Artificial intelligence (AI) is the core engine driving the development of intelligent transportation and autonomous driving, and its integration path is gradually deepening. Initially, the application of AI in intelligent transportation systems was mostly based on historical data for traffic flow prediction and traffic signal optimization, which belongs to "system-level AI". Next, AI began to drive autonomous vehicles, achieving real-time perception and decision-making in complex dynamic environments through sensor fusion and deep learning, which is "mono-level AI".
The key to future success lies in the seamless integration of these two elements. A unified AI framework will be able to process both macro-level data from urban infrastructure and micro-level data from vehicles. This means that AI can not only plan optimal routes for individual vehicles but also perform "cooperative right-of-way allocation" based on a holistic perspective, for example…