Abstract
Detecting the road terrain ahead of the ego-vehicle is an important issue for modern driver assistance systems. In particular, vehicle motion planning in inner city environment requires the detection of road terrain up to 3 seconds in advance. State-of-the-art visual road terrain detection systems have a hard time fulfilling this task, due to their limited range and the presence of occlusions (other vehicles, buildings, etc.), which are expected to occur often in complex scenarios. However, those systems provide significant information where the conditions are favorable (proximity to the ego-vehicle, no occlusions). Therefore, a complementary approach is needed to enhance already existing and established detection systems. In this paper we propose a probabilistic grid-based approach based on the observation and interpretation of other vehicles' behavior in the scene. It exploits their movements in order to infer the presence and location of occluded road surface. We will show that this approach presents various advantages over current visual road terrain detection systems, especially in those situations that are the most challenging for them. We will illustrate how our approach is designed to work in concert also with other available resources, e.g. offline road maps. Qualitative results on real-world scenes taken from the KITTI benchmark[11] demonstrate that the fusion of this method with visual road terrain detection can potentially extend our time horizon well over the 3 seconds mentioned above. Finally, we will show how our approach is planned to develop into a semantically enriched representation of the road, including road properties such as availability, lanes and directions.
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Document information
Published on 31/12/14
Accepted on 31/12/14
Submitted on 31/12/14
Volume 2015, 2015
DOI: 10.1109/ivs.2015.7225698
Licence: Other
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