Ing Ind - Inf (Mag.)(ord. 270) - BV (499) MOBILITY ENGINEERING
055657 - AUTONOMOUS VEHICLES
The course aims at introducing, with a systemic approach, students to the problems of autonomous and connected driving in order to present the technological challenges that this revolution is posing. In particular, following an overview of the evolution of ADAS, the course will be divided into three parts: the sensors necessary for autonomous driving, the control approaches with the open challenges, the interaction of the vehicle with the surrounding environment. Besides the lessons, students will have the opportunity to practically verify the main aspects of the course. There will be interventions from both academic and industrial experts in the sector and, where possible, guided tours.
Risultati di apprendimento attesi
Knowledge and understanding
At the end of the course, the student knows:
- the core features of an autonomous and connected vehicle - which sensors are required and how to process data from these sensors - which algorithms are used to drive an autonomous and connected vehicle - how the autonomous and connected vehicle interacts with the environment and in particular with other vehicles (V2V) and with the infrastructure (V2I)
Ability to apply knowledge and understanding
At the end of the course, the student is able to:
- post-process sensor data through state of the art algorithms (such as CNN) to extract relevant information about the environment surrounding the autonomous and connected vehicle - implement state of the art algorithms for driving autonomous and connected vehicles in a complex environment - communicate relevant information with other vehicles/the infrastructure for implementing highly complex ADAS
The course describes the potentialities of autonomous and connected vehicles and provides the student with all the necessary tools to end up with a working autonomous and connected vehicle. In particular, the sensory part, the control part and the interaction part will be assessed giving students ready to use algorithms and asking them to customize/improve these algorithms for specific purposes.
In detail, the topics dealt with within the course are:
Introduction to autonomous and connected vehicles
Robotic sensors & introduction to computer vision Camera models & camera calibration Stereo vision Image processing, feature detection & description Information extraction & classic visual recognition Modern computer vision techniques
Trajectory optimization Trajectory tracking & closed loop control Motion planning State machines Decision making under uncertainty Reinforcement learning
Introduction to localization & filtering theory Parametric filtering (KF, EKF, UKF) Non-parametric filtering (PF) Monte Carlo localization and particle filter SLAM Multi-sensor perception & sensor fusion
R. Siegwart, I. R. Nourbakhsh, D. Scaramuzza. Introduction to Autonomous Mobile Robots S. LaValle. Planning Algorithms D. A. Forsyth, J. Ponce. Computer Vision: A Modern Approach S. Thrun, W. Burgard, D. Fox. Probabilistic robotics F. Gustafsson. Statistical Sensor Fusion D. Simon. Optimal State Estimation: Kalman, Hinf, and Nonlinear Approaches D. Bertsekas, Reinforcement Learning and Optimal Control
A thorough knowledge of the modelling of mechanical systems as well as of classical control theory is required.
Modalità di valutazione
The exam is based on an oral discussion about the topics dealt with within the course with the aim of verifying both the knowledge achievements and the related application abilities.
Tipo Forma Didattica
Ore di attività svolte in aula
Ore di studio autonome
Laboratorio Di Progetto
Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese