Where can I drive? A System Approach: Deep Ego Corridor Estimation for Robust Automated Driving

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.