A complete system to determine the speed limit by fusing a GIS and a camera

Puthon, Anne-Sophie; Nashashibi, Fawzi; Bradai, Benazouz

doi:10.1109/itsc.2011.6082951

Cited by 6 publications

(8 citation statements)

References 19 publications

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“…But this operator is non-associative [41] implying the definition of a fusion order between the sources. In addition, the conflict is masked in the ignorance, which can be arguable in some cases 3 .…”

Section: Conflict-related Operatorsmentioning

confidence: 99%

“…The GIS gives numerous static road contextual data and the vision system gives dynamic and up to date information about the local driving context. Their combination allows the SLA to become closer to human reasoning [3]. Several techniques can be employed for their combination such as Voting techniques [4], Bayesian approaches [5], the Possibility theory [6], the Belief theory [7,8], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Several techniques can be employed for their combination such as Voting techniques [4], Bayesian approaches [5], the Possibility theory [6], the Belief theory [7,8], etc. If few studies using the Bayesian theory are proposed in the literature [9], most SLAs are based on belief functions [3,[10][11][12]. Indeed, belief functions constitute an intuitive, suitable framework for data imperfections (inaccuracy, incompletion, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, this approach does not consider the GIS inaccuracies, such as positioning errors due to the GP S and/or Dead-Reckoning (DR) sensor integration, digitalization errors of the road network, etc. To overcome these limitations, [3] and [13] proposed to take account of the GIS criteria regarding their nature: several attributes inform about the relevancy of the vehicle position estimation, while others are related to the description of the static driving context. This distinction helps in the definition of the amount of confidence given to each speed candidate with respect to the GIS estimated relevancy.…”

Section: Introductionmentioning

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: Conflict-related Operatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…These systems allow multiple improvements in driving assistance and some partial control such as: blind angle detection systems [1], lane departure warning [2], speed limit warning [3], pedestrian collision avoidance [4] and parking assistance [5] (among others). Nevertheless, some other intelligent transportation systems (ITS) topics are improved for research groups around the world.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Docking Based on Infrared System for Electric Vehicle Charging in Urban Areas

Pérez

Nashashibi

Lefaudeux

et al. 2013

Sensors

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Electric vehicles are progressively introduced in urban areas, because of their ability to reduce air pollution, fuel consumption and noise nuisance. Nowadays, some big cities are launching the first electric car-sharing projects to clear traffic jams and enhance urban mobility, as an alternative to the classic public transportation systems. However, there are still some problems to be solved related to energy storage, electric charging and autonomy. In this paper, we present an autonomous docking system for electric vehicles recharging based on an embarked infrared camera performing infrared beacons detection installed in the infrastructure. A visual servoing system coupled with an automatic controller allows the vehicle to dock accurately to the recharging booth in a street parking area. The results show good behavior of the implemented system, which is currently deployed as a real prototype system in the city of Paris.

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