In this work, we propose a novel adaptive grid mapping approach, the Adaptive Patched Grid Map, which enables a situational aware grid based perception for autonomous vehicles. Its structure allows a flexible representation of the surrounding unstructured environment. By splitting types of information into separate layers less memory is allocated when data is unevenly or sporadically available. However, layers must be resampled during the fusion process to cope with dynamically changing cell sizes. Therefore, we propose a novel spatial cell fusion approach. Together with the proposed fusion framework, dynamically changing external requirements, such as cell resolution specifications and horizon targets, are considered. For our evaluation, real-world data were recorded from an autonomous vehicle driving through various traffic situations. Based on this, the memory efficiency is compared to other approaches, and fusion execution times are determined. The results confirm the adaptation to requirement changes and a significant memory usage reduction.

翻译：本文提出了一种新颖的自适应网格映射方法——自适应补丁网格图，能够为自动驾驶车辆提供具备情境感知能力的基于网格的感知。其结构允许灵活表示周围非结构化环境。通过将信息类型划分为独立层，可在数据分布不均或稀疏可用时减少内存分配。然而，在融合过程中需对层进行重采样以应对动态变化的单元尺寸。为此，我们提出了一种新颖的空间单元融合方法。结合所提出的融合框架，可考虑动态变化的外部需求，如单元分辨率规格与视距目标。在评估中，我们记录了自动驾驶车辆行驶于多种交通场景下的真实世界数据。基于此，该方法与其它方法进行了内存效率对比，并测定了融合执行时间。结果验证了该方法对需求变化的适应性以及显著的内存占用降低效果。