  Decreasing costs of vision sensors and advances in embedded hardware boosted
lane related research detection, estimation, and tracking in the past two
decades. The interest in this topic has increased even more with the demand for
advanced driver assistance systems (ADAS) and self-driving cars. Although
extensively studied independently, there is still need for studies that propose
a combined solution for the multiple problems related to the ego-lane, such as
lane departure warning (LDW), lane change detection, lane marking type (LMT)
classification, road markings detection and classification, and detection of
adjacent lanes (i.e., immediate left and right lanes) presence. In this paper,
we propose a real-time Ego-Lane Analysis System (ELAS) capable of estimating
ego-lane position, classifying LMTs and road markings, performing LDW and
detecting lane change events. The proposed vision-based system works on a
temporal sequence of images. Lane marking features are extracted in perspective
and Inverse Perspective Mapping (IPM) images that are combined to increase
robustness. The final estimated lane is modeled as a spline using a combination
of methods (Hough lines with Kalman filter and spline with particle filter).
Based on the estimated lane, all other events are detected. To validate ELAS
and cover the lack of lane datasets in the literature, a new dataset with more
than 20 different scenes (in more than 15,000 frames) and considering a variety
of scenarios (urban road, highways, traffic, shadows, etc.) was created. The
dataset was manually annotated and made publicly available to enable evaluation
of several events that are of interest for the research community (i.e., lane
estimation, change, and centering; road markings; intersections; LMTs;
crosswalks and adjacent lanes). ELAS achieved high detection rates in all
real-world events and proved to be ready for real-time applications.

Computer Vision and Pattern Recognition