An agile autonomous car driving assistance using hybrid optimization-based kernel support vector convolutional network

Abstract

In robotic control systems, autonomous car driving is considered a complicated task. The conventional modular techniques necessitate precise localization, planning, and mapping procedures to provide safe driving. These requirements make autonomous car driving tasks computationally ineffective and sensitive to ecological changes. In recent times, the emergence of deep learning-based approaches in autonomous car driving systems provided promising solutions for complex vision task interpretation, localization, and environmental perception. But, they create distribution mismatch issues and require huge perilous interaction information to offer safe driving with the prediction of future collisions. Therefore, to overcome this issue, a novel Modified Kernel Support vector Convolutional based Hybrid Grasshopper Harris hawk (MKSC-HGH) approach is proposed. The proposed MKSC-HGH approach efficiently forecasts the uncertainties of agile autonomous car driving and guides proper navigation details of the track to avoid the possibility of collision with on-road obstacles and fences. This system inputs camera images captured through sensors, car speed, and manual driving data to analyze and predict safety control and probable collision. The prominent feature representing the ability of Modified Convolutional Neural Network (MCNN) and complex boundary encoding process of Kernel Support Vector Machine (KSVM) with hyperparameter tuning operation of Modified Grasshopper Optimization Algorithm based Improved Harris Hawk Optimization (MGOA-IHHO) algorithm made proposed system applicable to predict future states (ie. visual prediction, speed prediction, and collision prediction) more accurately. The efficiency of the proposed MKSC-HGH approach is investigated using evaluation measures namely completion time, average speed, top speed, and laps time. The simulation outcome shows the superiority of the proposed MKSC-HGH approach in predicting agile autonomous car driving over state of art techniques about task performance and sample efficiency.