The vibro-acoustic performance of a vehicle is a critical factor in customer perception of quality and comfort, yet optimizing for Noise, Vibration, and Harshness (NVH)—specifically road noise—presents a persistent challenge in the modern automotive development cycle. While advanced Finite Element Method (FEM) analysis is essential, the increasing complexity and volume of CAE simulation data often overwhelm manual interpretation, potentially leading to prolonged development times or compromises in final comfort quality. To address these challenges, this paper introduces the application of CDH/ACE (Autonomous Computational Experiments), a framework that integrates conventional CAE simulation workflows with advanced machine learning in an iterative, cyclic process. This creates an exceptionally user-friendly and self-correcting system that autonomously defines, performs, and learns from computational experiments. By leveraging machine learning algorithms to build robust predictive models from simulation data, the framework intelligently guides design exploration to achieve complex engineering objectives such as design of experiments, multi-objective optimization, and robustness analysis. We demonstrate this methodology through a comprehensive full-vehicle road noise optimization study, detailing the process of defining experiment parameters and configuring acoustic targets within the autonomous learning cycle. The results highlight the effectiveness of this highly automated and intuitive workflow, showing significant reductions in road noise and vehicle mass alongside a substantial decrease in manual engineering effort. Finally, the paper presents the tangible benefits of this approach, assessing current advantages and limitations while providing an outlook on the future application of autonomous, machine-learning-driven methodologies in accelerating modern vehicle development.