Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

Hajira Saleem,Adel Rajab,Faisal Riaz,Khairan Rajab,Muhammad Akram,Asadullah Shaikh,Mana Saleh Al Reshan

doi:10.32604/cmc.2022.022726

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.

Highlights

In recent years, we have witnessed a storm of advancements in autonomous self-driving ground vehicles, and significant research efforts in the industry and academia are being devoted to their successful implementation
Thereafter, the effectiveness of the tuned hyperparameter settings for other convolutional neural networks (CNNs) architectures was verified by training another model (M2)
For the comparison of the Particle Swarm Optimization (PSO) and Bat algorithms for the CNN architecture optimization, the average fitness of the individuals in a population is plotted at each iteration

Summary

Introduction

We have witnessed a storm of advancements in autonomous self-driving ground vehicles, and significant research efforts in the industry and academia are being devoted to their successful implementation In this regard, one of the challenges identified is the accurate prediction of the steering angle required for a vehicle to autonomously steer on a given terrain, due to the heterogeneity of roads and their geometries. Imitation learning is used to learn the steering angle to drive in different scenarios through human driving demonstration For this purpose, corresponding steering angles are collected simultaneously while driving the vehicle, and these are used as training data for the supervised learning by an artificial neural network (ANN) model. The performance of these networks strongly depends on their architecture, design, and training parameters [6]

Methods

Results

Conclusion