Fusion of digital map traffic signs and camera-detected signs

Jamshidi, Homayoun; Lukaszewicz, Thomas; Kashi, Amin; Berghuvud, Ansel; Zepernick, Hans‐Jürgen; Khatibi, Siamak

doi:10.1109/icspcs.2011.6140824

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…As compared to probabilistic methods, where there are only two states (happening or nonhappening) of an event, DST includes an unknown state to capture real-world uncertainty. [163,286] are the applications of DST for data fusion. However, increasing the data evidence also increases the complexity of this method.…”

Section: Evidence-based Data Fusionmentioning

confidence: 99%

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications

Qian

Wen

et al. 2020

ACM Comput. Surv.

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Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This paper provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy, and subsequently assess and compare existing standard techniques used at individual stages.

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Section: Evidence-based Data Fusionmentioning

confidence: 99%

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications

Qian

Wen

et al. 2020

ACM Comput. Surv.

View full text Add to dashboard Cite

show abstract

“…As compared to probabilistic methods, where there are only two states (happening or nonhappening) of an event, DST includes an unknown state to capture the real-world uncertainty. [122,224] are the applications of DST for data fusion. However, increasing the data evidence also increases the complexity of this method.…”

Section: Evidence-based Data Fusionmentioning

confidence: 99%

Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey

Wen

Jha

et al. 2019

Preprint

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Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompasses model training and implication involved in holistic development lifecycle of an IoT application often leads to complex system integration. This paper provides a comprehensive and systematic survey on the development lifecycle of ML-based IoT application. We outline core roadmap and taxonomy, and subsequently assess and compare existing standard techniques used in individual stage.

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“…Two difficulties remain not issued with the important system proposed in [7]. Firstly, the TSs detection with a camera-based system remains problematic.…”

Section: Introductionmentioning

confidence: 99%

“…Then, one can make TSs detection independently of atmospheric disturbances and traffic situations. An interesting approach [7], applied to speed limit TSs only, combines a camera based with a map-based system to provide information that is more precise. The first detects variable and temporary speed limits while the second detects implicit ones, such as when entering from a country road into a town when there is no TS indicating a speed limit.…”

Section: Introductionmentioning

confidence: 99%

AL-S2m: Soft road traffic Signs map for vehicular systems

Lahlouhi¹

2015

ijacsa

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Abstract-In this paper, we describe AL-S 2 m, a roadmap with traffic signs to be used in vehicular systems. AL-S 2 m is part of a more general system of traffic signs (TSs) management, called AL-S 2 , which includes two sides: central map server and client vehicular system. The server allows establishing, maintaining and disseminating AL-S 2 m. The client localizes the vehicle in AL-S 2 m and detects TSs. In this paper, we focus on the AL-S 2 m establishment. AL-S 2 m can handle variable TSs and its update is easy, which keeps it coherent with the reality. Also, it improves the map-matching algorithm. We implemented AL-S 2 m easily using an Android device.

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Fusion of digital map traffic signs and camera-detected signs

Cited by 10 publications

References 11 publications

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications

Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey

AL-S2m: Soft road traffic Signs map for vehicular systems

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