
Consider, as an example, adding state-machine or modal behavior to planning — for instance, a discrete state on “lane changing” or not. Everywhere we can, we’d like to train our driving behavior from a combination of human driving data and then impose constraints as planning invariants (i.e. the SDV should never collide with simulated vehicles even if they are performing extreme maneuvers). We don’t want to have to manually annotate “this motion corresponds to a lane change that began at this point in time”, nor “ignore this pedestrian in your planning”, or other such discrete, special purpose, tasks. We rethink designs that don’t have a reasonable path to train. We believe that those who don’t design for learnability will be left behind.