  The safety of autonomous driving systems, particularly self-driving vehicles,
remains of paramount concern. These systems exhibit affine nonlinear dynamics
and face the challenge of executing predefined control tasks while adhering to
state and input constraints to mitigate risks. However, achieving safety
control within the framework of control input constraints, such as collision
avoidance and maintaining system states within secure boundaries, presents
challenges due to limited options. In this study, we introduce a novel approach
to address safety concerns by transforming safety conditions into control
constraints with a relative degree of 1. This transformation is facilitated
through the design of control barrier functions, enabling the creation of a
safety control system for affine nonlinear networks. Subsequently, we formulate
a robust control strategy that incorporates safety protocols and conduct a
comprehensive analysis of its stability and reliability. To illustrate the
effectiveness of our approach, we apply it to a specific problem involving
adaptive cruise control. Through simulations, we validate the efficiency of our
model in ensuring safety without compromising control performance. Our approach
signifies significant progress in the field, providing a practical solution to
enhance safety for autonomous driving systems operating within the context of
affine nonlinear dynamics.
