This paper describes a framework for learning Automated Vehicles (AVs) driver
models via knowledge sharing between vehicles and personalization. The innate
variability in the transportation system makes it exceptionally challenging to
expose AVs to all possible driving scenarios during empirical experimentation
or testing. Consequently, AVs could be blind to certain encounters that are
deemed detrimental to their safe and efficient operation. It is then critical
to share knowledge across AVs that increase exposure to driving scenarios
occurring in the real world. This paper explores a method to collaboratively
train a driver model by sharing knowledge and borrowing strength across
vehicles while retaining a personalized model tailored to the vehicle's unique
conditions and properties. Our model brings a federated learning approach to
collaborate between multiple vehicles while circumventing the need to share raw
data between them. We showcase our method's performance in experimental
simulations. Such an approach to learning finds several applications across
transportation engineering including intelligent transportation systems,
traffic management, and vehicle-to-vehicle communication. Code and sample
dataset are made available at the project page this https URL

使用知识分享和个性化的方式描述了一种学习自动驾驶车辆（AVs）驾驶模型的框架，该模型在多个车辆之间进行知识分享，以提高其在真实世界中驾驶场景的暴露度。该方法通过共同训练一个驾驶模型，同时保留针对每个车辆独特条件和特性的个性化模型，实现了多个车辆之间的协作而无需共享原始数据。该方法在实验模拟中展示了出色的性能，适用于智能交通系统、交通管理和车辆之间的通信等多个交通工程应用领域。

通过知识共享和个性化学习自动驾驶车辆的驾驶员模型

Learning Driver Models for Automated Vehicles via Knowledge Sharing and  Personalization

Driving in dense traffic with human and autonomous drivers is a challenging
task that requires high level planning and reasoning along with the ability to
react quickly to changes in a dynamic environment. In this study, we propose a
hierarchical learning approach that uses learned motion primitives as actions.
Motion primitives are obtained using unsupervised skill discovery without a
predetermined reward function, allowing them to be reused in different
scenarios. This can reduce the total training time for applications that need
to obtain multiple models with varying behavior. Simulation results demonstrate
that the proposed approach yields driver models that achieve higher performance
with less training compared to baseline reinforcement learning methods.

本研究提出了一种层次学习方法，该方法使用未经预设奖励功能的无监督技能发现获取运动原语作为动作，可减少基于惩罚奖励机制的强化学习方法的总训练时间，该方法通过模拟实验表明其产生的驾驶员模型能够更快地展现高性能。

用无监督技能发现进行分层学习，用于高速公路合并应用

Hierarchical Learning with Unsupervised Skill Discovery for Highway Merging Applications

In this work, we propose a method for learning driver models that account for
variables that cannot be observed directly. When trained on a synthetic
dataset, our models are able to learn encodings for vehicle trajectories that
distinguish between four distinct classes of driver behavior. Such encodings
are learned without any knowledge of the number of driver classes or any
objective that directly requires the models to learn encodings for each class.
We show that driving policies trained with knowledge of latent variables are
more effective than baseline methods at imitating the driver behavior that they
are trained to replicate. Furthermore, we demonstrate that the actions chosen
by our policy are heavily influenced by the latent variable settings that are
provided to them.

本研究提出了一种基于学习驾驶员模型的方法，该模型能够考虑不能直接观测到的变量，并学习区分四类不同驾驶员行为的编码。通过该方法，得到的驾驶策略在模拟真实驾驶行为方面比基准方法更为有效，并且策略选择的行为明显受到所提供的潜变量设置的影响。