Comparative Analysis of Steering Angle Prediction for Automated Object using Deep Neural Network

Islam, Md Khairul; Yeasmin, Most. Nilufa; Kaushal, Chetna; Amin, Al; Islam, Rakibul; Showrov, Imran Hossain

doi:10.1109/icrito51393.2021.9596499

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…The sample sizes in the training, validation, and testing sets in the simulated data presented in [8], [9], and [10] are significantly different. In studies, the following metrics were employed: [10], [12] MSE, [13] Cross-Entropy, [14] In order to compare the models' accuracy and repeatability. Due to the simplicity, three full laps of driving were recorded at a constant speed.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Comparative Transfer Learning Models for End-to-End Self-Driving Car

Khidhir¹,

Morad²

2022

alkej

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Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steering angle of a self-driving vehicle that is suitable to be applied to embedded automotive technologies with limited performance. Three well-known pre-trained models were compared in this study: AlexNet, ResNet18, and DenseNet121. Transfer learning was utilized by modifying the final layer of pre-trained models in order to predict the steering angle of the vehicle. Experiments were conducted on the dataset collected from two different tracks. According to the study's findings, ResNet18 and DenseNet121 have the lowest error percentage for steering angle values. Furthermore, the performance of the modified models was evaluated on predetermined tracks. ResNet18 outperformed DenseNet121 in terms of accuracy, with less deviation from the center of the track, while DenseNet121 demonstrated greater adaptability across multiple tracks, resulting in better performance consistency.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Another study [12] presented a comparison between the three transfer learning models: Visual Geometry Group (VGG16), ResNet-152, Densely Connected Convolutional Networks (DenseNet-201, and Nvidia's model for steering angle control. The input image size for all models was (66x200x3).…”

Section: Related Workmentioning

confidence: 99%

Comparative Transfer Learning Models for End-to-End Self-Driving Car

Khidhir¹,

Morad²

2022

alkej

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“…There are several layers in the convolutional neural network, such as the input layer, hidden layer, and output layer. Each layer has several neurons or nodes that take information from the previously hidden layer or input layer that used the mapping function to process the data and transfer it to the next layer [ 85 , 86 ]. One of the categories is generated by the output layer.…”

Section: Proposed Methodologymentioning

confidence: 99%

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Islam

Kaushal

Amin

et al. 2022

Contrast Media & Molecular Imaging

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The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the “HAM10000” dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model’s classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.

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“…In (Alghodhaifi and Lakshmanan 2021), a comprehensive review of modelling and simulation alternatives are presented. Other relevant work include (Ijaz and Wang 2021), (Islam et al 2021), (Munir et al 2020), (Zhang and Huang 2020) and references therein.…”

Section: Introductionmentioning

confidence: 99%

A Photo-Realistic Simulation and Test Platform for Autonomous Vehicles Research

Proceedings of the International Conference on Industrial Engineering and Operations Management

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In this paper, we present an autonomous vehicles (AV) research testbed with photo-realistic visualisation and simulation capability. Compared to Oak Ridge National Labs CAVE system, Microsoft's AirSim, and MATLAB's Vehicle Dynamics Blockset approach, the key difference is a decoupled and modular system architecture which allows developers to focus on only one aspect of the whole system and be productive with minimal training. Basically, the proposed system has a game engine, an external hardware accelerator for vehicle dynamical models, and a GPU equipped computer to run the AI algorithms. Our vision is to have all vehicle dynamical models to be built in MATLAB/Simulink and run on a hardware accelerator like Speedgoat, which is a leading software and hardware framework for scientific system modelling and hardware in the loop simulation. The proposed photo realistic visualization environment is Unreal Engine based, and all AI models are keras based. This decoupled system architecture allows all vehicle dynamical models and AI decision making to be implemented outside the Unreal Engine, while the game engine doing all collision detections and reporting. Similarly, all traffic and pedestrian movement stochastic models can be implemented outside the game engine, allowing a framework agnostic approach to be used throughout the whole system.

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Comparative Analysis of Steering Angle Prediction for Automated Object using Deep Neural Network

Cited by 8 publications

References 10 publications

Comparative Transfer Learning Models for End-to-End Self-Driving Car

Comparative Transfer Learning Models for End-to-End Self-Driving Car

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

A Photo-Realistic Simulation and Test Platform for Autonomous Vehicles Research

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