Design of a deep learning-based voice recognition and control system for autonomous vehicle cockpits

With the rapid advancement of autonomous driving technology, the Human-Machine Interaction (HMI) systems within vehicle cockpits play a crucial role in enhancing user experience and ensuring driving safety. This paper presents the design of a deep learning-based voice recognition and control system tailored for autonomous vehicle cockpits, aiming to improve speech recognition accuracy and real-time responsiveness. The research encompasses speech signal processing, the design and optimization of deep learning models, multilingual support, and the mapping of voice commands to control actions. By constructing a virtual simulation environment and conducting multi-scenario, multilingual experiments, the results demonstrate that the proposed system outperforms traditional methods in both speech recognition accuracy and response time, significantly enhancing the intelligence and user experience of autonomous vehicle cockpits.