The success of autonomous vehicles ($AVhs$) depends upon the effectiveness of sensors being used and the accuracy of communication links and technologies being employed. But these sensors and communication links have great security and safety concerns as they can be attacked by an adversary to take the control of an autonomous vehicle by influencing their data. Especially during the state estimation process for monitoring of autonomous vehicles' dynamics system, these concerns require immediate and effective solution. In this paper we present a new adversarial deep reinforcement learning algorithm (NDRL) that can be used to maximize the robustness of autonomous vehicle dynamics in the presence of these attacks. In this approach the adversary tries to insert defective data to the autonomous vehicle's sensor readings so that it can disrupt the safe and optimal distance between the autonomous vehicles traveling on the road. The attacker tries to make sure that there is no more safe and optimal distance between the autonomous vehicles, thus it may lead to the road accidents. Further attacker can also add fake data in such a way that it leads to reduced traffic flow on the road. On the other hand, autonomous vehicle will try to defend itself from these types of attacks by maintaining the safe and optimal distance i.e. by minimizing the deviation so that adversary does not succeed in its mission. This attacker-autonomous vehicle action reaction can be studied through the game theory formulation with incorporating the deep learning tools. Each autonomous vehicle will use Long-Short-Term-Memory (LSTM)-Generative Adversarial Network (GAN) models to find out the anticipated distance variation resulting from its actions and input this to the new deep reinforcement learning algorithm (NDRL) which attempts to reduce the variation in distance. Whereas attacker also chooses deep reinforcement learning algorithm (NDRL) and wants to maximize the distance variation between the autonomous vehicles.

自动驾驶汽车的成功（$\mathrm{一种}VHs$）取决于所用传感器的有效性以及所用通信链路和技术的准确性。但是这些传感器和通信链路具有极大的安全性和安全性，因为它们可能会受到对手的攻击，从而通过影响其数据来控制自动驾驶汽车。特别是在用于监视自动驾驶动力学系统的状态估计过程中，这些问题需要立即有效的解决方案。在本文中，我们提出了一种新的对抗性深度强化学习算法（NDRL），该算法可用于在出现这些攻击时最大程度地提高自主车辆动力学的鲁棒性。在这种方法中，对手试图将有缺陷的数据插入自动驾驶汽车的 s的传感器读数，以便破坏在道路上行驶的自动驾驶汽车之间的安全距离和最佳距离。攻击者试图确保自动驾驶汽车之间没有更安全和最佳的距离，从而可能导致交通事故。进一步的攻击者还可以添加假数据，从而导致道路上的流量减少。另一方面，自动驾驶车辆将尝试通过保持安全和最佳距离（即通过最小化偏差）来防御此类攻击，以使对手无法成功执行任务。可以通过博弈论公式化并结合深度学习工具来研究这种攻击者自主的车辆动作反应。每辆自动驾驶汽车都将使用长期记忆（LSTM）-生成对抗网络（GAN）模型来找出其动作导致的预期距离变化，并将其输入到新的深度强化学习算法（NDRL）中，该算法试图减少距离变化。攻击者还选择了深度强化学习算法（NDRL），并希望最大化自动驾驶汽车之间的距离变化。