Cooperative crowd mapping for interconnected autonomous vehicles

互联自动驾驶车辆的协作人群测绘

• 批准号: 272999320
• 负责人: Professor Dr.-Ing. Claus Brenner
• 金额: --
• 依托单位: Institut für Kartographie und Geoinformatik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/272999320?language=en
• 关键词: Cooperative; crowd; mapping; interconnected; autonomous

The goal of this project is to develop algorithms which derive consistent models of the traffic area and typical movement patterns of traffic participants from data that were collected by many vehicles over an extended time period. The derived information will be provided to other cooperative vehicles so that they gain better knowledge of the road topology and potential hazard areas. Furthermore, the algorithms will be able to detect systematic changes over time and to update their knowledge incrementally.Two types of sensor input will be used in this project, (a) the trajectories of cooperative vehicles (calculated from GNSS measurements and stereo camera image sequences), and (b) trajectories of other traffic participants (pedestrians, bicyclists, tramways, cars) which are observed by the cooperative vehicles using stereo cameras. The project does not use expensive sensors (e.g. highly accurate multi-layer lidar sensors) since it is not likely that these will be part of cooperative vehicles in future.The following information will be extracted from the observed trajectories: the number and layout of lanes, the topology of, and possible maneuvers at intersections, stop lines, zebra crossings, and points where pedestrian are likely to cross. Beside these static aspects, the approaches will be able to detect deviations from previous knowledge like blocked lanes or an increased density of pedestrians.Both, descriptive and predictive models will be used to describe the traffic area and movement patterns. As descriptive models we will use semantic maps in which the traffic space is represented using a formal grammar. For example, the semantic maps will contain the layout and the typical movements patterns at intersections. The predictive models will be used to predict movements of traffic participants. For example, they will allow to predict the future trajectory of an observed pedestrian. Like in the case of the semantic map, each element in the predictive model will be attributed with the position in the traffic area to which it refers, i.e., each intersection will have its own pedestrian behavioral model.For a comprehensible evaluation of our approaches, we will create benchmark datasets for cooperative crowd mapping. These will be provided online to other research groups.

该项目的目标是开发算法，从许多车辆在较长时间内收集的数据中获得交通区域的一致模型和交通参与者的典型运动模式。所得到的信息将提供给其他合作车辆，以便它们更好地了解道路拓扑和潜在危险区域。此外，这些算法将能够检测系统随时间的变化，并逐步更新其知识。两种类型的传感器输入将用于本项目，（a）合作车辆的轨迹（从GNSS测量和立体相机图像序列计算），以及（B）其他交通参与者的轨迹（行人、骑自行车的人、有轨电车、汽车），其由协作车辆使用立体摄像机观察。该项目没有使用昂贵的传感器（例如高精度多层激光雷达传感器），因为这些传感器不太可能成为未来合作车辆的一部分。以下信息将从观察到的轨迹中提取：车道的数量和布局，交叉口的拓扑结构和可能的机动，停止线，斑马线和行人可能穿过的点。除了这些静态方面，这些方法将能够检测与先前知识的偏差，如堵塞的车道或行人密度增加。描述性和预测性模型都将用于描述交通区域和运动模式。作为描述性模型，我们将使用语义地图，其中的交通空间表示使用正式的语法。例如，语义地图将包含交叉口的布局和典型移动模式。预测模型将用于预测交通参与者的运动。例如，它们将允许预测观察到的行人的未来轨迹。类似于语义地图的情况，预测模型中的每个元素将被归因于其所指的交通区域中的位置，即，每个十字路口都有自己的行人行为模型，为了对我们的方法进行全面的评估，我们将创建用于协作人群映射的基准数据集。这些将在线提供给其他研究小组。