Real-time dynamic object detection using multi-sensor fusion for autonomous driving
No Thumbnail Available
Journal Title
Journal ISSN
Volume Title
Sähkötekniikan korkeakoulu |
Master's thesis
Author
Date
2021-01-25
Department
Major/Subject
Autonomous Systems
Mcode
ELEC3055
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
67 + 5
Series
Abstract
Autonomous driving requires reliable perception of the vehicle’s surroundings to safely operate in adversarial weather conditions and to avoid potential collision with dynamic obstacles. This work studies three dynamic object detectors and fusion of the objects detected by each detector for autonomous driving. A dynamic object detector was designed to fuse state-of-the-art camera based detection, radio detection and ranging (RADAR) fused with camera detection and segmented clusters from light detection and ranging (LiDAR) sensor point cloud data from the three detectors. Reliable perception can be achieved by sensing the environment with multiple sensors to create a fused model of the environment including detected dynamic objects. State-of-the-art object detection precision for autonomous driving has advanced with recent breakthroughs in neural networks. A public dataset was used to evaluate the designed detector’s precision and computational performance. The designed detector was also compared with a detector that was used as a base for the implementation. The designed detector’s fusion performance proved to be reliable for autonomous driving with the identified requirements. The computational performance requirements were not met on the system used for testing.Description
Supervisor
Kyrki, VilleThesis advisor
Ahmed, BilalKeywords
camera, light detection and ranging, radio detection and ranging, object detection, multi-modal, sensor fusion
