  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 https://github.com/wissamkontar.
