Combined Road Tracking for Paved Roads and Dirt Roads: Framework and Image Measurements.

We propose a modular framework for 3D tracking not only of paved roads but also of dirt roads. It is based on recursive state estimation of lane boundary points connected by clothoid pieces. While our tracking is flexible to integrate every kind of measurement, we specifically propose two image-based measurements. They combine traditional with modern computer vision: On the one hand, we show how to use directed edge detection to robustly measure road and lane boundaries. On the other hand, we introduce a innovative CNN-based measurement utilizing the self-similarity of (dirt) road areas. We demonstrate the performance of our approach in challenging scenarios. On a marked road, we achieve a median error of 0.13 m for the ego lane's boundaries in 25 m look-ahead. A difficult dirt road can also be tracked reliably with a lookahead length of 25 m, resulting in a median error of 0.3 m. The tracking, as well as both measurements, are real-time capable.