Cooperative crowd mapping for interconnected autonomous vehicles

这个项目的目标是开发算法，从许多车辆在较长时间段内收集的数据中推导出交通区域的一致模型和交通参与者的典型运动模式。得到的信息将被提供给其他合作车辆，以便他们更好地了解道路拓扑和潜在的危险区域。此外，算法将能够检测系统随时间的变化并增量更新他们的知识。本项目将使用两种类型的传感器输入，(A)协作车辆的轨迹(根据GNSS测量和立体摄像机图像序列计算)和(B)协作车辆使用立体摄像机观察到的其他交通参与者(行人、骑自行车的人、电车、汽车)的轨迹。该项目不使用昂贵的传感器(例如高精度的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、十字路口、停车线、斑马线和行人可能横穿的地点的拓扑结构和可能的操作。除了这些静态的方面，这些方法将能够检测到与先前知识的偏差，如车道堵塞或行人密度增加。描述性和预测性模型将用于描述交通区域和运动模式。作为描述性模型，我们将使用语义图，其中交通空间使用形式语法来表示。例如，语义地图将包含交叉口的布局和典型运动模式。预测模型将用于预测交通参与者的移动。例如，它们将允许预测被观察到的行人的未来轨迹。与语义地图的情况一样，预测模型中的每个元素都将被赋予它在交通区域中的位置，即每个十字路口将有自己的行人行为模型。为了对我们的方法进行可理解的评估，我们将创建用于协作人群映射的基准数据集。这些数据将在网上提供给其他研究小组。

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.