Improvement of multisensor fusion in speed limit determination by quantifying navigation reliability

Abstract

To cite this version:Anne-Sophie Puthon, Fawzi Nashashibi, Benazouz Bradai. Improvement of multisensor fusion in speed limit determination by quantifying navigation reliability. Intelligent Transportation System Conference -ITSC 10, Sep 2010, Funchal, Portugal. pp.855 -860, 2010, <10.1109/ITSC.2010 .5625242>. <hal-00546167>Improvement of multisensor fusion in speed limit determination by quantifying navigation reliabilityAnne-Sophie Puthon, Fawzi Nashashibi and Benazouz BradaiAbstract-Speed limit determination is a complex task that may be solved by fusing data from GIS (Geographical Information System) and camera sensor. Among the existing data fusion models the Dempster-Shafer Belief Theory is found to be the most appropriate in this application. A confidence measure weights each source output, namely speed limit present on road sign and driving situation. Using the discounting scheme of Dempster-Shafer, we propose a new way of computing the navigation confidence measure by taking into account the reliability of the GIS. Preliminary tests showed that our method achieves promising results and solves conflicts between visionand navigation-based system.

Multi-Agent Robot System and Events Executing Using GPS Cloud

“…In this section we want to show and to make some experiments on the Multi-Agent System and how can help decreasing the number of car road crashes which are daily happening and as it is known the main cause is speed that is mean the human side which doesn't respect the traffic signs [8,9]. for that the multi agent system suggests a small plan for the assistant which is plugged already inside the car and basing on GPS speed limit detection value the agent will make some rules if necessary [8][9][10][11].…”

“…for that the multi agent system suggests a small plan for the assistant which is plugged already inside the car and basing on GPS speed limit detection value the agent will make some rules if necessary [8][9][10][11].…”

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

“…This concept was first introduced by [5] and [6] and developed by [7]. All the consistent information concerning the implicit speed limit determination is summarized by nine criteria, subdivided into two classes.…”

“…The confidences of both classes are then combined to compute a probability for the validity of each speed as illustrated in figure 4. More details are given in [7].…”

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

“…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.…”