Cite this DOI

Rane, A., & A. Chiwhane, w. (2021). An Experimental Assessment of Deep Learning on Highway Driving. *Journal of Science & Technology*, *06*(01), 31–36. https://doi.org/10.46243/jst.2021.v6.i04.pp31-36

@article{rane2021experimental,
  author    = {Rane, Akash and A. Chiwhane, wetambari},
  title     = {{An Experimental Assessment of Deep Learning on Highway Driving}},
  journal   = {Journal of Science \& Technology},
  year      = {2021},
  month     = {aug},
  volume    = {06},
  number    = {01},
  pages     = {31--36},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2021.v6.i04.pp31-36},
  url       = {https://doi.org/10.46243/jst.2021.v6.i04.pp31-36},
  language  = {en},
  abstract  = {Many groups have used a different types of deep learning techniques on computer vision in highway drivingscenes.duringthispaper,we'llobservetheexperimentalassessmentofdeeplearning.Computer Vision with deep learning can bring a reasonable and robust, yet a powerful solution to the sector of autonomous driving. To prepare the deep learning for practical applications the neural networks requires the data sets to train for all types of scenarios of driving. We collect the Data sets and train the model with deep learning and computer vision algorithms for recognition of cars and lanes.}
}

TY  - JOUR
TI  - An Experimental Assessment of Deep Learning on Highway Driving
AU  - Rane, Akash
AU  - A. Chiwhane, wetambari
JO  - Journal of Science & Technology
PY  - 2021
DA  - 2021/08/16/
VL  - 06
IS  - 01
SP  - 31
EP  - 36
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Many groups have used a different types of deep learning techniques on computer vision in highway drivingscenes.duringthispaper,we'llobservetheexperimentalassessmentofdeeplearning.Computer Vision with deep learning can bring a reasonable and robust, yet a powerful solution to the sector of autonomous driving. To prepare the deep learning for practical applications the neural networks requires the data sets to train for all types of scenarios of driving. We collect the Data sets and train the model with deep learning and computer vision algorithms for recognition of cars and lanes.
DO  - 10.46243/jst.2021.v6.i04.pp31-36
UR  - https://doi.org/10.46243/jst.2021.v6.i04.pp31-36
ER  -

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    "type": "article-journal",
    "id": "10.46243/jst.2021.v6.i04.pp31-36",
    "DOI": "10.46243/jst.2021.v6.i04.pp31-36",
    "URL": "https://doi.org/10.46243/jst.2021.v6.i04.pp31-36",
    "title": "An Experimental Assessment of Deep Learning on Highway Driving",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
        {
            "family": "Rane",
            "given": "Akash"
        },
        {
            "family": "A. Chiwhane",
            "given": "wetambari"
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    "issued": {
        "date-parts": [
            [
                2021,
                8,
                16
            ]
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    "volume": "06",
    "issue": "01",
    "page": "31-36",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Many groups have used a different types of deep learning techniques on computer vision in highway drivingscenes.duringthispaper,we'llobservetheexperimentalassessmentofdeeplearning.Computer Vision with deep learning can bring a reasonable and robust, yet a powerful solution to the sector of autonomous driving. To prepare the deep learning for practical applications the neural networks requires the data sets to train for all types of scenarios of driving. We collect the Data sets and train the model with deep learning and computer vision algorithms for recognition of cars and lanes.",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2021.v6.i04.pp31-36 gives all four in one JSON answer.