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.

翻译：在与人世交互的安全关键系统中，不确定性在决策中的作用至关重要，尤其是在机器学习模型的背景下。为确保信息物理系统（CPS）的安全运行，必须熟练管理此类不确定性。本研究聚焦于利用机器学习框架开发车辆横向控制系统。具体而言，我们采用贝叶斯神经网络（BNN）这一概率学习模型来进行不确定性量化，从而能够评估模型预测的置信度或不确定性水平。基于BNN的控制器使用车辆在单一赛道上行驶时收集的模拟数据进行训练，随后在其他多种赛道上进行测试。我们分享两项重要发现：首先，训练后的模型展现出适应并有效控制车辆在多个类似赛道上行驶的能力；其次，集成到控制器中的预测置信度量化可作为早期预警系统，当算法对其预测缺乏信心并因此易发生故障时发出信号。通过设定置信度阈值，我们可触发人工干预，确保在算法超出安全参数范围时，控制权从算法中释放。