Master Thesis Proposal:Planning and Control

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 Path planning algorithms and related Control algorithms.

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 urban scenario an accurate path planning is essential to generate the most accurate trajectory for the autonomous car. Currently, accurate path planning algorithms are required for a lot of task and the same planner is not able to adapt the trajectory to different vehicle or different tasks. For example, a specific path planning algorithm is required for valet parking or to move vehicle with three or two wheels.

Also the control must be adapt in order to move different type of vehicle such as truck or wheelchair in autonomous mode.

This thesis proposal focus on develop a adaptable path planner for different use case scenario or different type of vehicle, adapt the control and test the results both in a simulation engine and a real autonomous target using an embedded platform.

Methodology:

The goal of this work is defined by these step:

- Path Planner analysis: o Analysis of the state of the art path planner algorithm and study of the implementation of path planner for navigation in highway and Urban in the current autonomous driving framework.

- Path Planning algorithm identification: o Identify the best Path planner for valet parking or for different type of vehicle in order to implement custom and efficient C++ module for the autonomous driving system.

- Quality comparison with an expensive platform o The result of the work is compared with an expensive platform. The goal is not to have the same performance but to understand the limits of a low cost and low power platform.

- Path Planner optimization: o To optimize embedded porting of the algorithm is important write specific CUDA kernels in order to speed up the execution exploiting the GPU inside the board.

- CNN Simulation on a real Autonomous driving framework o The test bench consist of a series of test use case on a real urban scenario inside a Unity base Simulator.

- Test on a real Target o The last step is the test of the algorithm in a real scenario with a real autonomous vehicle

Toolchain: C++ Unity 3D engine Matlab Simulink CUDA

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_P&C_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