Driver Interaction Intent Prediction With Dynamic Scene Semantic Fusion for Intelligent Cockpit

Driver interaction intent prediction is one of the core technologies enabling intelligent cockpits to transition from passive response to proactive service. Existing intent prediction methods mainly model for single service functions and are deficient in generalization of cross-functional prediction and generalized temporal prediction models predominantly neglect the spatio-temporal (ST) causal relationships between dynamic evolution of scenes and interaction intents, resulting in compromised prediction efficacy within complex environments. To address the above limitations, we propose a driver interaction intent prediction framework with scene semantic fusion in intelligent cockpits. The methodology involves three key stages: first, extracting temporal patterns from cockpit interaction sequences via LSTM networks, second, converting driving dynamics data into structured scene semantic text through parameter efficient fine-tuning of large language models (LLM), and third, establishing collaborative representations of interaction behavioral features and scene information based on scene-guided attention fusion mechanism to achieve accurate driver interaction intent prediction. Experimental results demonstrate that our method has superior performance compared with other methods, with Precision, Recall, F1-score, and Accuracy values of 0.8416, 0.7842, 0.8045, and 0.7948, respectively. In particular, the introduction of scene semantic information can effectively trace the motivating source of the driver interaction intent generation and effectively enhance the interpretability of the model. This research establishes a technical foundation for implementing personalized proactive services in intelligent cockpits.