Conflict management in multi-sensor dempster-shafer fusion for speed limit determination

Daniel, Jérémie; Lauffenburger, Jean-Philippe

doi:10.1109/ivs.2011.5940398

Cited by 8 publications

(10 citation statements)

References 6 publications

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“…The work presented here extends previous papers [15,19,20] by providing a study of the conflict management strategies and a conflict-based decision rule in the multisensor context. Based on real experiments, a qualitative and quantitative evaluation of the system performance is given.…”

Section: Introductionsupporting

confidence: 54%

“…The impact of the local decision is shown in [15]. Simulations and real-time experiments with the test car were carried out in [19] to compare two conflict management solutions. The first strategy was to select the speed with the maximum of belief considering a simple threshold constraint, while the second was to redistribute the eventual conflict using the P CR5.…”

Section: Resultsmentioning

confidence: 99%

“…1), a linear evolution of the bba is considered here. The particularity of the proposed bba is to define its evolution with respect to the relevancies of the navigation (R GIS ) and the vision (R V IS ) systems respectively described by (18) and (19). This makes sense since a sensor which presents a low relevancy may provide an information unadapted to the driving situation encountered.…”

Section: Basic Belief Assignmentmentioning

confidence: 99%

“…For the global association, the frame of discernment is then considered to be only composed of both sensor speeds. The combination equations for 2 sources which believe either in the same hypothesis or not, are presented in [19].…”

Section: Multi-sensor Combinationmentioning

confidence: 99%

“…(24) differs from the decision strategy of [19]. In the latter, the constrained Credibility maximum with a constant threshold has been used without consideration of the conflict level.…”

Section: Conflict As a Criterion For Decision Makingmentioning

confidence: 99%

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Fusing navigation and vision information with the Transferable Belief Model: Application to an intelligent speed limit assistant

Daniel

Lauffenburger

2014

Information Fusion

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The present paper focuses on the fusion, based on imprecise and uncertain information, between a Geographic Information System (GIS) and a Speed Limit Sign Recognition System (SLSRS), performed on camera images. This study is dedicated to the development of a Speed Limit Assistant (SLA) in the context of vehicle driving aid. The proposed SLA is developed within the Evidence Theory framework. The information from the sources is interpreted as belief functions using a non antagonistic bba in the Transferable Belief Model (TBM) semantics. This bba ensures that the conflict which could appear after the global fusion is exclusively due to source discordances. The present paper proposes a way to manage these discordances by formalizing a conflictrelated constraint decision rule. As far as the application is concerned, a two-level (decentralized) fusion architecture is developed. The sensor relevancy is estimated in a first step, followed by the GIS intra-sensor fusion with a maximum of Credibility decision which determines the context-compliant speed candidate considering the road information given by the digital map. This allows the detection of possible errors of the GIS. The multi-sensor fusion then combines the GIS and SLSRS information considering that the sensors are independent and specialized in one speed, each. For the decision, two strategies are adopted. The first one considers the conflict as a threshold for the final speed selection, and so allows the SLA to stay undecided in case of highly conflicting situations. The second strategy employs the 5 th version of the Proportional Conflict Redistribution operator. The SLA has been tested in simulation and in real-time experiments by qualitative and quantitative performance evaluations.

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Section: Introductionsupporting

confidence: 54%

Section: Resultsmentioning

confidence: 99%

Section: Basic Belief Assignmentmentioning

confidence: 99%

Section: Multi-sensor Combinationmentioning

confidence: 99%

“…(24) differs from the decision strategy of [19]. In the latter, the constrained Credibility maximum with a constant threshold has been used without consideration of the conflict level.…”

Section: Conflict As a Criterion For Decision Makingmentioning

confidence: 99%

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Fusing navigation and vision information with the Transferable Belief Model: Application to an intelligent speed limit assistant

Daniel

Lauffenburger

2014

Information Fusion

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Multi-level Dempster-Shafer Speed Limit Assistant

Daniel¹,

Lauffenburger²

2012

Advances in Intelligent and Soft Computing

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Triangular traffic signs detection based on RSLD algorithm

Boumediene

Cudel

Basset

et al. 2013

Machine Vision and Applications

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This paper describes an efficient method for the detection of triangular traffic signs on grey-scale images. This method is based on the proposed RSLD (RANSAC Symmetric Lines Detection) algorithm which transforms triangle detection into a simple segment detection. A multi-scale approach allows the detection of any warning and yield traffic signs, whatever their distance to the vehicle. This algorithm is applied to a set of selected corners obtained with a coding gradient method. Baseline detection uses the scale of selected triangles to confirm the presence of traffic signs. The study demonstrates that RSLD is a low computation method compared to standard triangle detection. The performance of the method proposed is compared with recently published methods on road sign databases, which use colour information. An equivalent detection rate is obtained with this algorithm, working on grey-scale images. This algorithm is implemented and runs in realtime at 30 frames per second.

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Conflict management in multi-sensor dempster-shafer fusion for speed limit determination

Cited by 8 publications

References 6 publications

Fusing navigation and vision information with the Transferable Belief Model: Application to an intelligent speed limit assistant

Fusing navigation and vision information with the Transferable Belief Model: Application to an intelligent speed limit assistant

Multi-level Dempster-Shafer Speed Limit Assistant

Triangular traffic signs detection based on RSLD algorithm

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