| Autor: |
Antoine, Caillot, Safa, Ouerghi, Yohan, Dupuis, Pascal, Vasseur, Rémi, Boutteau |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| DOI: |
10.5281/zenodo.7637904 |
| Popis: |
The idea of cooperative perception for navigation assistance was intro- duced about a decade ago with the aim to increase safety on dangerous areas like intersections. In this context, roadside infrastructure appeared very recently to provide a new point of view of the scene. In this paper, we propose to combine the Vehicle-To-Vehicle (V2V) and Vehicle- To-Infrastructure (V2I) approaches in order to take advantage of the elevated points of view offered by the infrastructure and the in-scene points of view offered by the vehicles to build a semantic grid map of the moving elements in the scene. To create this map, we chose to use camera information and 2-Dimentional (2D) bounding boxes in order to minimize the impact on the network and ignored possible depth informa- tion as opposed to all state-of-the art methods. We propose a framework based on two fusion methods: one based on the Bayesian theory and the other on the Dempster-Shafer Theory (DST) to merge the information and chose a label for each cell of the semantic grid in order to assess the best fusion method. Finally, we evaluate our approach on a set of datasets that we generated from the CARLA simulator varying the pro- portion of Connected Vehicle (CV) and the traffic density. We also show the superiority of the method based on the DST with a gain on the mean intersection over union between the two methods of up to 23.35 %. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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