OBJECTIVES:
• State of the art study: trailer assist technology offered by car manufacturers such as VOLKSWAGEN, Ford, Audi and Land Rover,
• Construction of a database (Images, Videos),
• Data preprocessing (Images, Videos),
• Development of an algorithm based on artificial intelligence to measure the angle between the trailer and the vehicle in real time from 2D images generated by the rear camera of the vehicle,
• Embedding the optimal algorithm in an embedded target.
REALIZATIONS:
• Acquire and annotate the database for training and validation,
• Code a basic CNN model in Python,
• Code and test the end to end solution with EfficientNet capable of estimating with an accuracy of 1 degree the value of the angle between the trailer and the vehicle from a 2D image,
• optimize the code for real time in python (
OBJECTIVES:
• Requirements analysis,
• Algorithmic exploration,
• Development, integration and validation of application software.
REALIZATIONS:
• Analysis of the requirements of the "trajectory-based ego-positioning" functionality inside the precise location component,
• Algorithmic exploration of the functionality of "trajectory based ego-positioning" for the centimeter localization of the ego including also a study of the state of the art,
• Comparative analysis of algorithmic performance based on simulation,
• Matlab development of the chosen algorithm.
TECHNICAL ENVIRONMENT:
• Matlab script, gitlabee, JIRA.
OBJECTIVES:
• Multi sensor data fusion for autonomous vehicle,
• Responsible for data synchronization,
• State of the art of existing methods and choice of methods used,
• Programming algorithms in C ++ under ROS,
• Testing and optimization of algorithms.
REALIZATIONS:
• Use of a centralized fusion architecture (preprocessing, data synchronization, spatial alignment, time alignment and data association),
• Camera data preprocessing using CNN Deep Learning architecture,
• Leddar data preprocessing using mathematical algorithms in C ++,
• Time synchronization between the clock of each sensor and the clock of the PC (the host),
• Spatial alignment,
• Time alignment of all data,
• Documentation of C ++ codes using Doxygen.
TECHNICAL ENVIRONMENT:
• ROS, C++, Python, Linux, Doxygen,
OBJECTIVES :
• Resizing of the electrical equipment,
• Automation of the system with the SIEMENS SIMATIC S7-1200 station,
• Design of a command-control HMI.
REALIZATIONS:
• Realization of the different electrical diagrams of the extrusion area,
• Sizing and choice of different electrical equipment,
• System automation using LADDER language,
• Design of an HMI to view the status of each sensor and actuator as well as the weekly consumption,
• Evolution of the Running Time from 87.79% up to 90% for the extrusion zone.
TECHNICAL ENVIRONMENT:
• TiaPortal, X-RELAIS
REALIZATIONS :
• Manual test of one of the components of the "AJB" Automation Junction Box, using the black box method,
• Proposal of a diagram of an electronic card to automate the AJB test,
• Realization of a Prototype card,
• Test and Validation of the electronic card.
TECHNICAL ENVIRONMENT:
• PSIM, ISIS
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