Autonomous driving is a key factor for future mobility. Properly perceiving the environment of
the vehicles is essential for a safe driving, which requires computing accurate geometric and …

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 …

Today, there are two major paradigms for vision-based autonomous driving systems:
mediated perception approaches that parse an entire scene to make a driving decision, and …

Today, visual recognition systems are still rarely employed in robotics applications. Perhaps
one of the main reasons for this is the lack of demanding benchmarks that mimic such …

With the market introduction of the 2014 Mercedes-Benz S-Class vehicle equipped with a
stereo camera system, autonomous driving has become a reality, at least in low speed …

Autonomous cars will likely play an important role in the future. A vision system designed to
support outdoor navigation for such vehicles has to deal with large dynamic environments …

In this paper we present Semantic Stixels, a novel vision-based scene model geared
towards automated driving. Our model jointly infers the geometric and semantic layout of a …

In this paper we present a probabilistic framework for increasing online object detection
performance when given a semantic 3D scene prior, which we apply to the task of traffic light …

The complete perception of the environment is an important prerequisite for realizing
autonomous driving. However, due to the existence of obstructions and the limits of sensing …

In autonomous driving systems a strong relation to highly accurate maps is taken to be
inevitable, although street scenes change frequently. However, a preferable system would …