Self-Reconfiguration for Fault-Tolerant FlexRay Networks

Klobedanz, Kay; Köenig, Andreas; Mueller, Wolfgang; Rettberg, Achim

doi:10.1109/isorcw.2011.38

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…By comparing the extracted metadata, errors can be detected very effectively through the novel algorithm in [37], although costing more message overhead and processing time. Furthermore, FlexRay network can be reconfigured in real-time without restart now thanks to the heuristic distributed coordinator proposed in [38], despite only 80% of individual errors can be spotted and then trigger the reconfiguration process.…”

Section: A Flexray Fault Detection Capabilitymentioning

confidence: 99%

A qualitative comparison of FlexRay and Ethernet in vehicle networks

Zeng

Khalid

Chowdhury

2015

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

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This paper presents a qualitative study of FlexRay and Ethernet in in-vehicle communication networks. Although both protocols have experienced fast growth in the past years, they still have some important deficiencies that deserve attention. This qualitative study has not only outlined the important shortcomings of in-vehicle FlexRay and Ethernet, but also identified their unique competitive edges, respectively. Intensive analyses of both protocols are carried out from three key perspectives: system cost, data transmission capacity and fault detection capability. Some improving approaches have also been pointed out. It is revealed that at the current stage FlexRay is better than Ethernet in transmitting time critical signals deterministically, but has higher cost and complexity. Ethernet, though less deterministic than FlexRay, possesses much greater bandwidth and can transmit data at quite small latency and jitter. This qualitative study has indicated that both protocols need further improvement to meet the requirements of future in-vehicle networks, yet Ethernet may lead the development and expand faster. I. INTRODUCTIONEncoded communication channels have long been used to transmit signals among different controllers in in-vehicle networks. The typical motivations for this fact are: 1) To reduce cable cost. Since wire harness subsystem is the third most expensive and heaviest subsystem after engine and chassis [1], adopting coded communication is obviously a wise strategy to follow. 2) To save package space. As vehicles are built smaller in size but more enriched in functions, routing of wire harness has become increasingly challenging. 3) To cater the demands for higher bandwidth. Nowadays some luxury cars, even not fully featured, already have more than 70 ECUs and 2500 signals to transmit [2], letting alone accommodating the rising demands from ADAS and infotainment systems. 4) To enhance communication safety. Digital transmission has unparalleled advantages in resisting external disturbance over traditional point-to-point analog wires. Moreover, digital signals can be encrypted to further enhance data security in transmission.After LIN (Local Interconnect Network) and CAN (Controller Area Network), FlexRay and Ethernet are two most recently developed wired in-vehicle communication networks. FlexRay was firstly public released in 2004 and has gradually become an ISO standard after 2009. FlexRay is commonly understood as the protocol for X-by-Wire applications, and can provide deterministic communication for chassis and vehicle dynamic controls. BMW is the first

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Section: A Flexray Fault Detection Capabilitymentioning

confidence: 99%

A qualitative comparison of FlexRay and Ethernet in vehicle networks

Zeng

Khalid

Chowdhury

2015

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

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“…FlexRay is an automotive network communication protocol developed by the FlexRay Consortium to govern on-board automotive computing. It is discussed in several research efforts [46,47,48]. The Local Interconnect Network (LIN) bus is a serial communication protocol used to connect components in vehicles.…”

Section: Communicationmentioning

confidence: 99%

The smart surface network: A bus-based approach to dense sensing

et al. 2015

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We present the Smart Surface Network (SSN), a hardware and software platform designed for dense sensing. Sensor nodes connected to the SSN communicate using a serial bus integrated within a mountable physical surface. The hardware architecture and bus access and communication mechanisms are implemented in a self-stabilizing manner, providing robust handling of unannounced arrivals and departures of network devices. An associated API supports a peer-to-peer communication paradigm, providing access to the physical, data link, and application layers of the bus. In this paper, we describe the SSN hardware architecture and present the bus access and peer discovery algorithms. We also discuss the design of the API and describe experimental results characterizing the fairness of the bus algorithm, the efficiency of the peer discovery algorithm, and the performance of the SSN system under varying load conditions.

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“…The approach also reduces the communication overhead, compared to their initial work. [92] improves further, using a self-organising distributed coordinator, which uses predetermined information about communication dependencies to determine valid (re)configurations for nodes on a FlexRay network. Here, reconfiguration refers to the tasks assigned to each of the static modules defined within the hardware setup.…”

Section: Performance Applications and Limitationsmentioning

confidence: 99%

“…A fault-tolerant system is more robust and can adapt and recover from faulty situations without severely degrading system performance. Fault-tolerance is achieved by building redundancy for critical operations and intelligence to switch to redundant logic, while a primary unit recovers from erroneous conditions, for example [92]. Fault-tolerance can be achieved at the individual node level or at a global level for the entire function.…”

Section: Reusing Resources With Dynamic Reconfigurationmentioning

confidence: 99%

Enhancing automotive embedded systems with FPGAs

Shreejith¹

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Modern vehicles represent a complex distributed cyber-physical system that simultaneously handles critical functions like drive-by-wire systems, non-critical functions like window/door control, and compute intensive multimedia functions. Distributed electronic control units (ECUs), which integrate processing elements and supporting peripherals (network interfaces, memory), implement a variety of functions in software, and information is exchanged between ECUs and sensors/actuators over in-vehicle networks. As the complexity of applications rises with increasing automation, extensive hardware support is required in the form of multicore processors or special purpose hardware accelerators to offer required levels of performance. Additional features also drive an increase in the number of ECUs since new functions are rarely consolidated on existing ECUs. Furthermore, network interfaces implemented as ASICs or dedicated logic must be adapted to handle increased communication loads, consuming power and requiring more infrastructure support in terms of cabling and weight. The increasing number of safety-critical functions further impacts complexity if existing one-to-one redundancy schemes are applied. Rising automation also poses news challenges like security, with researchers showing that internal networks are easily manipulated with catastrophic effects and total loss of control. Our research aims to address these challenges using architectural enhancements that are transparent at the computational and network levels, leveraging the capabilities of reconfigurable hardware. We present advanced ECU architectures with extended network capabilities, apply these in the context of safety critical systems, explore ways of extending these schemes to offer advanced security features, and show how such advanced systems can be validated in hardware. Our work represents an advancement in the state of the art with regard to applying FPGAs in vehicular systems.

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Self-Reconfiguration for Fault-Tolerant FlexRay Networks

Cited by 6 publications

References 7 publications

A qualitative comparison of FlexRay and Ethernet in vehicle networks

A qualitative comparison of FlexRay and Ethernet in vehicle networks

The smart surface network: A bus-based approach to dense sensing

Enhancing automotive embedded systems with FPGAs

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