IAMCV Interaction of Autonomous and Manually Controlled Vehicles

Citation Author(s):
Novel
Certad
Department Intelligent Transport Systems, Johannes Kepler University Linz, 4040 Linz, Austria
Enrico
Del Re
Department Intelligent Transport Systems, Johannes Kepler University Linz, 4040 Linz, Austria
Amirhesam
Aghanouri
Department Intelligent Transport Systems, Johannes Kepler University Linz, 4040 Linz, Austria
Delgermaa
Gankhuyag
Department Intelligent Transport Systems, Johannes Kepler University Linz, 4040 Linz, Austria
Walter
Morales-Alvarez
Department Intelligent Transport Systems, Johannes Kepler University Linz, 4040 Linz, Austria
Sebastian
Tschernuth
Department Intelligent Transport Systems, Johannes Kepler University Linz, 4040 Linz, Austria
Luigi
Del Re
Institute for Design and Control of Mechatronical Systems, Johannes Kepler University Linz, 4040 Linz, Austria
Cristina
Olaverri-Monreal
Department Intelligent Transport Systems, Johannes Kepler University Linz, 4040 Linz, Austria
Submitted by:
Novel Certad
Last updated:
Thu, 03/14/2024 - 06:00
DOI:
10.21227/d1g3-c160
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Abstract 

(Under Construction) The IAMCV Dataset was acquired as part of the FWF Austrian Science Fund-funded "Interaction of Autonomous and Manually-Controlled Vehicles" project. It is primarily centered on inter-vehicle interactions and captures a wide range of road scenes in different locations across Germany, including roundabouts, intersections, and highways. These locations were carefully selected to encompass various traffic scenarios, representative of both urban and rural environments. By simultaneously capturing data from multiple sensor modalities, our dataset provides an in-depth understanding of the road network from a driver-centric perspective, enabling researchers and developers to analyze, model, and evaluate autonomous driving algorithms and systems under diverse conditions. De data is available as: .csv, .pcd, and .jpeg files.

Instructions: 

The IAMCV dataset comprises different types of data:

  • Point clouds: The point clouds from the three LIDARs were stored using PCD file format. All the original fields that came from the LIDARs were also stored in the file:
    • x,y,z: coordinates of the target in the sensor frame of reference.
    • range: distance from the sensor to the target in mm.
    • ambience: or near IR, is the ambience level of infrared sensed by the receiver when the transmitter is not pointing to that area.
    • intensity: level of the signal in the receiver.
    • reflectivity: reflectivity level of the target.
    •  ring: LIDAR's layer. 
  • Images: Similarly, the images from the three cameras were stored using PNG file format. The images are stored without rectification and the intrinsic parameters are provided. The images of the dataset were anonymized before publishing by blurring faces and license plates.

Regarding the naming, files from cameras and LIDARs were named using the format "nn_sensor_tttttttttt.tttt.ext" as follows:

  • nn: stands for the number of the recording. Each recording has a unique two-digit identifier.
  • sensor: indicates whether the sensor is a camera or a LIDAR and its position. e.g., "camera_c" stands for the camera located in the center of the vehicle, and "lidar_l" stands for the LIDAR located in the left side of the vehicle.
  • tttttttttt.tttt: it corresponds to the timestand of the frame.
  • ext: file extension .pcd or .png
Funding Agency: 
Austrian Science Fund (FWF)
Grant Number: 
P 34485-N

Dataset Files

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