  This paper proposes an approach that predicts the road course from camera
sensors leveraging deep learning techniques. Road pixels are identified by
training a multi-scale convolutional neural network on a large number of
full-scene-labeled night-time road images including adverse weather conditions.
A framework is presented that applies the proposed approach to longer distance
road course estimation, which is the basis for an augmented reality navigation
application. In this framework long range sensor data (radar) and data from a
map database are fused with short range sensor data (camera) to produce a
precise longitudinal and lateral localization and road course estimation. The
proposed approach reliably detects roads with and without lane markings and
thus increases the robustness and availability of road course estimations and
augmented reality navigation. Evaluations on an extensive set of high precision
ground truth data taken from a differential GPS and an inertial measurement
unit show that the proposed approach reaches state-of-the-art performance
without the limitation of requiring existing lane markings.
