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Source: - Claudia Blaiotta, Bosch Center for Artificial Intelligence, Robert Bosch GmbH, claudia.blaiotta '@' de.bosch.com
Data Set Information: The raw data was acquired from a vehicle equipped with multiple sensors while driving, for approximately five hours, in an urban area in southern Germany. The sensor set included one mono-RGB camera, one stereo-RGB camera, an inertial measurement system with differential GPS and a lidar system. The preprocessed data available from this repository consists of 45 pedestrian tracks (in world coordinates) together with a semantic map of the static environment. For each track and at each time-step, not only the agent position is provided, but also body and head orientation attributes, as well as the position of all other agents and their type (e.g. car, cyclist, pedestrian etc.). Additional details about the preprocessing pipeline can be found in [1]. More information on the data format is provided in the next section. Attribute Information: : Pedestrian tracks are stored in the tracks.csv. Each row in such files contains 14 comma-separated attributes, with missing values denoted by ‘None’. The attributes are in order:
Relevant Papers: [1] Blaiotta, Claudia. 'Learning generative socially-aware models of pedestrian motion.' IEEE Robotics and Automation Letters, 2019. Citation Request: You may use this data for scientific, non-commercial purposes, as long as you give credit to the owners when publishing any work based on this data. Please cite [1]. |
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