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Development of a simulation platform for safety impact analysis considering vehicle dynamics, sensor errors, and communication latencies: Assessing cooperative adaptive cruise control under cyber attack

机译:考虑车辆动力学,传感器错误和通信延迟的安全影响分析仿真平台的开发:评估网络攻击下的协作式自适应巡航控制

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摘要

While safety is one of the most critical contributions of Cooperative Adaptive Cruise Control (CACC), it is impractical to assess such impacts in a real world. Even with simulation, many factors including vehicle dynamics, sensor errors, automated vehicle control algorithms and crash severity need to be properly modeled. In this paper, a simulation platform is proposed which explicitly features: (i) vehicle dynamics; (ii) sensor errors and communication delays; (iii) compatibility with CACC controllers; (iv) state-of-the-art predecessor leader following (PLF) based cooperative adaptive cruise control (CACC) controller; and (v) ability to quantify crash severity and CACC stability. The proposed simulation platform evaluated the CACC performance under normal and cybersecurity attack scenarios using speed variation, headway ratio, and injury probability. The first two measures of effectiveness (MOEs) represent the stability of CACC platoon while the injury probability quantifies the severity of a crash. The proposed platform can evaluate the safety performance of CACC controllers of interest under various paroxysmal or extreme events. It is particularly useful when traditional empirical driver models are not applicable. Such situations include, but are not limited to, cyber-attacks, sensor failures, and heterogeneous traffic conditions. The proposed platform is validated against data collected from real field tests and tested under various cyber-attack scenarios.
机译:安全是协作式自适应巡航控制(CACC)的最关键的贡献之一,但是在现实世界中评估这种影响是不切实际的。即使进行了模拟,也需要对许多因素进行建模,包括车辆动力学,传感器错误,自动车辆控制算法和碰撞严重性。在本文中,提出了一个仿真平台,其明确具有以下特点:(i)车辆动力学; (ii)传感器错误和通信延迟; (iii)与CACC控制器的兼容性; (iv)最先进的基于前跟随(PLF)的协同自适应巡航控制(CACC)控制器; (v)量化事故严重性和CACC稳定性的能力。拟议的仿真平台使用速度变化,行进比率和伤害概率评估了正常和网络安全攻击情况下的CACC性能。有效性的前两个度量(MOE)代表CACC排的稳定性,而伤害概率则量化了事故的严重性。所提出的平台可以评估在各种阵发性或极端事件下感兴趣的CACC控制器的安全性能。当传统的经验驱动模型不适用时,此功能特别有用。此类情况包括但不限于网络攻击,传感器故障和异构流量状况。所提出的平台已根据从实际测试中收集的数据进行了验证,并在各种网络攻击情况下进行了测试。

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