  Under shared autonomy, wheelchair users expect vehicles to provide safe and
comfortable rides while following users high-level navigation plans. To find
such a path, vehicles negotiate with different terrains and assess their
traversal difficulty. Most prior works model surroundings either through
geometric representations or semantic classifications, which do not reflect
perceived motion intensity and ride comfort in downstream navigation tasks. We
propose to model ride comfort explicitly in traversability analysis using
proprioceptive sensing. We develop a self-supervised learning framework to
predict traversability costmap from first-person-view images by leveraging
vehicle states as training signals. Our approach estimates how the vehicle
would feel if traversing over based on terrain appearances. We then show our
navigation system provides human-preferred ride comfort through robot
experiments together with a human evaluation study.
