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- W4287250217 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." @default.
- W4287250217 created "2022-07-25" @default.
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- W4287250217 date "2021-03-31" @default.
- W4287250217 modified "2023-09-27" @default.
- W4287250217 title "Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles" @default.
- W4287250217 doi "https://doi.org/10.48550/arxiv.2104.00169" @default.
- W4287250217 hasPublicationYear "2021" @default.
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