Enhancing the performance of a safe controller via supervised learning for truck lateral control
For autonomous vehicle control, this work addresses the trade-off between performance and safety by integrating learning-based controllers with formal safety guarantees.
This paper combines supervised learning with control barrier functions (CBFs) to synthesize controllers for truck lateral control that achieve both good performance and provable safety. In a lane-keeping case study for articulated trucks, the learned controller rarely requires CBF intervention, demonstrating effective performance.
Correct-by-construction techniques, such as control barrier functions (CBFs), can be used to guarantee closed-loop safety by acting as a supervisor of an existing or legacy controller. However, supervisory-control intervention typically compromises the performance of the closed-loop system. On the other hand, machine learning has been used to synthesize controllers that inherit good properties from a training dataset, though safety is typically not guaranteed due to the difficulty of analyzing the associated neural network. In this paper, supervised learning is combined with CBFs to synthesize controllers that enjoy good performance with provable safety. A training set is generated by trajectory optimization that incorporates the CBF constraint for an interesting range of initial conditions of the truck model. A control policy is obtained via supervised learning that maps a feature representing the initial conditions to a parameterized desired trajectory. The learning-based controller is used as the performance controller and a CBF-based supervisory controller guarantees safety. A case study of lane keeping for articulated trucks shows that the controller trained by supervised learning inherits the good performance of the training set and rarely requires intervention by the CBF supervisor