IAMCV Multi-Scenario Vehicle Interaction Dataset
<|reference_start|>IAMCV Multi-Scenario Vehicle Interaction Dataset: 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.<|reference_end|>
@article{certad2024iamcv,
    title={IAMCV Multi-Scenario Vehicle Interaction Dataset},
    author={Novel Certad, Enrico del Re, Helena Kornd"orfer, Gregory Schr"oder,
  Walter Morales-Alvarez, Sebastian Tschernuth, Delgermaa Gankhuyag, Luigi del
  Re and Cristina Olaverri-Monreal},
    journal={arXiv preprint arXiv:2403.08455},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08455},
    primaryClass={cs.RO cs.ET}
}