Teoresi è una società internazionale di servizi di ingegneria, nata a Torino nel 1987.
Siamo specializzati nel supportare le aziende nella realizzazione di progetti che utilizzano tecnologie all'avanguardia, dalla guida autonoma alle nanotecnologie applicate all’ambito medicale. Il nostro approccio innovativo prevede una stretta collaborazione con i reparti di Ricerca e Sviluppo dei principali marchi industriali. Realizziamo soluzioni chiavi in mano accelerando il time-to-market del cliente. Teoresi è una delle 10 aziende selezionate da Amazon per collaborare allo sviluppo di nuovi prodotti basati sull’interazione vocale di Alexa.
Siamo sempre alla ricerca di persone di talento da inserire nel nostro team. In Teoresi diamo valore agli aspetti innovativi di ogni sfida progettuale , al lavoro di squadra, alla diversità e e ci piace pensare liberi da confini, non solo geografici.
Siamo costantemente aggiornati sui progressi tecnologici, dando priorità alle persone e alla sostenibilità ambientale. Il nostro team multidisciplinare e la nostra presenza globale ci permettono di offrire opportunità di carriera internazionali e di soddisfare le esigenze di un mercato in costante evoluzione. Crediamo che la proattività e la curiosità per l'apprendimento continuo siano essenziali in un contesto di squadra e ci impegniamo a generare innovazione in tutto ciò che facciamo.
Se condividi i nostri valori e ti interessa far parte di un'azienda orientata al futuro, continua a leggere e candidati!
Description
Teoresi is looking for talented young people interested to develop a thesis project in the company.
In this case we introduces a master thesis proposal that focus on Simulation Engine and Hardware in The Loop techniques.
Company Teoresi Group -> Teoresi S.p.A. | Italy
Job requirements
Topic Characteristics:
ADAS and autonomous driving cover many different technologies: vision and artificial intelligent algorithm, embedded high efficient C++ code, planning algorithms, new control algorithms, simulation and unity assets design, Navigation, Human-Machine-Interface, Connectivity.
In the context of Autonomous driving, building a real car with different type of sensors is very expensive. Simulation and Hardware In the Loop (HIL) are two key points to reach the goal of autonomous vehicle. There are a lot of reasons to focus activity and resources on these topics: price, possibility to change sensors in real time and to test different type of vehicle setup, possibility to recreate incidents or rare events, massive testing, take more synthetic data for neural network etc..
Nowadays, most of the current simulator engine haven’t a real photorealistic graphic but, at the end of the last year some community release open source code to use realistic game engine (Unity 3D and Unreal Engine) as simulator for autonomous driving. There is a lot of work to do around these first release and today the opportunity is to expand the functionality of these framework to use it in a product development scenario.
This thesis proposal focus on implement an interface between a real 3D game engine simulator and the autonomous driving framework in order to be able to use real traffic data and 3D data from HD maps in simulation. Simulator use high quality C++ and the thesis allow to deeply understand mechanism inside the autonomous driving platform in order to integrate data from simulated scenario into the platform.
Methodology:
The goal of this work is defined by these step:
- Simulation Engine Study and identification:
o The first phase is focus in the identification of the better Simulation Engine in order to integrate HD Map data from open source resource and adapt the data to a real traffic engine simulator. Also an analysis of HD map data format is required to perform this task.
- Implementation:
o Development of C++ code for integrate HD data into the simulator Engine and build a 3D scenario from the data extracted.
- HD Map analysis:
o HD Map data and 3D engine must integrate tagging data for autonomous driving. In order to exploit the HD map data is essential have a corresponding match between all the objects inside the scenario and the data processed by the autonomous driving system.
- Test on a real Target
o The last step is the test of the HD map data generated in a real scenario with a real autonomous vehicle
Toolchain:
Tensorflow/Caffe
Unity 3D Engine
C++
CUDA
OpenStreetMap
Nvidia Platform
Preferential position requirements
At the end of the project, and after the graduation, there could be the opportunity to be included in the company, through a paid internship.
Interested parties are invited to respond to the following add by uploading their CV, or to send an email to job@teoresigroup.com, referring to the add (TeoTESI_HIL_201904) and attaching the detailed CV with the course of study and exams taken.
The opportunity offered is to work in a young and dynamic environment, able to recognize and reward the best professionals.
Under current legislation, the job offer is intended to be extended to both sexes (Law 903/77).
Education
Laurea tradizionale o specialistica
Career level
Laureando / neolaureato
