Programming safety into self-driving cars: Better AI algorithms for semi-autonomous vehicles

Date:
February 4, 2015
Source:
National Science Foundation
Summary:
For decades, researchers in artificial intelligence, or AI, worked on specialized problems, developing theoretical concepts and workable algorithms for various aspects of the field. Computer vision, planning and reasoning experts all struggled independently in areas that many thought would be easy to solve, but which proved incredibly difficult.



For decades, researchers in artificial intelligence, or AI, worked on specialized problems, developing theoretical concepts and workable algorithms for various aspects of the field. Computer vision, planning and reasoning experts all struggled independently in areas that many thought would be easy to solve, but which proved incredibly difficult.
Related Articles
Computer vision
Artificial intelligence
Bioinformatics
Scientific visualization
Quantum computer
Robot
However, in recent years, as the individual aspects of artificial intelligence matured, researchers began bringing the pieces together, leading to amazing displays of high-level intelligence: from IBM's Watson to the recent poker playing champion to the ability of AI to recognize cats on the internet.
These advances were on display this week at the 29th conference of the Association for the Advancement of Artificial Intelligence (AAAI) in Austin, Texas, where interdisciplinary and applied research were prevalent, according to Shlomo Zilberstein, the conference committee chair and co-author on three papers at the conference.
Zilberstein studies the way artificial agents plan their future actions, particularly when working semi-autonomously--that is to say in conjunction with people or other devices.
Examples of semi-autonomous systems include co-robots working with humans in manufacturing, search-and-rescue robots that can be managed by humans working remotely and "driverless" cars. It is the latter topic that has particularly piqued Zilberstein's interest in recent years.
The marketing campaigns of leading auto manufacturers have presented a vision of the future where the passenger (formerly known as the driver) can check his or her email, chat with friends or even sleep while shuttling between home and the office. Some prototype vehicles included seats that swivel back to create an interior living room, or as in the case of Google's driverless car, a design with no steering wheel or brakes.
Except in rare cases, it's not clear to Zilberstein that this vision for the vehicles of the near future is a realistic one.
"In many areas, there are lots of barriers to full autonomy," Zilberstein said. "These barriers are not only technological, but also relate to legal and ethical issues and economic concerns."
In his talk at the "Blue Sky" session at AAAI, Zilberstein argued that in many areas, including driving, we will go through a long period where humans act as co-pilots or supervisors, passing off responsibility to the vehicle when possible and taking the wheel when the driving gets tricky, before the technology reaches full autonomy (if it ever does).
In such a scenario, the car would need to communicate with drivers to alert them when they need to take over control. In cases where the driver is non-responsive, the car must be able to autonomously make the decision to safely move to the side of the road and stop.
"People are unpredictable. What happens if the person is not doing what they're asked or expected to do, and the car is moving at sixty miles per hour?" Zilberstein asked. "This requires 'fault-tolerant planning.' It's the kind of planning that can handle a certain number of deviations or errors by the person who is asked to execute the plan."
With support from the National Science Foundation (NSF), Zilberstein has been exploring these and other practical questions related to the possibility of artificial agents that act among us.
Zilberstein, a professor of computer science at the University of Massachusetts Amherst, works with human studies experts from academia and industry to help uncover the subtle elements of human behavior that one would need to take into account when preparing a robot to work semi-autonomously. He then translates those ideas into computer programs that let a robot or autonomous vehicle plan its actions--and create a plan B in case of an emergency.
There are a lot of subtle cues that go into safe driving. Take for example a four-way stop