Perception of other road users is a crucial task for intelligent vehicles. Perception systems can use on-board sensors only or be in cooperation with other vehicles or with roadside units. In any case, the performance of perception systems has to be evaluated against ground-truth data, which is a particularly tedious task and requires numerous manual operations. In this article, we propose a novel semi-automatic method for pseudo ground-truth estimation. The principle consists in carrying out experiments with several vehicles equipped with LiDAR sensors and with fixed perception systems located at the roadside in order to collaboratively build reference dynamic data. The method is based on grid mapping and in particular on the elaboration of a background map that holds relevant information that remains valid during a whole dataset sequence. Data from all agents is converted in time-stamped observations grids. A data fusion method that manages uncertainties combines the background map with observations to produce dynamic reference information at each instant. Several datasets have been acquired with three experimental vehicles and a roadside unit. An evaluation of this method is finally provided in comparison to a handmade ground truth.

翻译：对其他道路使用者的感知是智能车辆的关键任务。感知系统可仅使用车载传感器，或与其他车辆及路边单元协同工作。无论何种方式，感知系统的性能均需对照真值数据进行评估，这一过程尤为繁琐且需要大量人工操作。本文提出一种新颖的半自动伪真值估计方法。其原理在于：通过部署搭载激光雷达的试验车辆与固定于路侧的感知系统协同开展试验，共同构建动态参考数据。该方法基于栅格地图构建技术，重点在于生成包含数据集全序列有效信息的背景地图。所有代理数据被转换为带时间戳的观测栅格，并通过融合不确定性的数据方法将背景地图与观测结果结合，生成各时刻的动态参考信息。基于三辆试验车辆与一个路侧单元采集的多组数据集，本文最终将该方法与手工标定真值进行了对比评估。