Detection and Multi-Object Tracking (DAMOT) systems have a critical role to play in scene understanding in the context of autonomous driving. Modern Autonomous Driving Stacks (ADS) require a software processing unit or module that allows them to understand the data in the environment and convert it into vital information for further decision making. In this context, this work develops a DAMOT module based on Machine Learning techniques, such as DBSCAN or BEV-SORT, that receives information from LiDAR and RADAR sensors in CARLA Simulator. This module uses containerisation techniques with Docker and standard robotics communications with ROS. The performance of the method is evaluated in terms of detection in the AD PerDevKit dataset, developed by the authors.
Towards LiDAR and RADAR Fusion for Object Detection and Multi-object Tracking in CARLA Simulator
Lect. Notes in Networks, Syst.
Iberian Robotics conference ; 2022 ; Zaragoza, Spain November 23, 2022 - November 25, 2022
19.11.2022
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
RADAR LIDAR OBJECT DETECTION USING RADAR AND LIDAR FUSION
Europäisches Patentamt | 2023
|Exploring Domain Adaptation with Depth-Based 3D Object Detection in CARLA Simulator
Springer Verlag | 2024
|CARLA Simulated Data for Rare Road Object Detection
IEEE | 2021
|