Reinforcement learning has received high research interest for developing
planning approaches in automated driving. Most prior works consider the
end-to-end planning task that yields direct control commands and rarely deploy
their algorithm to real vehicles. In this work, we propose a method to employ a
trained deep reinforcement learning policy for dedicated high-level behavior
planning. By populating an abstract objective interface, established motion
planning algorithms can be leveraged, which derive smooth and drivable
trajectories. Given the current environment model, we propose to use a built-in
simulator to predict the traffic scene for a given horizon into the future. The
behavior of automated vehicles in mixed traffic is determined by querying the
learned policy. To the best of our knowledge, this work is the first to apply
deep reinforcement learning in this manner, and as such lacks a
state-of-the-art benchmark. Thus, we validate the proposed approach by
comparing an idealistic single-shot plan with cyclic replanning through the
learned policy. Experiments with a real testing vehicle on proving grounds
demonstrate the potential of our approach to shrink the simulation to real
world gap of deep reinforcement learning based planning approaches. Additional
simulative analyses reveal that more complex multi-agent maneuvers can be
managed by employing the cycling replanning approach.

本文提出一种利用深度强化学习的自主驾驶行为规划模型，通过预测未来交通情况，将高层行为规划接口化，并通过循环规划策略进行实验验证。

基于强化学习的行为规划与采样运动规划的自动驾驶集成

Integration of Reinforcement Learning Based Behavior Planning With  Sampling Based Motion Planning for Automated Driving

Learning-based approaches have achieved impressive performance for autonomous
driving and an increasing number of data-driven works are being studied in the
decision-making and planning module. However, the reliability and the stability
of the neural network is still full of challenges. In this paper, we introduce
a hierarchical imitation method including a high-level grid-based behavior
planner and a low-level trajectory planner, which is not only an individual
data-driven driving policy and can also be easily embedded into the rule-based
architecture. We evaluate our method both in closed-loop simulation and real
world driving, and demonstrate the neural network planner has outstanding
performance in complex urban autonomous driving scenarios.

本文介绍了一种分层的模仿学习方法，包括一个高层次基于网格的行为规划器和一个低层次的轨迹规划器来增强神经网络的可靠性和稳定性。该方法可以被嵌入到基于规则的架构中，且在封闭回路仿真和实际驾驶测试中表现卓越，特别适用于复杂的城市自动驾驶场景。

基于分层模仿学习的城市驾驶可解释运动规划器

Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning

We present a connectionist architecture that can learn a model of the
relations between perceptions and actions and use this model for behavior
planning. State representations are learned with a growing self-organizing
layer which is directly coupled to a perception and a motor layer. Knowledge
about possible state transitions is encoded in the lateral connectivity. Motor
signals modulate this lateral connectivity and a dynamic field on the layer
organizes a planning process. All mechanisms are local and adaptation is based
on Hebbian ideas. The model is continuous in the action, perception, and time
domain.

介绍了一种基于 Hebbian 思想的连接主义架构，该架构可以学习感知和动作之间的关系模型，并将该模型用于行为规划。