Augmenting night vision video images with longer distance road course information

Today´s night vision driver assistance systems help the driver by displaying an infrared image and detecting and highlighting other road users such as pedestrians or cyclists. To further increase active safety, future night vision systems could also visualize road course information. Especially the road courses at greater distances can help drivers interpret upcoming scenes. However, longer distance road course estimation is a challenging task because on-board sensors have a limited viewing range. This paper proposes a sensor fusion system that employs digital map information in combination with radar and camera sensors to estimate the 3D road course even at longer distances. The positioning task on the digital map is solved by a Bayesian framework that estimates position probability by means of map registration. By fusing road course data from the digital map and an optical lane recognition module, an accurate 3D road course estimation is obtained.