Digital Twin Analysis to Promote Safety and Security in Autonomous Vehicles

被引：74
作者：
Almeaibed S. [1 ]
Al-Rubaye S. [1 ]
Tsourdos A. [1 ]
Avdelidis N.P. [1 ]
机构：
[1] Cranfield University, Manufacturing Systems
来源：
IEEE Communications Standards Magazine | 2021年 / 5卷 / 01期
关键词：
Data acquisition;
D O I：
10.1109/MCOMSTD.011.2100004
中图分类号：
学科分类号：
摘要：
With the new industrial revolution of digital transformation, more intelligence and autonomous systems can be adopted in the manufacturing transportation processes. Safety and security of autonomous vehicles (AVs) have obvious advantages of reducing accidents and maintaining a cautious environment for drivers and pedestrians. Therefore, the transformation to data-driven vehicles is associated with the concept of digital twin, especially within the context of AV design. This also raises the need to adopt new safety designs to increase the resiliency and security of the whole AV system. To enable secure autonomous systems for smart manufacturing transportation in an end-to-end fashion, this article presents the main challenges and solutions considering safety and security functions. This article aims to identify a standard framework for vehicular digital twins that facilitate the data collection, data processing, and analytics phases. To demonstrate the effectiveness of the proposed approach, a case study for a vehicle follower model is analyzed when radar sensor measurements are manipulated in an attempt to cause a collision. Perceptive findings of this article can pave the way for future research aspects related to employing digital twins in the AV industry. © 2017 IEEE.
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页码：40 / 46
页数：6
相关论文
共 15 条
  • [11] Molina C.B.S.T., Et al., Assuring fully autonomous vehicles safety by design: The autonomous vehicle control (avc) module strategy, Proc. 2017 47th Annual IEEE/IFIP Int'l. Conf. Dependable Systems and Networks Wksps, pp. 16-21, (2017)
  • [12] Mihai S., Et al., Towards autonomous driving: A machine learning-based pedestrian detection system using 16-layer lidar, Proc. 13th Int'l. Conf. Commun, pp. 271-276, (2020)
  • [13] Henriksson J., Borg M., Englund C., Automotive safety and machine learning: Initial results from a study on how to adapt the iso 26262 safety standard, Proc. 2nd IEEE/ACM 1st Int'l. Wksp. Software Engineering for AI in Autonomous Systems, pp. 47-49, (2018)
  • [14] Steger M., Et al., A security metric for structured security analysis of cyber-physical systems supporting sae j3061, Proc. 2nd Int'l. Wksp. Modelling, Analysis, and Control of Complex CPS, pp. 1-6, (2016)
  • [15] Jeon W., Et al., Simultaneous cyber-attack detection and radar sensor health monitoring in connected acc vehicles, IEEE Sensors J, (2020)
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