Advances in centerline estimation for autonomous lateral control
This addresses the need for safe lateral control in autonomous driving by providing a usable world-coordinate representation, though it is incremental as it builds on existing line detection methods.
The authors tackled the problem of autonomous vehicles needing accurate lane-keeping by developing a perception pipeline that estimates road lines, centerline, vehicle heading, and lateral displacement from monocular vision, and they created a publicly available dataset with geometric ground truth for benchmarking.
The ability of autonomous vehicles to maintain an accurate trajectory within their road lane is crucial for safe operation. This requires detecting the road lines and estimating the car relative pose within its lane. Lateral lines are usually retrieved from camera images. Still, most of the works on line detection are limited to image mask retrieval and do not provide a usable representation in world coordinates. What we propose in this paper is a complete perception pipeline based on monocular vision and able to retrieve all the information required by a vehicle lateral control system: road lines equation, centerline, vehicle heading and lateral displacement. We evaluate our system by acquiring data with accurate geometric ground truth. To act as a benchmark for further research, we make this new dataset publicly available at http://airlab.deib.polimi.it/datasets/.