Deep reinforcement learning approach for autonomous vehicle systems for maintaining security and safety using LSTM-GAN

Abstract The success of autonomous vehicles ( A V h s ) 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.