Numerous 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.

中文翻译：

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CNN-OHGS：用于自主车辆控制系统的基于CNN对立的亨利气体溶解度优化模型

多年来，世界各地已经对自动驾驶汽车系统 (AVS) 进行了大量技术开发。随着我们的日常生活越来越依赖自动化车辆系统和控制设备，预计通过人工智能和机器人技术等科学技术，自动化进步的热潮将更近一步。从另一个角度来看，对 AVS 的网络威胁通过改变车辆之间的速度差异导致严重的事故和交通拥堵。为了克服这些缺点，本文提出了一种用于自主车辆控制系统的基于卷积神经网络的亨利气体溶解度优化（CNN-OHGS）算法，以增强车辆的鲁棒性。同时，攻击者试图将错误或有缺陷的数据嵌入自动驾驶汽车的传感器读数中，以中断自动驾驶汽车之间的最佳距离。因此，为了尽量减少此类问题，我们提出的框架采用 CNN-OHGS 算法来减少车辆之间的距离变化，从而确保安全性和最佳距离变化。最后，进行了实验分析，并对各种攻击的性能评估、FID 评估和悔恨函数以及所有传感器信号攻击的距离偏差进行了评估。进行了比较分析，我们可以清楚地表明，所提出的工作优于其他现有方法。我们提出的框架采用 CNN-OHGS 算法来减少车辆之间的距离变化，从而确保安全性和最佳距离变化。最后，进行了实验分析，并对各种攻击的性能评估、FID 评估和悔恨函数以及所有传感器信号攻击的距离偏差进行了评估。进行了比较分析，我们可以清楚地表明，所提出的工作优于其他现有方法。我们提出的框架采用 CNN-OHGS 算法来减少车辆之间的距离变化，从而确保安全性和最佳距离变化。最后，进行了实验分析，并对各种攻击的性能评估、FID 评估和悔恨函数以及所有传感器信号攻击的距离偏差进行了评估。进行了比较分析，我们可以清楚地表明，所提出的工作优于其他现有方法。