Artificial intelligence is on the rise, and with the investment of giants such as Google, Microsoft and Facebook, deep learning is surpassing machine learning. Then, now that rail transit encounters AI , what kind of scenario will the future rail transit be? What core technologies should enterprises master in order to continue to develop ? Huaqiang Wisdom Network Xiaobian will think about it today.
What is the future rail transit when AI is injected into rail transit?
Through the development of big data, new high-performance computing architecture and deep learning technology, combined with computer vision technology, language technology, natural language processing and other artificial intelligence technologies, the future rail transit will realize learning, reasoning, thinking, and AI under the blessing of AI. Intelligent behaviors and abilities such as planning.
In the field of rail transit, it is necessary to consider artificial intelligence embedded in the corresponding links on the basis of data, improve operational efficiency, improve product quality, and ultimately improve customer experience while reducing production costs.
In the future rail transit, the integration of technology can be completed by the vehicle manufacturer to complete the final integration and assembly work, forming a more integrated and integrated AI train control system solution than the current subsystems. While standing in the whole industry, the coordination of software and hardware is no longer a problem, and at the same time it presents an ecological and network-like relationship, and the vertical connection between enterprises is constantly close.
What core technologies should be mastered in enterprise development in future rail transit?
Conceptually, AI embedded in all walks of life experience three levels of perception, decision, and execution, allowing the entire system to operate according to the super brain. In terms of the industry specificity of rail transit, AI enters various application scenarios of rail transit, and the imagination space is large.
Smart train. Computer vision and radar technology continue to enrich the sensing layer to achieve comprehensive obstacle detection. According to the data processing, the decision is made to make the normal driving and the emergency situation processing. How to connect with other subjects, how to update data, systems, maps... These are the virgin land waiting to be explored. To some extent, you can refer to unmanned driving to understand.
Environmental awareness. In a series of future rail transit systems, environmental perception is the most fundamental. The coordination of sensors such as positioning, radar and vision is visualized by means of graphs. The algorithm can be extracted and processed to form a driving situation map, which provides an important basis for later decision-making. But in this information, enterprises need to leapfrog information collection, information processing and visual expression. After that, algorithm extraction is a difficult point to overcome.
Big data operation and maintenance. The transportation system is a field where people and data are concentrated. In the future artificial intelligence train control system, personal travel information, as well as surrounding environment, traffic conditions, and rail transit information will be linked. Big data operation and maintenance is an arduous and important part.
Human-computer interaction. How to make the machine full of "human touch" is the essence of human-computer interaction. Speech recognition, natural language processing, AR augmented reality, visualization... are all in the process of integrating AI technology into smart devices to make machines more understandable.
Intelligent scheduling. The so-called scheduling is "I am a revolutionary brick where to move." Utilize unified management to coordinate public resources and provide on-demand services with an open access platform.
In the view of Huaqiang Wisdom Network Xiaobian, the entry of AI into the rail transit industry is bound to subvert the original way of thinking. Under the current development status of product intelligent embedding, there is still a long way to go before.