Summary of Man Truckscenes: a Multimodal Dataset For Autonomous Trucking in Diverse Conditions, by Felix Fent et al.
MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse conditions
by Felix Fent, Fabian Kuttenreich, Florian Ruch, Farija Rizwin, Stefan Juergens, Lorenz Lechermann, Christian Nissler, Andrea Perl, Ulrich Voll, Min Yan, Markus Lienkamp
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents MAN TruckScenes, a multimodal dataset for autonomous trucking. It comprises over 740 scenes of 20s each, featuring various environmental conditions. The dataset includes sensor data from 4 cameras, 6 lidar, 6 radar sensors, 2 IMUs, and a high-precision GNSS. The 3D bounding boxes were manually annotated and reviewed to achieve high quality standards. The scenes are tagged according to distinct scene tags, and all objects are tracked throughout the scene. This dataset promotes a wide range of applications in autonomous trucking, including trailer occlusions, novel sensor perspectives, and terminal environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous trucks could greatly impact logistics and the environment. To make them safe on public roads, we need to improve their ability to perceive the environment. Machine learning methods rely on large datasets, but none are available for autonomous trucks yet. This paper presents a new dataset called MAN TruckScenes that solves this problem. It has over 740 scenes with different environmental conditions and includes sensor data from cameras, lidar, radar, IMUs, and GNSS. The scenes were tagged and objects were tracked throughout the scene to help researchers work on autonomous trucking. |
Keywords
* Artificial intelligence * Machine learning * Precision
