It helps a lot in my experience. So in simulation I tried this with imitation learning by training on a hood camera, a camera at the height of what a semi-truck

It helps a lot in my experience. So in simulation I tried this with imitation learning by training on a hood camera, a camera at the height of what a semi-truck hood would be, and another camera offset to the left 1.5m along with steering and throttle for labels. I also added random noise to the position (less than a meter), rotation (less than degree), fov (less than a degree), capture height (< 1%), and capture width (< 1%). The result was a 3x higher average score on a driving benchmark where the score was meters driven minus seconds taken, second-meters of lane deviation, and seconds where acceleration surpassed 0.5g forces (to measure comfort). The dataset, training code, and sim are at deepdrive.io - a different entity with the same name :)