The acquisition and analysis of high-quality sensor data constitute an essential requirement in shaping the development of fully autonomous driving systems. This process is indispensable for enhancing road safety and ensuring the effectiveness of the technological advancements in the automotive industry. This study introduces the Interaction of Autonomous and Manually-Controlled Vehicles (IAMCV) dataset, a novel and extensive dataset focused on inter-vehicle interactions. The dataset, enriched with a sophisticated array of sensors such as Light Detection and Ranging, cameras, Inertial Measurement Unit/Global Positioning System, and vehicle bus data acquisition, provides a comprehensive representation of real-world driving scenarios that include roundabouts, intersections, country roads, and highways, recorded across diverse locations in Germany. Furthermore, the study shows the versatility of the IAMCV dataset through several proof-of-concept use cases. Firstly, an unsupervised trajectory clustering algorithm illustrates the dataset's capability in categorizing vehicle movements without the need for labeled training data. Secondly, we compare an online camera calibration method with the Robot Operating System-based standard, using images captured in the dataset. Finally, a preliminary test employing the YOLOv8 object-detection model is conducted, augmented by reflections on the transferability of object detection across various LIDAR resolutions. These use cases underscore the practical utility of the collected dataset, emphasizing its potential to advance research and innovation in the area of intelligent vehicles.

翻译：高质量传感器数据的采集与分析是推动全自动驾驶系统发展的关键需求，这对于提升道路安全及确保汽车工业技术革新的有效性不可或缺。本文介绍了自主与人工控制车辆交互（IAMCV）数据集——一个聚焦车辆间交互的新型大规模数据集。该数据集融合了激光雷达、摄像头、惯性测量单元/全球定位系统及车辆总线数据采集等先进传感器阵列，全面呈现了涵盖环岛、交叉路口、乡村道路与高速公路的真实驾驶场景，数据采集于德国多个地点。此外，研究通过若干概念验证用例展示了IAMCV数据集的多样性：首先，无监督轨迹聚类算法验证了数据集在无需标注训练数据的情况下对车辆运动进行分类的能力；其次，利用数据集中捕获的图像，将在线相机标定方法与基于机器人操作系统（ROS）的标准方法进行了对比；最后，采用YOLOv8目标检测模型进行初步测试，并探讨了目标检测在不同激光雷达分辨率下的可迁移性。这些用例突显了所采集数据集的实用价值，展现了其在推动智能车辆领域研究与创新方面的潜力。