Generally ML models are trained once and then used in their trained state unchanging. They don’t continue to learn.
I went down a slight rabbit hole on this. As far as i can tell (looking at manufacturer claims on their website for devices of the era), 2018 is right on the edge of when a lane finding algorithm would have started to incorporate ML techniques. That’s when mobileye released the eyeq4, which claimed to run ML models, and Wikipedia says was used by Ford, BMW, Nissan, VW, and Honda, albeit on only some of their models. The previous Eyeq3 from 2014 seems to be more classical models, but still used some ML techniques, seemingly more for things like speed limit reading.
ML algorithms refer to quite a wide variety of approaches. Generally what separates them is when the parameters of a generic model/algorithm are optimized over some specific data, instead of using an algorithm handcrafted to the particular problem.
Generally ML models are trained once and then used in their trained state unchanging. They don’t continue to learn.
I went down a slight rabbit hole on this. As far as i can tell (looking at manufacturer claims on their website for devices of the era), 2018 is right on the edge of when a lane finding algorithm would have started to incorporate ML techniques. That’s when mobileye released the eyeq4, which claimed to run ML models, and Wikipedia says was used by Ford, BMW, Nissan, VW, and Honda, albeit on only some of their models. The previous Eyeq3 from 2014 seems to be more classical models, but still used some ML techniques, seemingly more for things like speed limit reading.
ML algorithms refer to quite a wide variety of approaches. Generally what separates them is when the parameters of a generic model/algorithm are optimized over some specific data, instead of using an algorithm handcrafted to the particular problem.