@phdthesis {PUBA301,
	title = {Road Terrain Detection for Advanced Driver Assistance Systems},
	author = {Tobias K{\"u}hnl},
	year = {2013},
	month = {October},
	abstract = {In recent years, automotive manufacturers have equipped their vehicles with innovative Advanced Driver Assistance Systems (ADAS) to ease driving and avoid dangerous situations, such as unintended lane departures or collisions with other road users, like vehicles and pedestrians. To this end, ADAS at the cutting edge are equipped with cameras to sense the vehicle surrounding. An important source of information for future ADAS is the road course, i.e., the future driving path of the ego-vehicle and other vehicles. Therefore, this thesis focuses on the camera-based analysis of road scenes and the detection of important types of road terrain, such as road area and ego-lane, which are necessary to draw inference about the actual road course and potential space for evasion maneuvers.

For this purpose, this thesis presents a generic concept for the visual and spatial analysis of the road environment. The core of the proposed method is a hierarchical feature extraction that combines local visual appearance with its spatial layout. In this sense, a novel vision-based approach for road terrain detection that goes beyond classical lane marking detection and image segmentation approaches is presented. Thus, the approach enhances the ability to cope with noise and appearance changes because the classification decision is not only based on local visual appearance but on a combination of visual and spatial aspects. This results in a higher robustness under various visual conditions due to different asphalt appearance, illumination changes, and shadows.

The approach{\textquoteright}s generic architecture internally represents certain visual properties, such as road area, road boundary, and lane marking information by means of a visuospatial representation. In contrast to many related approaches for road terrain detection, the proposed method does not employ an explicit road course model. Instead, the method learns classifying road terrain based on a combination of visual and spatial features by using machine learning. Especially the discrimination between ego-lane and other parts of the road area is very challenging, because a distinction based on local appearance is impossible. Extensive evaluations in urban scenarios show that the proposed system functions in spatially diverse road scenes and reliably detects ego-lane and road area even in challenging situations. Those situations may comprise bad-quality or missing lane markings, curbstones delimiting the road, and occlusion of lane delimiters, e.g., caused by parked cars.

Furthermore, the generic concept does not only have advantages in road terrain detection, but also in many other applications, benefitting from visual and spatial scene analysis. In order to prove this, the method is applied for pure vision-based ego-vehicle localization on the lane level. In this regard, a reliable classification allowing an inference about how many lanes exist adjacent to the ego-lane is presented on a large highway dataset.

In summary, this thesis presents a generic concept for visual and spatial analysis of the road environment and is therefore a substantial contribution to the development of future ADAS. Towards this end, the general approach is geared to problem-solving for complex situations that can not be handled by state-of-the-art methods, which has been shown for inner-city road terrain detection and ego-vehicle localization.},
	publisher = {Bielefeld University},
	booktitle = {Bielefeld University},
	institution = {University of Bielefeld}
}