Files & Packages
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Frontal Object Perception (FOP) and Moving Object Classification (MOC)
The objective of the FOP module is to deliver descriptions about relevant obstacles (e.g.: location, speed) in the frontal area of the ego vehicle including stationary and moving objects which can be detected by a sensor array: lidar, radar, mono-camera. MOC module aims to provide estimated information about class of moving objects detected by the FOP module. An object will be categorized into different classes: pedestrian (or group of pedestrians), bike, car and truck. This information gives important values for the target applications when dealing with different classes of objects, for example: vulnerable road users like pedestrians and bikes.
FOP and MOC module are performed simultaneously and divided into three main sub-tasks:
– Lidar Object Detection, Tracking and Classification
– Image Object Classification
– Data Fusion at Different Levels
These modules were developed as part of the European project: interactIVe – Accident avoidance by intervention for Intelligent Vehicles.
Modules are written in C++ language and using the EB Assist ADTF framework
R. Omar Chavez-Garcia
In collaboration with TRW Automotive and Centro Ricerche Fiat (CRF) S.C.p.A.