  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.
