Through constant improvements in recent years radar sensors have become a viable alternative to lidar as the main distancing sensor of an autonomous vehicle. Although robust and with the possibility to directly measure the radial velocity, it brings it's own set of challenges, for which existing algorithms need to be adapted. One core algorithm of a perception system is dynamic occupancy grid mapping, which has traditionally relied on lidar. In this paper we present a dual-weight particle filter as an extension for a Bayesian occupancy grid mapping framework to allow to operate it with radar as its main sensors. It uses two separate particle weights that are computed differently to compensate that a radial velocity measurement in many situations is not able to capture the actual velocity of an object. We evaluate the method extensively with simulated data and show the advantages over existing single weight solutions.

翻译：近年来，通过持续改进，雷达传感器已成为自动驾驶车辆主距离传感器中激光雷达的可行替代方案。尽管雷达具有鲁棒性且可直接测量径向速度，但它也带来了特有的挑战，需要调整现有算法以应对。感知系统的核心算法之一是动态占用栅格地图构建，传统上该算法依赖激光雷达。本文提出了一种双权重粒子滤波器，作为贝叶斯占用栅格地图构建框架的扩展，使其能够以雷达作为主传感器运行。该滤波器使用两种分别独立计算的粒子权重，以补偿在许多情况下径向速度测量无法捕获物体实际速度的问题。我们使用模拟数据对该方法进行了广泛评估，并展示了其相对于现有单权重解决方案的优势。