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Found 7 papers, 2 papers with code
Joint object detection and re-identification for 3D obstacle multi-camera systems
In recent years, the field of autonomous driving has witnessed remarkable advancements, driven by the integration of a multitude of sensors, including cameras and LiDAR systems, in different prototypes.
Automated Defect Recognition of Castings defects using Neural Networks
Industrial X-ray analysis is common in aerospace, automotive or nuclear industries where structural integrity of some parts needs to be guaranteed.
Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups
Most sensor setups for onboard autonomous perception are composed of LiDARs and vision systems, as they provide complementary information that improves the reliability of the different algorithms necessary to obtain a robust scene understanding.
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection
In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models.
Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection
The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness.
siaNMS: Non-Maximum Suppression with Siamese Networks for Multi-Camera 3D Object Detection
The rapid development of embedded hardware in autonomous vehicles broadens their computational capabilities, thus bringing the possibility to mount more complete sensor setups able to handle driving scenarios of higher complexity.
BirdNet: a 3D Object Detection Framework from LiDAR information
Understanding driving situations regardless the conditions of the traffic scene is a cornerstone on the path towards autonomous vehicles; however, despite common sensor setups already include complementary devices such as LiDAR or radar, most of the research on perception systems has traditionally focused on computer vision.
