A Global Navigation Management Architecture Applied to Autonomous Robots in Urban Environments

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

et al. 2022

Many studies faced the problem of vehicle autonomous navigation in different fields, but nowadays just a few of them uses all the implicit information coming from the context in which such navigation is occurring. This results in a huge potential information loss that prevents us from adapting the vehicle's behavior to each different situation it may be in. In a previous work, we defined a method to model the static context of navigation using ontologies and take it into account in the command law when performing a local navigation task. In this paper, we extend our model of the context of navigation, and define a software architecture able to update the context dynamically, by using sensor information. The method is tested with real-time experiments on driving simulator. They show that the Context of Navigation can be effectively updated during the navigation and leads to a smarter vehicle's behavior on the road.

Section: Attribute Meaning Cp Description Nodeidmentioning

confidence: 99%

Dynamic Context Awareness in Autonomous Navigation

Moussaoui

Pousseur

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

et al. 2022

On the Implementation of a Semantic Model for Intelligent Vehicle Navigation

Lecture Notes in Networks and Systems

Abel

2022

This paper considers the problem related to the navigation of multiactuated intelligent vehicles in interaction with its occupants and its environment, by modeling and describing the semantics of the contexts associated to the movement. That is, formally defining the semantic model of the navigation context, as obstacles, pedestrians, roundabouts, information on the vehicle itself (load, power, etc.). This semantic modeling generates an additional degree of abstraction by "reasoning" about the information collected by the various sensors of an autonomous vehicle in order to build a navigation context for the latter. This navigation context is able to provide decision support for the autonomous or semiautonomous vehicle (interaction to the driver). In this paper, we show initial modeling results by defining and selecting the relevant information in the creation of a context for the vehicle, structuring the contextual information so as to be able to reasoning on it and thus dynamically update the context in which the vehicle operates; maintaining information by choosing suitable data persistence models in order to be able to query previous simulations efficiently.

Autonomous vehicle navigation with semantical modelling: A project overview

2022 IEEE/SICE International Symposium on System Integration (SII)

Abel

2022

In this paper we consider the problem related to the autonomous navigation of a robotic vehicle by modeling and describing the semantics of the context associated to the navigation. Considering the semantical modelling of the navigation context, as obstacles, pedestrians, roundabouts, information on the vehicle itself (load, power, etc.), on the robotic vehicle, it is integrating to add this symbolic artificial intelligence to a classical navigation control scheme. This semantic modeling generates an additional degree of abstraction by "reasoning" about the information collected by the various sensors of the autonomous vehicle in order to build a navigation context. This navigation context is able to provide decision support for the autonomous or semi-autonomous vehicle (interaction to the driver). In this paper, we present a project developed at