  Lane detection is an essential part of the perception sub-architecture of any
automated driving (AD) or advanced driver assistance system (ADAS). When
focusing on low-cost, large scale products for automated driving, model-driven
approaches for the detection of lane markings have proven good performance.
More recently, data-driven approaches have been proposed that target the
drivable area / freespace mainly in inner-city applications. Focus of these
approaches is less on lane-based driving due to the fact that the lane concept
does not fully apply in unstructured, residential inner-city environments.
So-far the concept of drivable area is seldom used for highway and inter-urban
applications due to the specific requirements of these scenarios that require
clear lane associations of all traffic participants. We believe that
lane-based, mapless driving in inter-urban and highway scenarios is still not
fully handled with sufficient robustness and availability. Especially for
challenging weather situations such as heavy rain, fog, low-standing sun,
darkness or reflections in puddles, the mapless detection of lane markings
decreases significantly or completely fails. We see potential in applying
specifically designed data-driven freespace approaches in more lane-based
driving applications for highways and inter-urban use. Therefore, we propose to
classify specifically a drivable corridor of the ego lane on pixel level with a
deep learning approach. Our approach is kept computationally efficient with
only 0.66 million parameters allowing its application in large scale products.
Thus, we were able to easily integrate into an online AD system of a test
vehicle. We demonstrate the performance of our approach under challenging
conditions qualitatively and quantitatively in comparison to a state-of-the-art
model-driven approach.
