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

Cui, Lian; Hu, Jia; Park, Byungkyu Brian; Bujanovic, Pavle

doi:10.1016/j.trc.2018.10.005

Cited by 95 publications

(42 citation statements)

References 34 publications

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“…In the case of smart-grids, cyber-attacks can cause damage at connecting physical assets [165,166]. In transportation, cyber-attacks might be in the way vehicles dynamics and monitoring is collected and analysed [167]. In telecommunication systems the cyber-attacks may directly affect the network topology by line-addition, line-removal, and line-switching [168].…”

Section: Agent-based Complex Networkmentioning

confidence: 99%

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

et al. 2020

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Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.Processes 2020, 8, 312 2 of 29 in a telecommunication system network, nodes (e.g., routers and switches) may suffer over time a degradation in their properties that may lead them to diminish their performance. However, just the daily variation on traffic demand also has an impact on such nodes performance. Unexpected, external intervention such as cyber-attacks or extreme weather conditions may affect the properties, even the topology of a telecommunication system.Intelligent distributed systems are capable of modelling how different parts of the network might work individually and collectively [10]. Ultimately, this is used as support for any decision-making system aiming to achieve better overall system functioning and its consequent performance. Intelligent distributed systems encompass autonomous learning units that can be associated with the nodes of a complex network. These intelligent network nodes are able to act independently and also interact with other nodes to pursue both individual (local) and general system-level targets. The necessary communication between individuals can be represented by links of the complex network connecting their nodes. It is natural to understand such nodes as intelligent agents within a MAS framework. Each agent separately obeying simple rules but working together with other agents makes it possible to approach complex engineering challenges. Intelligent distributed systems have been proposed in a broad range of engineered complex systems [11,12]. Precisely, the review proposed herein focuses only on engineered systems and manufacturing processes, given the wide spectrum of the approached topic. Out of this review fall, then, topics related to social systems analysis such as migration models [13,14] or online social media [15,16]. Similarly, natural systems are out of this review scope. This is the case of computational chemistry [17,18], biology [19,20], and metabolic networks [21,22].Automatic optimisation and control in an engineering system is associated with a near real-time data acquisition and an optimal decision-making. Th...

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Section: Agent-based Complex Networkmentioning

confidence: 99%

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

et al. 2020

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“…The scenario in this case study is built based on the use case presented in [20]. We also obtained relevant knowledge of CAV functionalities, attacks and potential impacts from this reference.…”

Section: Case Studymentioning

confidence: 99%

“…We consider a CAV that moves in a platoon, which is operated under Cooperative Adaptive Cruise Control (CACC). A typical CACC includes GPS, radar sensors, and Dedicated Short Range Communication (DSRC) devices to communicate with other CAVs in the platoon [20]. The platoon operations are illustrated in Figure 3.…”

Section: Scenario Descriptionmentioning

confidence: 99%

“…The platoon operations are illustrated in Figure 3. In particular, the radar sensors are used to measure the distance between the subject vehicle and its preceding vehicle; the GPS provides the location of other adjacent vehicles; while the DSRC devices are utilised for communication with nearby vehicles [20]. The platoon is moving on a road as represented in Figure 4.…”

Section: Scenario Descriptionmentioning

confidence: 99%

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A simplified approach for dynamic security risk management in connected and autonomous vehicles

Le¹,

Maple²

2019

Living in the Internet of Things (IoT 2019)

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“…In our system the measurements attained from sensors and V2V communication are fused to attain a better representation of the environment. In addition, based on the model predictive control (MPC) framework [31][32][33][34], a temporal longitudinal controller and a spatial lateral controller are developed together for smooth and safe vehicle manoeuvring. A number of field experiments are carried out to evaluate the performance of the perception and control methods of our platform in different road conditions.…”

Section: Fig 1 a Platoon Of Vehicles On The Roadmentioning

confidence: 99%

Development and verification of cooperative adaptive cruise control via LTE‐V

Wang

Chen

Wang

et al. 2019

IET Intelligent Transport Systems

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In this paper, we present a testbed platform for realizing cooperative adaptive cruise control (CACC) enabled by LTE-V (LTE-vehicle). The platform is developed on a platoon of vehicles, each of which is equipped with a suite of on-board sensing and computing devices for environment perception and automated vehicle control, as well as an LTE-V transceiver for high-performance vehicle-to-vehicle (V2V) communication. The hardware architecture and software architecture, especially the perception and control methods, of the platform are described. Field experiments in different road conditions are conducted to verify the feasibility of our platform. The results also show the potential of V2V communications via LTE-V in terms of improving the sensing capability of individual vehicle's on-board sensors.2 information (e.g., speed, wheel angle, heading and location, throttle and brake actions, etc.) to achieve better environment perception, has attracted wide research attentions. The dedicated short-range communications (DSRC) technology has been considered as the solution to realizing V2X communication for years. The US Federal Communication Commission (FCC) has allocated a 75 MHz spectrum in the 5.9 GHz band for DSRC [25]. The information exchanged among vehicles can be encoded into Basic Safety Message (BSM) and periodically broadcasted by vehicles according to the Society of Automotive Engineering (SAE) standard [26]. However, due to the ad hoc working nature, DSRC may not be able to fully guarantee highly-reliable and low-delay communication in all traffic conditions.In the past few years, the cellular-based LTE-V (LTE vehicle) technology [27] has started to be investigated in many countries and research communities. For example, a data exchange standard [28] is currently being developed by the Society of Automotive Engineers of China (SAE-China). Cooperative automated driving is seen as one of the most important use cases supported by LTE-V and LTE-eV2X (the next generation of LTE-V) according to 3GPP TR 22.886 [29]. LTE-V supports both V2V (vehicle-to-vehicle) communication over the peer-to-peer PC5 interface, and V2N (vehicle-to-network) communication over the LTE-Uu interface. Therefore, the interference management and scheduling of V2V communication traffic can be assisted by base stations to obtain a more reliable and efficient message dissemination compared with DSRC, especially in the highly mobile environment. The transmission delay of LTE-V is expected to be about 25ms through V2V direct communication, which meets the latency requirements of platooning defined in 3GPP TR 22.886. A comparative analysis between LTE-V and DSRC [30] is shown in Table 1, which clearly demonstrates the advantages of the former technology.

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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

Cited by 95 publications

References 34 publications

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

A simplified approach for dynamic security risk management in connected and autonomous vehicles

Development and verification of cooperative adaptive cruise control via LTE‐V

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