  In safety-critical systems that interface with the real world, the role of
uncertainty in decision-making is pivotal, particularly in the context of
machine learning models. For the secure functioning of Cyber-Physical Systems
(CPS), it is imperative to manage such uncertainty adeptly. In this research,
we focus on the development of a vehicle's lateral control system using a
machine learning framework. Specifically, we employ a Bayesian Neural Network
(BNN), a probabilistic learning model, to address uncertainty quantification.
This capability allows us to gauge the level of confidence or uncertainty in
the model's predictions. The BNN based controller is trained using simulated
data gathered from the vehicle traversing a single track and subsequently
tested on various other tracks. We want to share two significant results:
firstly, the trained model demonstrates the ability to adapt and effectively
control the vehicle on multiple similar tracks. Secondly, the quantification of
prediction confidence integrated into the controller serves as an early-warning
system, signaling when the algorithm lacks confidence in its predictions and is
therefore susceptible to failure. By establishing a confidence threshold, we
can trigger manual intervention, ensuring that control is relinquished from the
algorithm when it operates outside of safe parameters.
