from data. To circumvent the ambiguous annotation of human driving decisions, our method learns high-level driving decisions by imitating low-level control behaviors. We show in the simulation experiments that our modular driving agent can generalize its driving decision and control to various complex scenarios where the rule-based programs fail. It can also generate smoother and safer driving trajectories than end-to-end neural policies.

翻译：自主驱动系统有感知、决定、规划和控制管道。决定模块处理来自感知模块的信息,指导下游规划和控制模块的执行。另一方面,最近深层次学习的成功表明,这一管道可以由端到端神经控制政策取代,然而,数据驱动神经网络的安全不能很好地得到保障。在这项工作中,我们提议了一个混合框架,通过端到端的模拟学习,在经典模块管道中学习神经决定。这一混合框架可以保留传统管道的优点,例如严格实施物理和逻辑限制,同时从数据中学习复杂的驱动决定。为绕过对驱动决定的模糊注解,我们的方法通过模仿低级控制行为来学习高层次驾驶决定。我们在模拟实验中显示,我们的模块驱动器可以将其驾驶决定和控制概括到基于规则的方案失败的各种复杂情景。它还可以产生比端到端神经政策更滑滑、更安全的驾驶轨迹。