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The safety of autonomous driving systems, particularly self-driving vehicles,remains of paramount concern. These systems exhibit affine nonlinear dynamicsand face the challenge of executing predefined control tasks while adhering tostate and input constraints to mitigate risks. However, achieving safetycontrol within the framework of control input constraints, such as collisionavoidance and maintaining system states within secure boundaries, presentschallenges due to limited options. In this study, we introduce a novel approachto address safety concerns by transforming safety conditions into controlconstraints with a relative degree of 1. This transformation is facilitatedthrough the design of control barrier functions, enabling the creation of asafety control system for affine nonlinear networks. Subsequently, we formulatea robust control strategy that incorporates safety protocols and conduct acomprehensive analysis of its stability and reliability. To illustrate theeffectiveness of our approach, we apply it to a specific problem involvingadaptive cruise control. Through simulations, we validate the efficiency of ourmodel in ensuring safety without compromising control performance. Our approachsignifies significant progress in the field, providing a practical solution toenhance safety for autonomous driving systems operating within the context ofaffine nonlinear dynamics.

Enhancing Safety in Nonlinear Systems: Design and Stability Analysis of Adaptive Cruise Control