Machine Learning for the Next Generation of Digital Cockpit
As the cars are becoming self-driving, the in-cabin experiences are also being revolutionized. Equipped with machine learning engine, the car will know the driver personally, know their commute preferences, and even know their families. The cross-vehicle in-cabin knowledge discovery can make the cars capable of providing real-time assistance, such as point of interest recommendation, trip visualization and monitoring, and just-in-context services. We will discuss several data representation and machine learning techniques and system architecture towards the driver behavior learning and real time pattern discovery. It is also worth to know that these fundamental techniques are common between outside facing autonomous driving and inside facing context learning.
Guan Wang is a staff machine learning engineer at NIO (NextEV). He is a hackathon champion and entrepreneur. He is the first AI person at NIO, where he is the major designer, architect, and engineer for a range of NIO’s in-house AI products, especially w.r.t vehicle perception. Before NIO, he was the first person to bring machine learning to the business analytics department at LinkedIn and helped the team grew from 3 people to about 100 people in a short time. Guan Wang holds a Ph.D. in Computer Science specialized in machine learning and data mining from University of Illinois at Chicago with Prof. Philip S. Yu.