This paper presents an overview of the state of the art for safety-critical optimal control of autonomous systems.Optimal control methods are well studied,but become computationally infeasible for real-time applications when there are multiple hard safety constraints involved.To guarantee such safety constraints,it has been shown that optimizing quadratic costs while stabilizing affine control systems to desired(sets of)states subject to state and control constraints can be reduced to a sequence of Quadratic Programs(QPs)by using Control Barrier Functions(CBFs)and Control Lyapunov Functions(CLFs).The CBF method is computationally efficient,and can easily guarantee the satisfaction of nonlinear constraints for nonlinear systems,but its wide applicability still faces several challenges.First,safety is hard to guarantee for systems with high relative degree,and the above mentioned QPs can easily be infeasible if tight or time-varying control bounds are involved.The resulting solution is also sub-optimal due to its myopic solving approach.Finally,this method works conditioned on the system dynamics being accurately identified.The authors discuss recent solutions to these issues and then present a framework that combines Optimal Control with CBFs,hence termed OCBF,to obtain near-optimal solutions while guaranteeing safety constraints even in the presence of noisy dynamics.An application of the OCBF approach is included for autonomous vehicles in traffic networks.