This paper presents an automated driving system (ADS) data acquisition and processing platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) cooperative perception. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which can process the sensor data from multiple CAVs and extract the objects' Identity (ID) number, position, speed, and orientation information in the map and Frenet coordinates. First, the ADS data acquisition and analytics platform are presented. Specifically, the experimental CAVs platform and sensor configuration are shown, and the processing software, including a deep-learning-based object detection algorithm using LiDAR information, a late fusion scheme to leverage cooperative perception to fuse the detected objects from multiple CAVs, and a multi-object tracking method is introduced. To further enhance the object detection and tracking results, high definition maps consisting of point cloud and vector maps are generated and forwarded to a world model to filter out the objects off the road and extract the objects' coordinates in Frenet coordinates and the lane information. In addition, a post-processing method is proposed to refine trajectories from the object tracking algorithms. Aiming to tackle the ID switch issue of the object tracking algorithm, a fuzzy-logic-based approach is proposed to detect the discontinuous trajectories of the same object. Finally, results, including object detection and tracking and a late fusion scheme, are presented, and the post-processing algorithm's improvements in noise level and outlier removal are discussed, confirming the functionality and effectiveness of the proposed holistic data collection and processing platform.
翻译:本文展示了一个自动化驱动系统数据采集和处理平台,用于车辆轨迹提取、重建和评价,其基础是连接的自动化车辆(CAV)合作认知;该平台展示了从原始先进感官数据收集到数据处理的全面管道,从原始高级感官数据收集到数据处理,从多个CAV处理传感器的传感器数据,提取地图和Frenet坐标中的物体身份(ID)号码、位置、速度和定向信息;首先,展示了ADS数据采集和分析平台,具体地展示了CAV的实验性逻辑平台和传感器配置,并展示了处理软件,包括使用LIDAR信息的基于深学习的物体检测算法,一种利用合作感知将多个CAVA的检测对象连接起来,并采用了多点跟踪方法;为了进一步增强物体检测和跟踪结果,制作了由点云和矢量地图组成的高定义地图,并转发给一个世界模型,以过滤道路上的物体,并提取Frenet的坐标和航道信息。此外,一项后处理目标的基于深层次的测算方法,包括改进最后轨迹跟踪、AIART的跟踪方法,以及从Aly解算算方法到最终测算方法的计算。