Intelligent systems in the automotive industry: applications and trends

Gusikhin, Oleg; Rychtyckyj, Nestor; Filev, Dimitar

doi:10.1007/s10115-006-0063-1

Cited by 60 publications

(20 citation statements)

References 33 publications

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“…Für die Automobilindustrie ist Maschinelles Lernen und Künstliche Intelligenz nicht nur bei der Automatisierung der Fahrzeugführung von Interesse, sondern auch in anderen Bereichen wie dem Design, der Produktion oder dem After-Sales-Management [19] …”

Section: Die Automation Die Den Lernenden Fahrer Ersetztunclassified

Lernen autonome Fahrzeuge?

Wachenfeld

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2015

Autonomes Fahren

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Section: Die Automation Die Den Lernenden Fahrer Ersetztunclassified

Lernen autonome Fahrzeuge?

Wachenfeld

Winner

2015

Autonomes Fahren

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“…For the car industry, machine learning and artificial intelligence are of interest not only for the automation of driving, but also in other fields such as design, production and after-sales management [19]. These areas, and that of infotainment, are not our concern, however.…”

Section: Automation That Replaces the Learning Drivermentioning

confidence: 99%

Do Autonomous Vehicles Learn?

Wachenfeld

Winner

2016

Autonomous Driving

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With autonomous driving, a technological system will replace humans in driving automobiles. The car industry, universities, and large IT companies, are currently working on implementing functions permitting a technological system to take on vehicle operation. Their focus is on the tasks that are also done by humans: of perception, cognition, deciding how to act (planning) and carrying out this behavior (acting). In addition, humans possess further capabilities not directly connected to driving a vehicle. For example, learning directly changes people's capacity to tackle tasks. In the driver-vehicle-environment system, this human capability raises a question: Will the technological system that is to replace humans also exhibit a capacity to learn? In the most diverse fields, primarily IT-driven ones, there are learning and learned systems of the most varied kinds, which rival conventional analytical systems in their performance. What marks out vehicle automation, though, is firstly its relevance to safety; secondly, how cars as a product additionally differ from other IT-industry goods in their system life cycles. Both of these particularities, with their challenges and attempts at solutions, are the subject of this chapter. Attention is also given to collective learning in the context of autonomous driving, as directly exchanging with and copying from the learned is one of the particular advantages machine learning has over the human version.Replicating human learning in machine learning occupies a whole area of research. It is expected that examining the processes of human learning will both provide a deeper

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“…[24][25][26][27] Moreover, in this article, a middleware-based fault-tolerant Ethernet connection is developed for process control networks with no change to COTS hardware and software and is transparent to IP-based applications achieving less than 1 ms end-to-end swap time and less than 2 s failover time. 5 Multiple-port NICs with the embedded processor, PCI port, and data storage capabilities are being developed by different companies, 17,18,[28][29][30] but still the existing embedded software needs to be upgraded to achieve the optimum fault-tolerance capabilities.…”

Section: Related Workmentioning

confidence: 99%

Highly scalable intelligent sensory application and time domain matrix for safety-critical system design

Jeong

2018

International Journal of Distributed Sensor Networks

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The designs of highly scalable intelligent sensory application-Ethernet-based communication architectures-are moving toward the integration of a fault recovery and fault-detection algorithm on the automotive industry. In particular, each port on the same network interface card design is required to provide highly scalable and low-latency communication. In this article, we present a study of intelligent sensory application for the Ethernet-based communication architecture and performance of multi-port configuration which is mainly used in safety-enhanced application such as automotive, military, finance, and aerospace, in other words, safety-critical applications. Our contributions and observations on the highly scalable intelligent behavior: (1) proposed network interface card board design scheme and architecture with multi-port configuration are a stable network configuration; (2) timing matrix is defined for fault detection and recovery time; (3) experimental and related verification methods by cyclic redundancy check between client-server and testing platform provide comparable results to each port configurations; and (4) application program interface-level algorithm is defined to make network interface card ready for fault detection.

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Intelligent systems in the automotive industry: applications and trends

Cited by 60 publications

References 33 publications

Lernen autonome Fahrzeuge?

Lernen autonome Fahrzeuge?

Do Autonomous Vehicles Learn?

Highly scalable intelligent sensory application and time domain matrix for safety-critical system design

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