Complex traffic scenes greatly challenge the road safety of automated vehicles (AVs). Recent work only provides an independent perspective from the fundamental modules. This paper integrates the decision-making and path-planning modules to ensure the autonomous driving performance in the high-speed cruising scenario. First, to guarantee deep exploration of the reinforcement learning method, a Bootstrapped deep-Q-Network (BDQN) is proposed to address the adaptive decision-making of AVs. Then, quantifying the multi-performance requirements of AVs under high-speed cruising can be complex. We employ an inverse reinforcement learning (IRL) approach to learn path-planning ability from skilled drivers, generating a reference path for executing lane changes. The simulation results demonstrate the proposed framework can ensure the autonomous cruising performance with safety guarantees.