Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and &lt;inline-formula&gt; 
                     &lt;tex-math notation="LaTeX"&gt;$k$ &lt;/tex-math&gt;
                  &lt;/inline-formula&gt;-Nearest Neighbor Scheme

Dairi, Abdelkader; Harrou, Fouzi; Sun, Ying; Sarwat, Mohamed

doi:10.1109/jsen.2018.2831082

Cited by 82 publications

(10 citation statements)

References 41 publications

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“…The main feature of deep learning models consists of their capacity to automatically extracting relevant information from large data [39]. They have attracted attention in the machine-learning community and investigated in a wide range of applications [38,40,41,11].…”

Section: Introductionmentioning

confidence: 99%

Comparative study of machine learning methods for COVID-19 transmission forecasting

Dairi

Harrou

Zeroual

et al. 2021

Journal of Biomedical Informatics

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130

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Section: Introductionmentioning

confidence: 99%

Comparative study of machine learning methods for COVID-19 transmission forecasting

Dairi

Harrou

Zeroual

et al. 2021

Journal of Biomedical Informatics

Self Cite

130

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“…Hybrid methods include a combination of multiple ML or DL methods used in CV techniques. There are many intelligent transportation applications for this approach, such as license plate recognition [ 85 , 100 , 101 ], video anomaly detection [ 68 , 89 , 92 , 102 ], automatic license plate recognition [ 25 , 103 ], vehicle detection [ 11 , 12 , 53 , 55 ], pedestrian detection [ 58 , 104 ], lane line detection [ 63 , 105 ], obstacle detection [ 106 , 107 , 108 , 109 , 110 ], structural damage detection [ 111 , 112 , 113 ], and autonomous vehicle applications [ 13 , 114 , 115 ].…”

Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

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“…A stereo vision-based method was presented in [20] for obstacle detection in VANETs in an urban environment. The utilized model was a Deep-Stacked Auto-encoder (DSA), with the k-nearest neighbor classi er using three real-life datasets.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Method for Automatic Reliability Prediction of CR-VANETs

Bahramnejad

Movahhedinia

Naseri

2023

Preprint

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Reliability is a critical issue in vehicular networks. A Deep Learning (DL) method is proposed in this study to automatically predict the reliability of Cognitive Radio Vehicular Networks (CR-VANETs) ignored in the previous research. First, a dataset is generated based on a previously proposed method for the reliability assessment of CR-VANETs. Then, a model is proposed to predict the networks' reliability using the DL method and compared with other machine learning methods. While machine learning methods have been applied in vehicular networks, they have not been used for reliability prediction. The proposed DL model is utilized in this research to predict CR-VANETs' reliability. Based on the results, the DL model outperforms other machine learning methods for reliability prediction. The correlation coefficient and root mean square error of the test data for the DL model are 0.9862 and 0.0381. These results indicate the CR-VANETs' reliability prediction accurately using the proposed method.

show abstract

Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math> </inline-formula>-Nearest Neighbor Scheme

Cited by 82 publications

References 41 publications

Comparative study of machine learning methods for COVID-19 transmission forecasting

Comparative study of machine learning methods for COVID-19 transmission forecasting

Computer Vision Applications in Intelligent Transportation Systems: A Survey

A Deep Learning Method for Automatic Reliability Prediction of CR-VANETs

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