Robust Spatiotemporal Lane Detection Model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50021769" target="_blank" >RIV/62690094:18450/25:50021769 - isvavai.cz</a>
Result on the web
<a href="https://journals.sagepub.com/doi/10.1177/03611981241260696" target="_blank" >https://journals.sagepub.com/doi/10.1177/03611981241260696</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1177/03611981241260696" target="_blank" >10.1177/03611981241260696</a>
Alternative languages
Result language
angličtina
Original language name
Robust Spatiotemporal Lane Detection Model
Original language description
Lane lines are frequently interrupted in autonomous driving environments because of some objective conditions, such as occlusion or congestion, which often lead to the decreased detection performance of a model. Current detection methods relying on spatial information struggle to detect complete lane lines in such conditions. In this paper, we build a robust lane detection model by fusing spatiotemporal information and dilated convolution. The proposed model is aided by the dilated convolution, which expands the scope of convolutional processes to extract more lane feature information from various perception environments. Convolutional gate recurrent units (ConvGRUs) are employed at the high-level semantic phase to aid the proposed model to get more effective lane feature information by dealing with the spatiotemporal information of consecutive frames. Compared with models FCN, DeepLabv3, RefineNet, SCNN, Cheng-DET, LDNet, SegNet, SegNet-Ego-Lane, Res18, Res34, ResNet-18-SAD, ResNet-34-SAD, ENet-SAD, ReNet-101, R-18-E2E, R-34-E2E, R-101-SAD, R-101-E2E, ResNet34-Qin, LaneNet, PINET(64x32), UNet_ConvLSTMSegNet_ConvLSTM, LDSTNet, extensive experiments on three well-known lane detection benchmarks prove the usefulness of the proposed model, achieving robust results and competitive performance.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20104 - Transport engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
TRANSPORTATION RESEARCH RECORD
ISSN
0361-1981
e-ISSN
2169-4052
Volume of the periodical
2679
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
2213-2227
UT code for WoS article
001288888500001
EID of the result in the Scopus database
2-s2.0-85201019299