ANALYSIS OF SPATIO-TEMPORAL TRAFFIC PATTERNS BASED ON PEDESTRIAN TRAJECTORIES
For driver assistance and autonomous driving systems, it is essential to predict the behaviour of other traffic participants. Usually, standard filter approaches are used to this end, however, in many cases, these are not sufficient. For example, pedestrians are able to change their speed or direc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/497/2016/isprs-archives-XLI-B2-497-2016.pdf |
| Summary: | For driver assistance and autonomous driving systems, it is essential to predict the behaviour of other traffic participants. Usually,
standard filter approaches are used to this end, however, in many cases, these are not sufficient. For example, pedestrians are able to
change their speed or direction instantly. Also, there may be not enough observation data to determine the state of an object reliably,
e.g. in case of occlusions. In those cases, it is very useful if a prior model exists, which suggests certain outcomes. For example, it is
useful to know that pedestrians are usually crossing the road at a certain location and at certain times. This information can then be
stored in a map which then can be used as a prior in scene analysis, or in practical terms to reduce the speed of a vehicle in advance in
order to minimize critical situations. In this paper, we present an approach to derive such a spatio-temporal map automatically from the
observed behaviour of traffic participants in everyday traffic situations. In our experiments, we use one stationary camera to observe
a complex junction, where cars, public transportation and pedestrians interact. We concentrate on the pedestrians trajectories to map
traffic patterns. In the first step, we extract trajectory segments from the video data. These segments are then clustered in order to derive
a spatial model of the scene, in terms of a spatially embedded graph. In the second step, we analyse the temporal patterns of pedestrian
movement on this graph. We are able to derive traffic light sequences as well as the timetables of nearby public transportation. To
evaluate our approach, we used a 4 hour video sequence. We show that we are able to derive traffic light sequences as well as time
tables of nearby public transportation. |
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| ISSN: | 1682-1750 2194-9034 |