Interaction of Autonomous and Manually Controlled Vehicles Multiscenario 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 intervehicle interactions. The dataset, enriched with a sophisticated array of sensors such as lidar, 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. First, an unsupervised trajectory clustering algorithm illustrates the dataset’s capability in categorizing vehicle movements without the need for labeled training data. Second, the authors 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.

• Record URL:
  • http://dx.doi.org/10.1109/MITS.2024.3378114
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/19391390
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Certad, Novel
  • Del Re, Enrico
  • Korndörfer, Helena
  • Schröder, Gregory
  • Morales-Alvarez, Walter
  • Tschernuth, Sebastian
  • Gankhuyag, Delgermaa
  • del Re, Luigi
  • Olaverri-Monreal, Cristina
• Publication Date: 2024-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 6-20
• Serial:
  • IEEE Intelligent Transportation Systems Magazine
  • Volume: 16
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1939-1390
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5117645

Subject/Index Terms

• TRT Terms: Advanced driver information systems; Autonomous vehicles; Datasets; Laser radar; Vehicle mix; Vehicle to vehicle communications
• Identifier Terms: YOLO
• Geographic Terms: Germany
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01925117
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 23 2024 5:43PM