State-of-the-art autonomous driving systems rely on high definition (HD) maps for localization and navigation. However, building and maintaining HD maps is time-consuming and expensive. Furthermore, the HD maps assume structured environment such as the existence of major road and lanes, which are not present in rural areas. In this work, we propose an end-to-end transformer networks based approach for map-less autonomous driving. The proposed model takes raw LiDAR data and noisy topometric map as input and produces precise local trajectory for navigation. We demonstrate the effectiveness of our method in real-world driving data, including both urban and rural areas. The experimental results show that the proposed method outperforms state-of-the-art multimodal methods and is robust to the perturbations of the topometric map. The code of the proposed method is publicly available at \url{https://github.com/Jiaolong/trajectory-prediction}.
翻译:最新自主驾驶系统依靠高定义(HD)地图进行定位和导航。然而,建造和维护HD地图既费时又费钱。此外,HD地图假定有结构环境,例如农村地区不存在的主要道路和车道;在这项工作中,我们提议为无地图自主驾驶采用以端到端变压器网络为基础的方法。拟议的模型将原始LIDAR数据和吵闹的远方地图作为输入,并制作精确的当地导航轨迹。我们展示了我们的方法在现实世界驱动数据(包括城市和农村地区)中的有效性。实验结果显示,拟议的方法优于最先进的多式方法,而且对图形图的扰动非常有力。提议的方法代码可在以下网站公开查阅:https://github.com/Jiaolong/trajotory-protregy}。