Safe Neural Control for Non-Affine Control Systems with Differentiable Control Barrier Functions
This work addresses safety in non-affine control systems, such as autonomous driving, by reducing conservativeness and enabling optimal policy learning, though it builds incrementally on prior CBF methods.
The paper tackles safety-critical control for non-affine systems by integrating higher-order control barrier functions into neural ODE-based learning models as differentiable CBFs, enabling safe and less conservative control policies, and demonstrates effectiveness in LiDAR-based autonomous driving with comparisons to existing methods.
This paper addresses the problem of safety-critical control for non-affine control systems. It has been shown that optimizing quadratic costs subject to state and control constraints can be sub-optimally reduced to a sequence of quadratic programs (QPs) by using Control Barrier Functions (CBFs). Our recently proposed High Order CBFs (HOCBFs) can accommodate constraints of arbitrary relative degree. The main challenges in this approach are that it requires affine control dynamics and the solution of the CBF-based QP is sub-optimal since it is solved point-wise. To address these challenges, we incorporate higher-order CBFs into neural ordinary differential equation-based learning models as differentiable CBFs to guarantee safety for non-affine control systems. The differentiable CBFs are trainable in terms of their parameters, and thus, they can address the conservativeness of CBFs such that the system state will not stay unnecessarily far away from safe set boundaries. Moreover, the imitation learning model is capable of learning complex and optimal control policies that are usually intractable online. We illustrate the effectiveness of the proposed framework on LiDAR-based autonomous driving and compare it with existing methods.