Semantic Bird's-Eye View Road Line Mapping

The development of Autonomous Vehicles (AVs) today requires precise and reliable detection of road line markings. Indeed, recognizing road line markings from camera images acquired by the vehicle plays a crucial role in ensuring its safe navigation and improving its driving performance. Road line detection is of key importance in real-time scenarios for navigation purposes, as well as offline for the generation of HD maps. In recent years, deep neural networks have proven effective in performing this task. In particular, Convolutional Neural Networks (CNNs) have helped develop multiple Advanced Driver Assistance Systems (ADAS), now fully integrated into common commercial vehicles. This paper presents a novel CNN-based pipeline for recognizing road line markings from front-view camera images in an online setup, and it shows how these detections can be aggregated offline into aerial-like maps as a first step toward the creation of HD maps. The proposed architecture comprises a multi-decoder to accurately classify image pixels representing different classes of road line markings, as well as those related to the drivable area. The mapping system then projects the extracted road line points into the Bird's-Eye View (BEV) space and integrates the extracted information with accurate localization measurements for georeferencing. Experimental evaluations on real-world data, including data acquired with instrumented vehicles, reveal the effectiveness of the proposed pipeline in both frame-by-frame detection and integrated mapping quality.