Toyota to open another US research facility for self-driving tech image

Toyota announced it would establish its third US research lab in Ann Arbor, Michigan in June to speed up the development of autonomous cars.

The world’s biggest automaker by sales opened in January an artificial intelligence centre in Palo Alto working with Stanford University and another one in Cambridge where it collaborates with the Massachusetts Institute of Technology. Toyota has now announced the third research lab in Ann Arbor, near the University of Michigan campus, where it would fund research in artificial intelligence, robotics and materials science. These facilities are part of automaker’s commitment to invest around 1 billion dollars in AI systems to accelerate the development of autonomous vehicles. The company said that a group of about 15 team members would transfer to the new lab when it opened.

Although the focus of each of the three strategically located facilities will be broad, each will feature a different core discipline. Ann Arbor will focus primarily on fully autonomous (chauffeured) driving, Palo Alto will work on “guardian angel” driving, where the driver is always engaged but the vehicle assists as needed, while Cambridge will dedicate a large portion of its work to simulation and deep learning.

Toyota Research Institute has four initial targets, according to a company statement. First, the researchers will focus on the improvement of safety technologies in cars, second, they will work to offer access to cars to those who otherwise cannot drive, including seniors and those with special needs, and thirdly, the facilities will help translate Toyota’s expertise in creating products for outdoor mobility into products for indoor mobility. Finally, the Institute will accelerate scientific discovery by applying techniques from artificial intelligence and machine learning, particularly in the area of materials science. Using computation and machine learning, it hopes to speed up scientific discovery in this area, lowering costs and improving the performance of future mobility systems.