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：为实现自动驾驶，基于传感器输入对车辆环境建模是必要步骤。雷达凭借其显著优势，成为推断车辆周围栅格单元占据状态的热门选择。为解决雷达探测数据稀疏和噪声问题，我们提出一种基于深度学习的逆传感器模型（ISM），以学习从稀疏雷达探测到极坐标测量栅格的映射。改进的激光雷达测量栅格被用作参考。所学习的雷达测量栅格结合雷达多普勒速度测量，进一步用于生成动态栅格地图（DGM）。在实际高速公路场景下的实验表明，我们的方法优于手工设计的几何ISM。与最先进的深度学习方法相比，我们的方法是首个从有限视场（FOV）雷达中学习极坐标方案下单帧测量栅格的方法。该学习框架使得所学习的ISM独立于雷达安装位置，从而能够灵活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。