自動運転車が局所生成した複数のベクトル化タイルからのニューラルHDマップ生成
Neural HD Map Generation from Multiple Vectorized Tiles Locally Produced by Autonomous Vehicles
自動運転車が複数回の走行で局所的に生成したベクトル化タイルを入力とし、エンドツーエンドの生成ニューラルネットワークGNMapで高品質なグローバルHDマップを自動構築する手法を提案。
著者: Miao Fan, Yi Yao, Jianping Zhang, Xiangbo Song, Daihui Wu
分類: cs.RO
原文アブストラクト
High-definition (HD) map is a fundamental component of autonomous driving systems, as it can provide precise environmental information about driving scenes. Recent work on vectorized map generation could produce merely 65% local map elements around the ego-vehicle at runtime by one tour with onboard sensors, leaving a puzzle of how to construct a global HD map projected in the world coordinate system under high-quality standards. To address the issue, we present GNMap as an end-to-end generative neural network to automatically construct HD maps with multiple vectorized tiles which are locally produced by autonomous vehicles through several tours. It leverages a multi-layer and attention-based autoencoder as the shared network, of which parameters are learned from two different tasks (i.e., pretraining and finetuning, respectively) to ensure both the completeness of generated maps and the correctness of element categories. Abundant qualitative evaluations are conducted on a real-world dataset and experimental results show that GNMap can surpass the SOTA method by more than 5% F1 score, reaching the level of industrial usage with a small amount of manual modification. We have already deployed it at Navinfo Co., Ltd., serving as an indispensable software to automatically build HD maps for autonomous driving systems.
