Alessia SAGGESE | AUTONOMOUS VEHICLE DRIVING

cod. 0622700063

AUTONOMOUS VEHICLE DRIVING

	0622700063
	DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
	EQF7
	COMPUTER ENGINEERING
	2020/2021

CURRICULUM ARTIFICIAL INTELLIGENCE

	OBBLIGATORIO
	YEAR OF COURSE 2
	YEAR OF DIDACTIC SYSTEM 2017
	SECONDO SEMESTRE

TEACHING UNITS

SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
ING-INF/05	3	24	LESSONS	COMPULSORY SUBJECTS, CHARACTERISTIC OF THE CLASS
ING-INF/05	2	16	EXERCISES	COMPULSORY SUBJECTS, CHARACTERISTIC OF THE CLASS
ING-INF/05	1	8	LAB	COMPULSORY SUBJECTS, CHARACTERISTIC OF THE CLASS

PROFESSORS

	ALESSIA SAGGESE T Curriculum
	ANTONIO GRECO Curriculum

	Objectives
	The goal of the course is to introduce the main problems related to the realization of autonomus driving systems. Knowledge and understanding Technologies used for sensing in autonomous vehicles. The main tasks of an autonomous driving / driving support system: localization and mapping, scene understanding, motion planning, driver state analysis. Applied knowledge and understanding Ability to design and realize a simple autonomous driving system, including all the tasks, possibly with the help of a simulation software.

The goal of the course is to introduce the main problems related to the realization of autonomus driving systems.

Knowledge and understanding
Technologies used for sensing in autonomous vehicles. The main tasks of an autonomous driving / driving support system: localization and mapping, scene understanding, motion planning, driver state analysis.

Applied knowledge and understanding
Ability to design and realize a simple autonomous driving system, including all the tasks, possibly with the help of a simulation software.

	Prerequisites
	In order to achieve the goals of the course, the knowledge of the contents of the Machine Learning course and of the Python programming language is required.

	Contents
	INTRODUCTION (4-0-0) HISTORY OF AUTONOMOUS DRIVING SYSTEMS, DRIVING LEVELS, PERCEPTION, DECISION AND ACTION OVERVIEW SENSORS AND PERCEPTION (10-6-0) SENSORS OVERVIEW (CAMERA, STEREO, RADAR, LIDAR, SONAR, GNSS/IMU ETC). SENSOR POSITIONING ACCORDING TO THE APPLICATIVE SCENARIO (URBAN, HIGHWAY). GNSS/IMU, LIDAR. ACQUISITION AND MANAGEMENT OF LIDAR DATA. 2D OBJECT DETECTION. POINT CLOUD BASED 3D OBJECT DETECTION. LOCALIZATION AND MOTION PLANNING (10-4-0) LANE DETECTION, STATE ESTIMATION BY LIDAR, GNSS AND IMU; MAPPING FOR PLANNING, OCCUPANCY GRID; MISSION PLANNING, CREATING A ROAD NETWORK GRAPH; DYNAMIC OBJECT INTERACTIONS. SIMULATION ENVIRONMENT (8-4-2) USAGE OF CARLA SIMULATOR

Contents

INTRODUCTION (4-0-0)
HISTORY OF AUTONOMOUS DRIVING SYSTEMS, DRIVING LEVELS, PERCEPTION, DECISION AND ACTION OVERVIEW

SENSORS AND PERCEPTION (10-6-0)
SENSORS OVERVIEW (CAMERA, STEREO, RADAR, LIDAR, SONAR, GNSS/IMU ETC). SENSOR POSITIONING ACCORDING TO THE APPLICATIVE SCENARIO (URBAN, HIGHWAY). GNSS/IMU, LIDAR. ACQUISITION AND MANAGEMENT OF LIDAR DATA. 2D OBJECT DETECTION. POINT CLOUD BASED 3D OBJECT DETECTION.

LOCALIZATION AND MOTION PLANNING (10-4-0)
LANE DETECTION, STATE ESTIMATION BY LIDAR, GNSS AND IMU; MAPPING FOR PLANNING, OCCUPANCY GRID; MISSION PLANNING, CREATING A ROAD NETWORK GRAPH; DYNAMIC OBJECT INTERACTIONS.

SIMULATION ENVIRONMENT (8-4-2)
USAGE OF CARLA SIMULATOR

	Teaching Methods
	THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY EXERCITATIONS. DURING THE IN-CLASS EXERCITATIONS THE STUDENTS ARE DIVIDED IN TEAMS AND ARE ASSIGNED SOME PROJECT-WORKS TO BE DEVELOPED ALONG THE DURATION OF THE COURSE. THE PROJECTS INCLUDE ALL THE CONTENTS OF THE COURSE AND IS ESSENTIAL BOTH FOR THE ACQUISITION OF THE RELATIVE ABILITIES AND COMPETENCES, AND FOR DEVELOPING AND REINFORCING THE ABILITY TO WORK IN A TEAM. IN THE LABORATORY EXERCITATIONS THE STUDENTS IMPLEMENT THE ASSIGNED PROJECTS USING THE CARLA SOFTWARE LIBRARIES. IN ORDER TO PARTICIPATE TO THE FINAL ASSESSMENT AND TO GAIN THE CREDITS CORRESPONDING TO THE COURSE, THE STUDENT MUST HAVE ATTENDED AT LEAST 70% OF THE HOURS OF ASSISTED TEACHING ACTIVITIES.

Teaching Methods

THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY EXERCITATIONS. DURING THE IN-CLASS EXERCITATIONS THE STUDENTS ARE DIVIDED IN TEAMS AND ARE ASSIGNED SOME PROJECT-WORKS TO BE DEVELOPED ALONG THE DURATION OF THE COURSE. THE PROJECTS INCLUDE ALL THE CONTENTS OF THE COURSE AND IS ESSENTIAL BOTH FOR THE ACQUISITION OF THE RELATIVE ABILITIES AND COMPETENCES, AND FOR DEVELOPING AND REINFORCING THE ABILITY TO WORK IN A TEAM. IN THE LABORATORY EXERCITATIONS THE STUDENTS IMPLEMENT THE ASSIGNED PROJECTS USING THE CARLA SOFTWARE LIBRARIES.

IN ORDER TO PARTICIPATE TO THE FINAL ASSESSMENT AND TO GAIN THE CREDITS
CORRESPONDING TO THE COURSE, THE STUDENT MUST HAVE ATTENDED AT LEAST 70% OF THE HOURS OF ASSISTED TEACHING ACTIVITIES.

	Verification of learning
	THE EXAM AIMS AT EVALUATING, AS A WHOLE: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, THE ABILITY TO APPLY THAT KNOWLEDGE TO SOLVE PROGRAMMING PROBLEMS REQUIRING THE USE OF ARTIFICIAL VISION TECHNIQUES; INDEPENDENCE OF JUDGMENT, COMMUNICATION SKILLS AND THE ABILITY TO LEARN. THE EXAM INCLUDES TWO STEPS: THE FIRST ONE CONSISTS IN AN ORAL EXAMINATIONS AND IN THE DISCUSSION OF MID TERM PROJECTS REALIZED DURING THE COURSES. THE SECOND STEP CONSISTS IS BASED ON THE REALIZATION OF A FINAL TERM PROJECT: THE STUDENTS, PARTITIONED INTO TEAMS, ARE REQUIRED TO REALIZE A SYSTEM, FINALIZED TO A COMPETITION AMONG THE TEAMS, DESIGNING AND METHODOLOGICAL CONTRIBUTIONS OF THE STUDENTS, TOGETHER WITH THE SCORE ACHIEVED DURING THE COMPETITION, ARE CONSIDERED FOR THE EVALUATION. THE AIM IS TO ASSESS THE ACQUIRED KNOWLEDGE AND ABILITY TO UNDERSTANDING, THE ABILITY TO LEARN, THE ABILITY TO APPLY KNOWLEDGE, THE INDEPENDENCE OF JUDGMENT, THE ABILITY TO WORK IN A TEAM. IN THE FINAL EVALUATION, EXPRESSED IN THIRTIETHS, THE EVALUATION OF THE INTERVIEW AND OF THE MID TERM PROJECTS WORK WILL ACCOUNT FOR 40% WHILE THE FINAL TERM PROJECT WILL ACCOUNT FOR 60%. THE CUM LAUDE MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE THAT THEY CAN APPLY THE KNOWLEDGE AUTONOMOUSLY EVEN IN CONTEXTS OTHER THAN THOSE PROPOSED IN THE COURSE

Verification of learning

THE EXAM AIMS AT EVALUATING, AS A WHOLE: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, THE ABILITY TO APPLY THAT KNOWLEDGE TO SOLVE PROGRAMMING PROBLEMS REQUIRING THE USE OF ARTIFICIAL VISION TECHNIQUES; INDEPENDENCE OF JUDGMENT, COMMUNICATION SKILLS AND THE ABILITY TO LEARN.

THE EXAM INCLUDES TWO STEPS: THE FIRST ONE CONSISTS IN AN ORAL EXAMINATIONS AND IN THE DISCUSSION OF MID TERM PROJECTS REALIZED DURING THE COURSES. THE SECOND STEP CONSISTS IS BASED ON THE REALIZATION OF A FINAL TERM PROJECT: THE STUDENTS, PARTITIONED INTO TEAMS, ARE REQUIRED TO REALIZE A SYSTEM, FINALIZED TO A COMPETITION AMONG THE TEAMS, DESIGNING AND METHODOLOGICAL CONTRIBUTIONS OF THE STUDENTS, TOGETHER WITH THE SCORE ACHIEVED DURING THE COMPETITION, ARE CONSIDERED FOR THE EVALUATION.
THE AIM IS TO ASSESS THE ACQUIRED KNOWLEDGE AND ABILITY TO UNDERSTANDING, THE ABILITY TO LEARN, THE ABILITY TO APPLY KNOWLEDGE, THE INDEPENDENCE OF JUDGMENT, THE ABILITY TO WORK IN A TEAM.

IN THE FINAL EVALUATION, EXPRESSED IN THIRTIETHS, THE EVALUATION OF THE INTERVIEW AND OF THE MID TERM PROJECTS WORK WILL ACCOUNT FOR 40% WHILE THE FINAL TERM PROJECT WILL ACCOUNT FOR 60%. THE CUM LAUDE MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE THAT THEY CAN APPLY THE KNOWLEDGE AUTONOMOUSLY EVEN IN CONTEXTS OTHER THAN THOSE PROPOSED IN THE COURSE

	Texts
	Lecture notes provided during the course.

	More Information
	Course language is English.

BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2022-05-23]

Alessia SAGGESE | AUTONOMOUS VEHICLE DRIVING