Lane Detection and Tracking Based on Lidar Data

Abstract

The contribution presents a novel approach to the detection and tracking of lanes based on lidar data. Therefore, we use the distance and reflectivity data coming from a one-dimensional sensor. After having detected the lane through a temporal fusion algorithm, we register the lidar data in a world-fixed coordinate system. To this end, we also incorporate the data coming from an inertial measurement unit and a differential global positioning system. After that stage, an original image of the road can be inferred. Based on this data view, we are able to track the lane either with a Kalman filter or by using a polynomial approximation for the underlying lane model.

Automatic lane marking extraction from point cloud into polygon map layer

“…shape, pattern) of the road marking. A similar example of the road marking detection using 2D images can be found in Thuy and Leon (2010).…”

Information fusion for automotive applications – An overview

“…Depending on the application, this may include the car's dynamic state, road geometry [20,21], the driver's biomedical condition, the degree of the driver's distraction [22], discrete driving modes (like ''accelerating,'' ''standstill,'' ''going backwards,'' etc.) [23], manoeuvering intentions [24], and many more.…”

Deep Multi-Sensor Lane Detection

“…c) LiDAR-based lane detection: Several techniques have been proposed using LiDAR measurements as the input [7], [8], [9], [10]. However, these techniques largely do not leverage the recent advances in deep learning.…”