Exploration of natural language processing applications in autonomous driving interaction systems based on transformer architecture

The rapid development of autonomous driving systems has brought increasing attention to human-machine interaction, particularly in speech-based command recognition and execution. However, traditional models such as RNN and LSTM often face limitations in processing long-sequence data, understanding complex contexts, and ensuring real-time responses under noisy conditions. This study addresses these challenges by introducing a Transformer-based framework designed to enhance the speech interaction capabilities of autonomous vehicles. The proposed framework employs advanced selfattention mechanisms and parallel processing to capture long-range dependencies and contextual information effectively. Experiments were conducted using a combination of publicly available English speech command datasets and synthesized Chinese datasets, simulating various real-world scenarios. Results demonstrate that the Transformer model significantly outperforms traditional models, achieving a speech recognition accuracy of 94.3%, an average response time of 120 milliseconds, and a user satisfaction rate of 89.5%. These findings highlight the model’s robustness, efficiency, and applicability in diverse and dynamic environments. This study makes several contributions to the field of autonomous driving.