An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization

Abstract

The increasing trend of autonomous driving vehicles in smart cities emphasizes the need for safe travel. However, the presence of obstacles, potholes, and complex road environments, such as poor illumination and occlusion, can cause blurred road images that may impact the accuracy of maneuver prediction in visual perception systems. To address these challenges, a novel ensemble model named ABHO-based deep CNN-BiLSTM has been proposed for traffic sign detection. This model combines a hybrid convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with the alarming-based hunting optimization (ABHO) algorithm to improve maneuver prediction accuracy. Additionally, a modified hough-enabled lane generative adversarial network (ABHO based HoughGAN) has been proposed, which is designed to be robust to blurred images. The ABHO algorithm, inspired by the defending and social characteristics of starling birds and Canis kojot, allows the model to efficiently search for the optimal solution from the available solutions in the search space. The proposed ensemble model has shown significantly improved accuracy, sensitivity, and specificity in maneuver prediction compared to previously utilized methods, with minimal error during lane detection. Overall, the proposed ensemble model addresses the challenges faced by autonomous driving vehicles in complex and obstructed road environments, offering a promising solution for enhancing safety and reliability in smart cities.