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This paper describes a framework for learning Automated Vehicles (AVs) drivermodels via knowledge sharing between vehicles and personalization. The innatevariability in the transportation system makes it exceptionally challenging toexpose AVs to all possible driving scenarios during empirical experimentationor testing. Consequently, AVs could be blind to certain encounters that aredeemed detrimental to their safe and efficient operation. It is then criticalto share knowledge across AVs that increase exposure to driving scenariosoccurring in the real world. This paper explores a method to collaborativelytrain a driver model by sharing knowledge and borrowing strength acrossvehicles while retaining a personalized model tailored to the vehicle's uniqueconditions and properties. Our model brings a federated learning approach tocollaborate between multiple vehicles while circumventing the need to share rawdata between them. We showcase our method's performance in experimentalsimulations. Such an approach to learning finds several applications acrosstransportation engineering including intelligent transportation systems,traffic management, and vehicle-to-vehicle communication. Code and sampledataset are made available at the project page https://github.com/wissamkontar.

Learning Driver Models for Automated Vehicles via Knowledge Sharing and Personalization