To implement autonomous driving, one essential step is to model the vehicle environment based on the sensor inputs. Radars, with their well-known advantages, became a popular option to infer the occupancy state of grid cells surrounding the vehicle. To tackle data sparsity and noise of radar detections, we propose a deep learning-based Inverse Sensor Model (ISM) to learn the mapping from sparse radar detections to polar measurement grids. Improved lidar-based measurement grids are used as reference. The learned radar measurement grids, combined with radar Doppler velocity measurements, are further used to generate a Dynamic Grid Map (DGM). Experiments in real-world highway scenarios show that our approach outperforms the hand-crafted geometric ISMs. In comparison to state-of-the-art deep learning methods, our approach is the first one to learn a single-frame measurement grid in the polar scheme from radars with a limited Field Of View (FOV). The learning framework makes the learned ISM independent of the radar mounting. This enables us to flexibly use one or more radar sensors without network retraining and without requirements on 360{\deg} sensor coverage.

翻译：为了实现自动驾驶，一个关键步骤是基于传感器输入对车辆环境进行建模。雷达凭借其显著优势，成为推断车辆周围网格单元占用状态的常用选择。针对雷达检测数据稀疏且存在噪声的问题，我们提出一种基于深度学习的逆传感器模型（ISM），用于学习从稀疏雷达检测到极坐标测量网格的映射。改进的基于激光雷达的测量网格被用作参考。通过将学习到的雷达测量网格与雷达多普勒速度测量值相结合，进一步生成动态网格地图（DGM）。在真实高速公路场景中的实验表明，我们的方法优于手工设计的几何ISM。与最新的深度学习方法相比，我们的方法是首个在有限视场（FOV）条件下从雷达学习极坐标方案中单帧测量网格的工作。该学习框架使得所学的ISM不受雷达安装位置影响。这使我们能够灵活地使用一个或多个雷达传感器，无需重新训练网络，也无需360°传感器覆盖要求。