Convolutional Neural Network Based on Self-Driving Autonomous Vehicle (CNN)

Naik, Gautam; Ameta, Prerit; Shayeer, N. Baba; Rakesh, Baboo; Dravida, Swethasri

doi:10.1007/978-981-16-7167-8_68

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Data production without supervision would impact a model trained with redundant training sets. In order to recognise the environment in which predictable and unpredictable things are regarded, we must limit the data required to train the reinforcement learning model [30][31][32][33][34][35][36][37][38][39] to a set of data points.…”

Section: Related Workmentioning

confidence: 99%

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Vijayakumar¹,

Shanthini²,

Karthik³

et al. 2023

Computer Systems Science and Engineering

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The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans. Hence, there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs. An optimal prediction engine is required that suitably reads the background objects and make optimal decisions. In this paper, the author(s) develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles (SUVs) that uses three different modules involving (a) recognition model, (b) planning model and (c) prediction model. The study develops a direct realization method for an autonomous vehicle driving. The direct realization method is designed as a behavioral model that incorporates three different modules to ensure optimal autonomous driving. The behavioral model includes recognition, planning and prediction modules that regulates the input trajectory processing of input video datasets. A deep learning algorithm is used in the proposed approach that helps in the classification of known or unknown objects along the line of sight. This model is compared with conventional deep learning classifiers in terms of recall rate and root mean square error (RMSE) to estimate its efficacy. Simulation results on different traffic environment shows that the Ensemble Convolutional Network Reinforcement Learning (E-CNN-RL) offers increased accuracy of 95.45%, reduced RMSE and increased recall rate than existing Ensemble Convolutional Neural Networks (CNN) and Ensemble Stacked CNN.

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Section: Related Workmentioning

confidence: 99%

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Vijayakumar¹,

Shanthini²,

Karthik³

et al. 2023

Computer Systems Science and Engineering

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“…In contrast, end-to-end training with deep learning [18] utilizing neural network models can extract relevant structured and non-linear features. In numerous domains, including autonomous automobiles [19], tumor identification [20,21], bio-informatics [22], and machine translation [23], it has had considerable success.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture

Ryu

Jang

2022

Applied Sciences

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Convolution neural networks have received much interest recently in the categorization of hyperspectral images (HSI). Deep learning requires a large number of labeled samples in order to optimize numerous parameters due to the expansion of architecture depth and feature aggregation. Unfortunately, only few examples with labels are accessible, and the majority of spectral images are not labeled. For HSI categorization, the difficulty is how to acquire richer features with constrained training data. In order to properly utilize HSI features at various scales, a 3D Capsule-Net based supervised architecture is presented in this paper for HSI classification. First, the input data undergo incremental principal component analysis (IPCA) for dimensionality reduction. The reduced data are then divided into windows and given to a 3D convolution layer to get the shallow features. These shallow features are then used by 3D Capsule-Net to compute high-level features for classification of HSI. Experimental investigation on three common datasets demonstrates that the categorization performance by Capsule-Net based architecture exceeds a number of other state-of-the-art approaches.

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Workforce forecasting for state transportation agencies: A machine learning approach

Ogungbire,

Mitra

2024

International Journal of Transportation Science and Technology

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Convolutional Neural Network Based on Self-Driving Autonomous Vehicle (CNN)

Cited by 5 publications

References 10 publications

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture

Workforce forecasting for state transportation agencies: A machine learning approach

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