Ego-Lane Analysis System (ELAS): Dataset and algorithms

Abstract

Decreasing costs of vision sensors and advances in embedded hardware boosted lane related research detection, estimation, tracking, etc. 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). Moreover, the system was also validated quantitatively and qualitatively on other public datasets. ELAS achieved high detection rates in all real-world events and proved to be ready for real-time applications.

Implications of a Narrow Automated Vehicle-Exclusive Lane on Interstate 15 Express Lanes

“…e external environment is detected through forward-facing cameras and then steers to keep on track through lane detection and tracking algorithms. Berriel et al [13] proposed a vision-based, real-time ego-lane analysis system that is capable of estimating egolane position, classifying lane marking types and road striping, performing lane departure warnings, and detecting lane changing events.…”

Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios

“…The lane model was also improved to generate a B-snake model [42] or parallel-snake model [43]. Moreover, a spline-based particle filter is used to model the curvature of the lane [44].…”

Mapping Road Lanes Using Laser Remission and Deep Neural Networks

“…The system outputs a cubic spline for each of the right and left lane (in the car's track), besides, each lane class. In [3] Berriel et al propose a system that works on a temporal sequence of images applying an inverse perspective mapping and a particle filter for each image to track the lane along time. The system outputs a cubic spline for each of the right and left lane (in the car's track), besides, each lane class.…”

“…These methods, however, are only used in the presence of road marks, do not produce a road map and were not tested on a real autonomous car. In addition, the method presented in [3] is strongly dependent on parameters.…”