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Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles

2021-04-01 00:12:39
Muhyun Back, Jinkyu Lee, Kyuho Bae, Sung Soo Hwang, Il Yong Chun
arXiv_CV
           
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2104.00169

PDF

https://arxiv.org/pdf/2104.00169.pdf

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