McGill University researchers say they've developed a technique to train a remote-controlled, offroad car to drive on terrain from aerial and first-person imagery. Their hybrid approach accounts for terrain roughness and obstacles using on-board sensors, enabling it to generalize to environments with vegetation, rocks, and sandy trails. The work is preliminary, but it might hold promise for autonomous vehicle companies that rely chiefly on camera footage to train their navigational AI. U.K.-based Wayve is in that camp, as are Tesla, Mobileye, and Comma.ai. The researchers' work combines elements of model-free and model-based AI training methods into a single graph to leverage the strength of both while offsetting their weaknesses.