  In advanced driver assistant systems and autonomous driving, it is crucial to
estimate distances between an ego vehicle and target vehicles. Existing
inter-vehicle distance estimation methods assume that the ego and target
vehicles drive on a same ground plane. In practical driving environments,
however, they may drive on different ground planes. This paper proposes an
inter-vehicle distance estimation framework that can consider slope changes of
a road forward, by estimating road gradients of \emph{both} ego vehicle and
target vehicles and using a 2D object detection deep net. Numerical experiments
demonstrate that the proposed method significantly improves the distance
estimation accuracy and time complexity, compared to deep learning-based depth
estimation methods.
