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Maschinelles Lernen zur Geschwindigkeits- und Bewegungsrichtungsschätzung von Objekten basierend auf Scan-Artefakten von LiDAR-Sensoren

  • Light detection and ranging (LiDAR) sensors are increasingly applied to automated driving vehicles. Microelectromechanical systems are an established technology for making LiDAR sensors cost-effective and mechanically robust for automotive applications. These sensors scan their environment using a pulsed laser to record a point cloud. The scanning process leads in the point cloud to a distortion of objects with a relative velocity to the sensor. The consecutive generation and processing of points offers the opportunity to enrich the measured object data from the LiDAR sensors with velocity information by extracting information with the help of machine learning, without the need for object tracking. Turning it into a socalled 4D-LiDAR. This allows object detection, object tracking, and sensor data fusion based on LiDAR sensor data to be optimized. Moreover, this affects all overlying levels of autonomous driving functions or advanced driver assistance systems. However, since such sensor-specific effects are rarely available in publicLight detection and ranging (LiDAR) sensors are increasingly applied to automated driving vehicles. Microelectromechanical systems are an established technology for making LiDAR sensors cost-effective and mechanically robust for automotive applications. These sensors scan their environment using a pulsed laser to record a point cloud. The scanning process leads in the point cloud to a distortion of objects with a relative velocity to the sensor. The consecutive generation and processing of points offers the opportunity to enrich the measured object data from the LiDAR sensors with velocity information by extracting information with the help of machine learning, without the need for object tracking. Turning it into a socalled 4D-LiDAR. This allows object detection, object tracking, and sensor data fusion based on LiDAR sensor data to be optimized. Moreover, this affects all overlying levels of autonomous driving functions or advanced driver assistance systems. However, since such sensor-specific effects are rarely available in public datasets and the velocities of target objects are not included as ground truth in these datasets, it makes sense to enrich the limited real-world data with synthetic data. Therefore, this paper discusses how such datasets can be created and combined to efficiently predict velocities on realworld data using the authors' novel method dubbed VeloPoints.show moreshow less

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Metadaten
Author:Thomas ZehORCiDGND, Lukas HaasORCiDGND
Place of publication:Kempten
Document Type:conference proceeding (presentation)
Conference:Tagung Automatisiertes Fahren, 07. – 08. Dezember 2023, München, Deutschland
Language:German
Date of Publication (online):2024/07/19
Year of first Publication:2024
Publishing Institution:Hochschule für angewandte Wissenschaften Kempten
Number of pages:19 Folien
First Page:1
Last Page:19
Institutes:Fakultät Elektrotechnik
IFM - Institut für Fahrerassistenz und vernetzte Mobilität
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke
6 Technik, Medizin, angewandte Wissenschaften
Open Access:open_access
Research focus:FSP2: Mobilität
Publication Lists:Zeh, Thomas
Haas, Lukas
Publication reviewed:nicht begutachtet
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2024/07/24
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