Learning Driver Models for Automated Vehicles via Knowledge Sharing and Personalization

TL;DR

This paper proposes a federated learning framework for personalized driver models in automated vehicles, enabling knowledge sharing across vehicles without sharing raw data, to improve safety and efficiency.

Contribution

It introduces a novel federated learning approach for collaborative and personalized driver modeling in automated vehicles, addressing variability and data privacy concerns.

Findings

Effective knowledge sharing improves driver model accuracy.

Personalization enhances model performance for individual vehicles.

Simulation results demonstrate the method's potential benefits.

Abstract

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.