Understanding High-Level Semantics by Modeling Traffic Patterns

Hongyi Zhang, Andreas Geiger, Raquel Urtasun; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 3056-3063

Abstract


In this paper, we are interested in understanding the semantics of outdoor scenes in the context of autonomous driving. Towards this goal, we propose a generative model of 3D urban scenes which is able to reason not only about the geometry and objects present in the scene, but also about the high-level semantics in the form of traffic patterns. We found that a small number of patterns is sufficient to model the vast majority of traffic scenes and show how these patterns can be learned. As evidenced by our experiments, this high-level reasoning significantly improves the overall scene estimation as well as the vehicle-to-lane association when compared to state-of-the-art approaches [10].

Related Material


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[bibtex]
@InProceedings{Zhang_2013_ICCV,
author = {Zhang, Hongyi and Geiger, Andreas and Urtasun, Raquel},
title = {Understanding High-Level Semantics by Modeling Traffic Patterns},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}