Submitted via IRC for TheMightyBuzzard
In the field of self-driving cars, algorithms for controlling lane changes are an important topic of study. But most existing lane-change algorithms have one of two drawbacks: Either they rely on detailed statistical models of the driving environment, which are difficult to assemble and too complex to analyze on the fly; or they're so simple that they can lead to impractically conservative decisions, such as never changing lanes at all.
At the International Conference on Robotics and Automation tomorrow, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new lane-change algorithm that splits the difference. It allows for more aggressive lane changes than the simple models do but relies only on immediate information about other vehicles' directions and velocities to make decisions.
[...] One standard way for autonomous vehicles to avoid collisions is to calculate buffer zones around the other vehicles in the environment. The buffer zones describe not only the vehicles' current positions but their likely future positions within some time frame. Planning lane changes then becomes a matter of simply staying out of other vehicles' buffer zones.
[...] With the MIT researchers' system, if the default buffer zones are leading to performance that's far worse than a human driver's, the system will compute new buffer zones on the fly — complete with proof of collision avoidance.
Let me know when someone finds an algorithm that can deal with unknown situations as intuitively as human beings can. Until then...
(Score: 2) by Immerman on Friday May 25 2018, @02:47PM
Yeah, what immediately sprang to mind in response was that old chestnut:
"When in trouble or in doubt run in circles scream and shout"
Pretty sure it won't take much AI to outperform that...