The new AI model, named Drive GPT-4, is capable of performing end-to-end autonomous driving by generating and understanding natural language commands. This system can not only follow instructions like turning or parking but also answer questions about its actions and decisions.

Drive GPT-4 integrates computer vision and natural language processing, enabling it to process and reason with both text and non-text data, such as images and videos, in real time. It uses a vision encoder and a large language model (LLM), connected by an attention mechanism, to align visual and textual modalities and manage autonomous driving tasks.

The model is trained using a method called visual instruction tuning, which involves using machine-generated data to follow specific instructions and responses.

Drive GPT-4 has demonstrated impressive performance, outperforming other methods in various metrics and datasets, and showcasing robustness and generalization ability in different driving environments and scenarios. It can follow complex instructions and answer diverse questions, making the autonomous driving experience more interactive, clear, and enjoyable.

It seems like it has some quite good abilities through its integration of computer vision and natural language processing:

• It can comprehend and execute natural language instructions related to driving, such as "turn left at the next intersection" or "park near the blue building."
• It can answer questions about its actions and decisions in natural language, such as explaining why it slowed down or providing information about the speed limit.
• It can process and reason with both text and non-text data, such as images and videos, in real time, which is vital for understanding and navigating its surroundings.
• It can identify and understand visual elements crucial for safe driving, such as traffic signs, lane markings, road conditions, and nearby vehicles or objects.
• It can communicate clearly with passengers and potentially other drivers, explaining its actions, providing feedback, and answering questions in a natural and understandable manner.
• It uses a vision encoder and a large language model, connected by an attention mechanism, to align visual and textual modalities, enabling it to perform tasks that require understanding and integrating both visual and textual information.
• It is trained using a method that involves creating synthetic instructions and responses using images or videos from driving scenes, which helps it to understand and follow specific instructions related to driving scenarios.
• It can directly determine and execute low-level control actions like steering, accelerating, and decelerating based on natural language instructions and visual cues.
• It can follow complex and multi-step driving instructions and navigate accordingly.

Do you think that the Drive GPT4 is the beginning of the end for human driving?