Information fusion for automotive applications – An overview

Stiller, Christoph; León, Fernando Puente; Kruse, Marco

doi:10.1016/j.inffus.2011.03.005

Cited by 63 publications

(31 citation statements)

References 51 publications

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“…The algorithms of data association can be divided into explicit data association algorithms and implicit data association algorithms [126]. Methods for explicit data association tracking vary widely: from the nearest neighbor (NN) algorithm [127], the multihypothesis tracking (MHT) approach [128], the probabilistic data association (PDA) approach [129], to the joint probability data association (JPDA) algorithms [130], [131].…”

Section: ) Measurement Uncertaintymentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

307

125

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Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

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Section: ) Measurement Uncertaintymentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

307

125

View full text Add to dashboard Cite

show abstract

“…This research finds that combining a VRS with a CS is a way to ensure that the overall system is robust, and technologically sophisticated (as with the VRS Sensor Fusion Concept). This section will cover how VRS technology enables a standard powertrain vehicle to a potentially improve fuel economy by as much as 14% [77].…”

Section: Vehicular Radar Systems 47mentioning

confidence: 99%

A Planning Tool to Assess Advanced Vehicle Sensor Technologies on Traffic Flow, Fuel Economy, and Emissions

Wifvat

2018

SAE Technical Paper Series

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“…Where, Q is surface gathered water volume, () PQ is the corresponding flood risk probability, q is rainstorm intensity, corresponding to rainstorm data and flood risk probability 1 P ;  is runoff coefficient, corresponding to the factual terrain and flood risk probability 2 P ; F is specific catchments area(SCA), corresponding to the land use and flood risk probability 3 P , which can be got from the remote sense image. The probability of urban flood risk is got from the product of probabilities of these three factors.…”

Section: Multi-source Information Integration Operator and Input Of Rmentioning

confidence: 99%

“…On the one hand, people benefit from the abundant information, and on the other hand, people bear the decision trouble because of the poor management of information. How to make the information fusion for the intelligent decision has been a hot topic 1,2,3 . Multi-source information fusion is proposed in 20 th century which refers processing the information from different data sources using computer technology.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Multi-source Information Fusion for Intelligent Flood Risk Analysis Based on Random Set Theory

Xie¹,

Yi²,

Tang³

et al. 2012

IJCIS

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Information fusion has been a hot topic currently, how to make information fusion for intelligent decision is a challenge. Although the applications of random set theory attract many researchers, the probability function distribution is still imprecise. In this paper, we give a new definition of probability distribution function (PDF) of random set theory, and propose an integration methodology for urban flood risk assessment by fusing multi-source information (e. g., remote sensing images, Digital Elevation Model (DEM) and rainstorm data) based on random set theory. The methodology analyzes and fuses the multi-source information, which overcomes the uncertainty of the decision makers of flood risk and generates precise estimates of the probability of flood risk. In our experiments, we take Wuhan city in China and three kinds of data sources information as an example to assess flood risk level. The experiments indicate that our algorithm not only provide precise estimates of the probability of flood risk but also give the bound of probability.

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Information fusion for automotive applications – An overview

Cited by 63 publications

References 51 publications

Overview of Environment Perception for Intelligent Vehicles

Overview of Environment Perception for Intelligent Vehicles

A Planning Tool to Assess Advanced Vehicle Sensor Technologies on Traffic Flow, Fuel Economy, and Emissions

Uncertainty Multi-source Information Fusion for Intelligent Flood Risk Analysis Based on Random Set Theory

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