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1 |
Driverless vehicle security: Challenges and future research opportunities
امنیت وسیله نقلیه بدون راننده: چالش ها و فرصت های تحقیقاتی آینده-2020 As self-driving vehicles become increasingly popular, new generations of attackers will seek to exploit
vulnerabilities introduced by the technologies that underpin such vehicles for a range of motivations (e.g.
curiosity, criminally-motivated, financially-motivated and state-sponsored). For example, vulnerabilities
in self-driving vehicles may be exploited to be used in terrorist attacks such as driving into places of mass
gatherings (i.e. using driverless vehicles as weapons to cause death or serious bodily injury). This survey
presents a categorized summary of security methodologies developed to secure sensing, positioning,
vision, and network technologies that can be equipped in driverless-vehicles. These technologies have the
potential to benefit their security from tailored machine learning models. Future research opportunities
are also identified. Keywords: Self-driving vehicles | Driverless vehicles | Intrusion detection | Network security | Autonomy and trust |
مقاله انگلیسی |
2 |
ViDAQ: A computer vision based remote data acquisition system for reading multi-dial gauges
ViDAQ: سیستم کسب اطلاعات از راه دور مبتنی بر بینایی ماشین برای خواندن سنجهای چند زبانه-2019 This paper presents and evaluates design improvements to the Visual Data Acquisition (ViDAQ) system for
reading multi-dial gauges. ViDAQ in general, is targeted to occupy a niche application for a cost effective and
readily deployable solution for non-intrusive and remote acquisition of data from legacy human machine interface
(HMI) indicators. Legacy HMI indicators that pose numerous technological hurdles in being digitally
monitored, include analogue rotary multi-dial gauges, alarm lamps, switches etc, much like those common to
industrial process monitoring systems can benefit from ViDAQ. Furthermore, ViDAQ is poised to assist in realizing
an overarching design goal of a generic EYE-on-HMI (Expert supervisorY systEm) framework. As a framework,
EYE-on-HMI stands to integrate the burgeoning field of machine learning and computer vision for realtime
detection of human-in-the-loop operator errors and gather human performance data in any commercial
and/or industrial process control domain for improving operational safety. Operator interaction with HMI is
vital to the operational safety of any process control such as in nuclear power plant operation, aviation, public
transit vehicles, driverless vehicles, etc. and thus should be monitored actively. Keywords: Computer vision | Human machine interface (HMI) | Human factors engineering (HFE) | Expert supervisory system | Nuclear power plant (NPP) | Cyber physical systems (CPS) | Remote monitoring |
مقاله انگلیسی |