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.

在与真实世界进行接口的安全关键系统中，决策中的不确定性在机器学习模型中起着关键作用。本研究重点研发了一种使用机器学习框架的车辆横向控制系统，特别采用贝叶斯神经网络（BNN）来处理不确定性量化，该能力使我们能够评估模型预测的置信水平或不确定性。经过模拟数据训练的 BNN 控制器在单个轨道上测试以及其他各种轨道都表现出了适应和有效控制车辆的能力。通过建立置信阈值，我们可以触发手动干预，确保在算法操作超出安全参数范围时控制权可以被剥夺。