Flexible On-Board Stream Processing for Automotive Sensor Data

Schweppe, Hendrik; Zimmermann, Armin; Grill, D.

doi:10.1109/tii.2009.2037145

Cited by 32 publications

(18 citation statements)

References 16 publications

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“…For example, [16] proposed an onc 2017 Information Processing Society of Japan board stream processing method for engineering testing and diagnosis in vehicle systems. The Vehicle Data Stream Mining System (VEDAS) [17] and Minefleet [18] are distributed data stream mining platforms for use in mobile computing devices or automotive systems.…”

Section: Data Stream Management Systemsmentioning

confidence: 99%

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

Yamaguchi

Watanabe

Sato

et al. 2017

Journal of Information Processing

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Data stream management systems (DSMSs) are suitable for managing and processing continuous data at high input rates with low latency. For advanced driver assistance including autonomous driving, embedded systems use a variety of onboard sensor data with communications from outside the vehicle. Thus, the software developed for such systems must be able to handle large volumes of data and complex processing. We develop a platform that integrates and manages data in an automotive embedded system using a DSMS. However, because automotive data processing, which is distributed in in-vehicle networks of the embedded system, is time-critical and must be reliable to reduce sensor noise, it is difficult to identify conventional DSMSs that meet these requirements. To address these new challenges, we develop an automotive embedded DSMS (AEDSMS). This AEDSMS precompiles high-level queries into executable query plans when designing automotive systems that demand time-criticality. Data stream processing is distributed in in-vehicle networks appropriately, where real-time scheduling and senor data fusion are also applied to meet deadlines and enhance the reliability of sensor data. The main contributions of this paper are as follows: (1) we establish a clear understanding of the challenges faced when introducing DSMSs into the automotive field; (2) we propose an AEDSMS to tackle these challenges; and (3) we evaluate the AEDSMS during run-time for advanced driver assistance.

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Section: Data Stream Management Systemsmentioning

confidence: 99%

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

Yamaguchi

Watanabe

Sato

et al. 2017

Journal of Information Processing

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“…In [6] an approximate method is proposed to overcome these difficulties of duplicate-sensitive aggregation functions in faulty sensor networks. In a similar way, a flexible on-board stream processing method of sensor data of a vehicle is introduced in [21]. , b], ⊕, ⊙) (see [10], [14]).…”

Section: B In-network Aggregation In Sensor Networkmentioning

confidence: 99%

Information Fusion in Intelligent Systems

Milovanović¹

2016

Proceedings of the International Scientific Conference - Sinteza 2016

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Abstract:Information fusion in intelligent systems is one of the basic problems. This issue is rapidly increasing since more complex systems are being developed, e.g., robotics, vision, knowledge-based systems and data mining. The process of combining several values into a single representative one is called aggregation, and we present two applications of aggregation functions in multisensor data fusion and network aggregation in sensor networks. Special attention is devoted to the situation of pairs of aggregation functions under the semiring structure (pseudo-analysis). The author shall present two applications of it in image processing.

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“…The first utilizations have been in the automotive field. Schweppe et al proposed on-board stream processing for engineering testing and diagnosis in vehicle systems [21]. One of their main features was the adaptation of the behavior of data stream processing in diagnosis when critical events occur, e.g., when the reading rate of sensor data increases.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Embedded Data Stream Processing Platform for Android Devices

Akiyama¹,

Nakamoto²,

Yamaguchi³

et al. 2015

ijacsa

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Abstract-Automotive information services utilizing vehicle data are rapidly expanding. However, there is currently no data centric software architecture that takes into account the scale and complexity of data involving numerous sensors. To address this issue, the authors have developed an in-vehicle datastream management system for automotive embedded systems (eDSMS) as data centric software architecture. Providing the data stream functionalities to drivers and passengers are highly beneficial. This paper describes a vehicle embedded data stream processing platform for Android devices. The platform enables flexible query processing with a dataflow query language and extensible operator functions in the query language on the platform. The platform employs architecture independent of data stream schema in in-vehicle eDSMS to facilitate smoother Android application program development. This paper presents specifications and design of the query language and APIs of the platform, evaluate it, and discuss the results.

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Flexible On-Board Stream Processing for Automotive Sensor Data

Cited by 32 publications

References 16 publications

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

Information Fusion in Intelligent Systems

Vehicle Embedded Data Stream Processing Platform for Android Devices

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