MIT and Stanford will work with Toyota to develop better self driving cars

MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) announced a new $25 million research center funded by Toyota to further the development of autonomous vehicle technologies, with the goal of reducing traffic casualties and potentially even developing a vehicle incapable of getting into an accident.

Announced at a press conference in California, the Toyota-CSAIL Joint Research Center will be part of a combined $50 million that Toyota has committed to dual centers at MIT and Stanford University to advance the state of autonomous systems.

CSAIL researchers plan to start by exploring a new alternative approach, in which the human driver pays attention at all times with an autonomous system that is there to jump in to save the driver in the event of an unavoidable accident. This type of system could not only improve safety by reducing the number of accidents, but could also enhance the overall driving experience, Rus explains. She envisions creating a system that could prevent collisions and also provide drivers with assistance navigating tricky situations; support a tired driver by watching for unexpected dangers and diversions; and even offer helpful tips such as letting the driver know she is out of milk at home and planning a new route home that allows the driver to swing by the grocery store.

“A highly advanced system like this would be a major advance in the field of autonomy and an important step on the way to creating a safer world for drivers,” Rus says.

Research at MIT will focus on “advanced architectures” that will let cars perceive, understand, and interpret their surroundings. That will be led by Daniela Rus, who recently worked on self-driving golf carts and the laser, or LIDAR, sensors autonomous vehicles typically use to map the world around them.

Stanford will concentrate on computer vision and machine learning.

Taking on the “moonshot” challenges

Research at the new center will be aimed at improving vehicular transportation by advancing the science of autonomous systems. Researchers will tackle challenges integral to the development of advanced automated vehicle systems, including building new tools for collecting and analyzing navigation data with the goal of learning from human driving; creating perception and decision-making systems for safe navigation; developing predictive models that can anticipate the behavior of humans, vehicles, and the larger environment; inventing state-of-the-art tools to handle congestion and high-speed driving in challenging situations including adverse weather; improving machine-vision algorithms used to detect and classify objects; and creating more intelligent user interfaces.