A Framework for Drivable Area Detection Via Point Cloud Double Projection on Rough Roads

Xu, Fengyu; Liang, Huawei; Wang, Zhong Lin; Lin, Linglong

doi:10.1007/s10846-021-01381-7

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…Learning-based methods usually offer the higher performance [26][27][28]30,36]. However, there are non-learning-based methods that reach similar performance levels [35,40,41].…”

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confidence: 99%

Recent Developments on Drivable Area Estimation: A Survey and a Functional Analysis

Hortelano,

Villagrá,

Godoy

et al. 2023

Sensors

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Most advanced autonomous driving systems (ADS) today rely on the prior creation of high-definition maps (HD maps). This process is expensive and needs to be performed frequently to keep up with the changing conditions of the road environment. Creating accurate navigation maps online is an alternative to reduce the cost and broaden the current operational design domains (ODD) of modern ADS. This paper offers a snapshot of the state of the art in drivable area estimation, which is an essential technology to deploy ADS in ODDs where HD maps are limited or unavailable. The proposed review introduces a novel architecture breakdown that fits learning-based and non-learning-based techniques and allows the analysis of a set of impactful and recent drivable area algorithms. In addition to that, complimentary information for practitioners is provided: (i) an assessment of the influence of modern sensing technologies on the task under study and (ii) a selection of relevant datasets for evaluation and benchmarking purposes.

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“…Learning-based methods usually offer the higher performance [26][27][28]30,36]. However, there are non-learning-based methods that reach similar performance levels [35,40,41].…”

mentioning

confidence: 99%