The evolution of AI applications in cockpit systems has been a fascinating journey, from the early integration of voice recognition and facial monitoring functions in vehicles to the widespread adoption of advanced reasoning models like DeepSeek-R1 by automakers. This progression can be divided into three key phases: pre-large model era, post-large model era, and the era of multimodal large language models (LLMs) and reasoning models.
In the pre-large model era, cockpits transitioned from mechanical to electronic systems, incorporating small AI models for tasks such as facial and voice recognition. As technology advanced, the post-large model era saw a significant expansion in the scope and quantity of AI applications, although accuracy and adaptability remained inconsistent.
The latest phase, characterized by the integration of multimodal LLMs and reasoning models, has propelled cockpits into a realm of deep interaction and self-evolution. This shift has enabled features like “linkage interaction,” “multi-modal interaction,” “personalized interaction,” “active interaction,” and “precise interaction.”
For instance, precise interaction involves leveraging inference models to enhance the accuracy of voice recognition and response speed. Multi-modal interaction, on the other hand, combines data from various sources to enable seamless gesture control, facial recognition, eye tracking, and emotional interaction within the cockpit.
Moreover, the concept of self-evolution has become a key feature of modern cockpit AI systems. Through long-term memory, feedback learning, and active cognition, AI agents can construct user profiles, anticipate user requests, and provide personalized recommendations based on user behavior.
The symbiosis of large and small models is another crucial trend in cockpit AI development. While large models handle complex calculations in the background, small models excel at real-time responses for tasks like voice control and gesture recognition. This collaboration ensures an efficient and intelligent cockpit ecosystem that enhances the user experience.
In practical applications, companies like NIO are embracing a two-wheel drive approach, focusing on both large and small models to create a holistic AI system for their vehicles. By leveraging the strengths of both types of models, automakers can deliver cutting-edge AI capabilities that transform the driving experience.
As we look towards the future, the continued advancement of AI in cockpits promises even more innovative features and functionalities, ushering in a new era of intelligent and user-friendly vehicle interactions.
