CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

Abstract

AbstractNumerous developments in technology toward autonomous vehicle systems (AVSs) have been performed for so many years all over the world. As our day‐to‐day life is becoming progressively dependent on automation vehicle system and control devices, the craze on automation advancements is expected to move closer through scientific technologies like artificial intelligence and robotics. From another point of view, the cyber threat to the AVS causes drastic accidents and traffic congestion by varying the speed differences among the vehicles. To overcome such shortcomings, this paper presented a convolutional neural network‐oppositional‐based Henry gas solubility optimization (CNN‐OHGS) algorithm for an autonomous vehicle control system to enhance the robustness of the vehicle. At the same time, the attackers attempt to embed the faulty or defective data into the sensor readings of the autonomous vehicle to interrupt the optimal distances among the automated vehicles. Therefore to minimize such issues, our proposed framework employs the CNN‐OHGS algorithm to reduce the distance variations among the vehicles thus ensuring the safety and optimal distance variation. Finally, the experimental analysis is conducted and the performance evaluation for various attacks, FID evaluation, and remorse function, and distance deviation for all sensor signal attacks are evaluated. The comparative analysis is made and we can clearly state that the proposed work has outperformed other existing approaches.