• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Vehicular Communications >Deep reinforcement learning approach for autonomous vehicle systems for maintaining security and safety using LSTM-GAN
【24h】

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

机译：基于LSTM-GAN维护安全性和安全性的自主车辆系统的深增强学习方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The success of autonomous vehicles (AV hs) 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. (c) 2020 Elsevier Inc. All rights reserved.
机译：自治车辆（AV HS）的成功取决于所使用的传感器的有效性以及所采用的通信链路和技术的准确性。但这些传感器和通信链接具有很大的安全性和安全问题，因为它们可以通过对手攻击来通过影响其数据来控制自主车辆。特别是在监测自动车辆动态系统的国家估算过程中，这些问题需要立即和有效的解决方案。在本文中，我们提出了一种新的对抗深度加强学习算法（NDRL），可用于最大化自主车辆动态在存在这些攻击时的鲁棒性。在这种方法中，对手试图将有缺陷的数据插入自动车辆的传感器读数，以便它可以破坏在道路上行驶的自主车辆之间的安全和最佳距离。攻击者试图确保自主车辆之间没有更安全和最佳的距离，因此可能导致道路事故。进一步的攻击者还可以添加假数据，使其导致降低道路上的交通流量。另一方面，自主车辆将通过维持安全和最佳距离来试图从这些类型的攻击中捍卫这些类型的攻击。通过最大限度地减少偏差，以使对手不会在其使命中取得成功。这种攻击者自主车辆作用反应可以通过融合深层学习工具来研究博弈论制定。每个自主车辆将使用长短期内存（LSTM）的对抗网络（GAN）模型来找出由其动作产生的预期距离变化，并将其输入到新的深度加强学习算法（NDRL）尝试降低距离的变化。虽然攻击者也选择了深度加强学习算法（NDRL），并且希望最大化自动车辆之间的距离变化。 （c）2020 Elsevier Inc.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号