Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles
This addresses a practical limitation in advanced driver assistance and autonomous driving systems, offering an incremental improvement over existing methods that assume a same ground plane.
The paper tackles the problem of inter-vehicle distance estimation in driving environments where vehicles may be on different ground planes, proposing a framework that uses road gradients of both ego and target vehicles to significantly improve accuracy and time complexity compared to deep learning-based methods.
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.