Non-parametric Lane Estimation in Urban Environments

Lane estimation of the ego vehicle plays a key role in navigating a car through unknown areas. In fact, solving this problem is a prerequisite for any vehicle driving autonomously in previously unmapped areas. Most of the proposed methods for lane detection are tuned for freeways and rural environments. In urban scenarios, however, they are unable to reliably detect the ego lane in many situations. Often, these methods simply work on the principle of fitting a parametric model to lane markers. Since a large variety of lane shapes are found in urban environments, it is obvious that these models are too restrictive. Moreover, the complex structure of intersection-like situations further hampers the success of the aforementioned methods. Therefore we propose a non-parametric lane model which can handle a wide range of different features such as grass verge, free space, lane markers etc. The ego lane estimation is formulated as a shortest path problem. A directed acyclic graph is constructed from the feature pool rendering it efficiently solvable. The proposed approach is easily extendable as it is able to cope with pixel-wise low level features as well as high-level ones jointly. We demonstrate the potential of our method in urban and rural areas and present experimental findings on difficult real world data sets.