Open Access

ARTICLE

Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

Hajira Saleem¹, Faisal Riaz¹, Asadullah Shaikh², Khairan Rajab^2,3, Adel Rajab^2,*, Muhammad Akram², Mana Saleh Al Reshan²

1 Control, Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation (NCRA HEC Pakistan), and with the Department of Computer Science and Information Technology, Mirpur University of Science and Technology (MUST), Mirpur Azad Kashmir, 10250, Pakistan
2 College of Computer Science and Information System, Najran University, Najran, 61441, Saudi Arabia
3 College of Computer Science and Engineering, University of South Florida, Tampa, 33620, United States

* Corresponding Author: Adel Rajab. Email:

Computers, Materials & Continua 2022, 71(2), 2285-2302. https://doi.org/10.32604/cmc.2022.022726

Received 17 August 2021; Accepted 29 September 2021; Issue published 07 December 2021

Abstract

Deep learning techniques, particularly convolutional neural networks (CNNs), have exhibited remarkable performance in solving vision-related problems, especially in unpredictable, dynamic, and challenging environments. In autonomous vehicles, imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs. In this regard, globally, researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results. Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs. However, to the best of our knowledge, these techniques are yet to be applied to address the problem of imitation-learning-based steering angle prediction. Thus, in this study, we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters, which are employed to solve the steering angle prediction problem. To validate the performance of each hyperparameters’ set and architectural parameters’ set, we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set: optimizer, Adagrad; learning rate, 0.0052; and nonlinear activation function, exponential linear unit. As per our findings, we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones. Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach. Infield testing was also performed using the model trained with the optimal architecture, which we developed using our approach.

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