Advances in centerline estimation for autonomous lateral control
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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 computed 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 able to retrieve, from a single image, all the information required by a vehicle lateral control system: road lines equation, centerline, vehicle heading and lateral displacement. We also evaluate our system by acquiring a new dataset with accurate geometric ground truth, and we make it publicly available to act as a benchmark for further research.
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