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

Autor:	Back, Muhyun, Lee, Jinkyu, Bae, Kyuho, Hwang, Sung Soo, Chun, Il Yong
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 3 figures, 2 tables, submitted to IEEE ICAS 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2104.00169 Zobrazit plný text záznamu View this record from Arxiv