Improved metaheuristics with deep learning based object detector for intelligent control in autonomous vehicles

Abstract

In recent times, the autonomous systems have gained considerable interest due to their improved performance and lesser requirements for manual support. The autonomous systems have been extensively implemented in various domains such as logistics, industries, health care, finance, and so on. Intelligence control is the incorporation of autonomous and Artificial Intelligence (AI) systems that assist in critical decision-making steps. Autonomous driving is a new field in intelligent transportation systems that requires automated classification, detection, and the ranging of on-road difficulties. Therefore, the current study develops an Improved Metaheuristics technique with Deep Learning-based Object Detectors for Intelligent Control in Autonomous Vehicles (IMDLOD-ICAV). The presented technique mainly detects the objects to assist, in driving the autonomous vehicles. In the current research work, the RetinaNet model is applied as an object detector whereas the hyperparameter tuning process is executed with the help of the Nadam optimizer. Besides, the Elman Neural Network (ENN) model is also exploited to recognize the objects with a high accuracy. The parameter tuning process is performed with the help of the Improved Dragonfly Algorithm (IDFA). The authors conducted a comprehensive set of experiments to establish the superior performance of the proposed IMDLOD-ICAV technique. The outcomes confirmed the enhanced performance of the IMDLOD-ICAV technique with a maximum accuracy of 99.38%.