Multi-sensor data fusion using Bayesian programming : an automotive application

Coué, Christophe; Fraichard, Thierry; Bessìère, Pierre; Mazer, Emmanuel

doi:10.1109/irds.2002.1041379

Cited by 42 publications

(18 citation statements)

References 5 publications

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“…Presently, two data fusion methods have been tested to merge change detection satellite features, vulnerability data and other non Earth Observation data. One method is based on a Naïve Bayesian Classification [3] approach, and the other exploits the Support Vector Machine [4] classification algorithm. The output is a Damage Map (see Fig.…”

Section: The Ape Methodsmentioning

confidence: 99%

Advanced procedures for volcanic and seismic monitoring

Iorio¹,

Stramondo²,

Bignami³

et al. 2016

Int. J. SAFE

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European Union FP7 APhoRISM project proposes the development and testing of two new methods to combine Earth Observation satellite data from different sensors and ground data. The aim is to demonstrate that this two types of data, appropriately managed and integrated, can provide new improved Copernicus Emergency products useful for seismic and volcanic crisis management. The first method, APE -A Priori information for Earthquake damage mapping, concerns the generation of maps to address the detection and estimate of damage caused by a seismic event. The novelty of APE relies on the exploitation of a priori information derived by InSAR time series to measure surface movements, shake maps obtained from seismological data, and buildings vulnerability information. The second method, MACE -Multi-platform volcanic Ash Cloud Estimation, concerns the exploitation of GEO sensor platform, LEO satellite sensors and ground measures to improve the ash detection and retrieval, and to characterize the volcanic ash clouds. INGV covers the role of project coordinator and ALMA Sistemi is participating in the exploitation and dissemination of the project results.

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Section: The Ape Methodsmentioning

confidence: 99%

Advanced procedures for volcanic and seismic monitoring

Iorio¹,

Stramondo²,

Bignami³

et al. 2016

Int. J. SAFE

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“…Moreover, the Bayesian Occupancy Filter (BOF) proposal handles uncertainty and noisy readings by using Bayesian theory [8], which increases the robustness of the system. In addition, the use of the prediction/estimation framework allows to take into account history of occupation and hence overcome occlusion events and substantiate occupancy estimation.…”

Section: The Bayesian Occupancy Filtermentioning

confidence: 99%

“…Further, in [8], modeled sensors work simultaneously, while [4] improves the system taking into account asynchronous sensors.…”

Section: Sensor Data Pre-processingmentioning

confidence: 99%

A Review of the Bayesian Occupancy Filter

Saval-Calvo

Medina-Valdés

Castillo-Secilla

et al. 2017

Sensors

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Autonomous vehicle systems are currently the object of intense research within scientific and industrial communities; however, many problems remain to be solved. One of the most critical aspects addressed in both autonomous driving and robotics is environment perception, since it consists of the ability to understand the surroundings of the vehicle to estimate risks and make decisions on future movements. In recent years, the Bayesian Occupancy Filter (BOF) method has been developed to evaluate occupancy by tessellation of the environment. A review of the BOF and its variants is presented in this paper. Moreover, we propose a detailed taxonomy where the BOF is decomposed into five progressive layers, from the level closest to the sensor to the highest abstract level of risk assessment. In addition, we present a study of implemented use cases to provide a practical understanding on the main uses of the BOF and its taxonomy.

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“…Bayesian Occupancy Filters (BOF) [5], [6], [9] have been applied to object tracking. BOF model the environment using a grid representation, with additional velocity components.…”

Section: Related Workmentioning

confidence: 99%

Conditional transition maps: Learning motion patterns in dynamic environments

Kucner

Saarinen

Magnusson

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper we introduce a method for learning motion patterns in dynamic environments. Representations of dynamic environments have recently received an increasing amount of attention in the research community. Understanding dynamic environments is seen as one of the key challenges in order to enable autonomous navigation in real-world scenarios. However, representing the temporal dimension is a challenge yet to be solved. In this paper we introduce a spatial representation, which encapsulates the statistical dynamic behavior observed in the environment. The proposed Conditional Transition Map (CTMap) is a grid-based representation that associates a probability distribution for an object exiting the cell, given its entry direction. The transition parameters are learned from a temporal signal of occupancy on cells by using a localneighborhood cross-correlation method. In this paper, we introduce the CTMap, the learning approach and present a proof-ofconcept method for estimating future paths of dynamic objects, called Conditional Probability Propagation Tree (CPPTree). The evaluation is done using a real-world data-set collected at a busy roundabout.

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Multi-sensor data fusion using Bayesian programming : an automotive application

Cited by 42 publications

References 5 publications

Advanced procedures for volcanic and seismic monitoring

Advanced procedures for volcanic and seismic monitoring

A Review of the Bayesian Occupancy Filter

Conditional transition maps: Learning motion patterns in dynamic environments

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