自主导航地图的昨天、今天和明天EI北大核心CSCD

本文以无人驾驶技术的演化背景为出发点,总结自主导航地图,即高精地图的概念、与普通导航地图的区别、内容组成及数据来源。在此基础上,论述了高精制图流程的主要模块,包括先验图层的数据采集和更新、动态图层的实时构建、边缘计算、数据标准化和压缩服务。本文重点介绍了高精制图技术面临的4个研究难题及发展现状:①复杂驾驶环境的自动建模;②边角案例的数据采集;③伦理困境中的导航决策;④无人驾驶技术的安全性能评估。这些难题从不同的侧面挑战着高精地图自主导航能力的极限,需要政府立法机构、相关企业、研究机构和公众在全球范围内协同攻关。并指出,随着无人驾驶技术的不断完善,安全第一的研究重点将逐渐扩展成兼顾安全可信、环保节能和身心愉悦的移动体验,高精地图的结构化数码将衍生出多样化的适合人眼阅读的高清地图。如何充分提高导航数据的成本效益,实时生成高精地图和高清地图,同时为机器和人类提供最优服务,将成为新的研究热点。

Yesterday,today and tomorrow of autonomous navigation maps

Taking the evolutionary background of driverless technology as the starting point, this paper reviews the concept of autonomous navigation maps, i.e., high definition maps or HD maps, along with their content components, data sources and differences from ordinary navigation maps. On this basis the author introduces the main modules of the HD mapping process, including data acquisition and update of the a priori map layer, real-time construction of the dynamic map layer, edge computing, data standardization and compression services. Four main research bottlenecks are highlighted: ① automatic modeling of complex driving environments; ② data acquisition for corner cases; ③ navigation decisions in ethical dilemmas; ④ safety performance evaluation of driverless technologies. These bottlenecks are challenging the limits of autonomous navigation capability of HD maps from different perspectives, and demand a global collaboration among government legislatures, related enterprises, research institutions, and the public. With the continuous improvement of driverless technology, the focus of safety-first research will gradually expand into a mobile experience that balances safety and trustworthiness, environmental protection and energy saving, and physical and mental pleasure. The structured digital code of HD maps will be customized to a diverse range of high legibility maps for the human eye. How to leverage the cost-effectiveness of navigation data, generate high precision maps and high legibility maps in real time, and provide optimal services for both machines and humans will become a new research hotspot.