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.
@article{arxiv.2104.00169,
title = {Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles},
author = {Muhyun Back and Jinkyu Lee and Kyuho Bae and Sung Soo Hwang and Il Yong Chun},
journal= {arXiv preprint arXiv:2104.00169},
year = {2021}
}