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AUTONOMOUS VEHICLE DRIVING

Mario VENTO AUTONOMOUS VEHICLE DRIVING

0622700063
DEPARTMENT OF INFORMATION AND ELECTRICAL ENGINEERING AND APPLIED MATHEMATICS
EQF7
COMPUTER ENGINEERING
2024/2025

OBBLIGATORIO
YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2022
SPRING SEMESTER
CFUHOURSACTIVITY
324LESSONS
18LAB
216EXERCISES
MARIO VENTO 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.

APPLYING 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
TEACHING UNIT 1 - INTRODUCTION
(LESSON/PRACTICE/WORKSHOP HOURS 8/0/0)

1 (3 HOURS LESSON): SMART CITY, SMART ROAD, AUTONOMOUS AND CONNECTED VEHICLES
2 (2 HOURS LESSON): SELF-DRIVING VEHICLES AND LEVELS OF AUTOMATION
3 (3 HOURS LESSON): CONNECTED VEHICLES: I2V AND V2X

KNOWLEDGE AND UNDERSTANDING: UNDERSTANDING THE CONCEPT OF AUTONOMOUS AND CONNECTED VEHICLES, LEVELS OF AUTOMATION AND THE I2V AND V2X PARADIGMS
APPLIED KNOWLEDGE AND UNDERSTANDING: ABILITY TO DISTINGUISH THE CHARACTERISTICS OF AUTONOMOUS AND CONNECTED VEHICLES.

TEACHING UNIT 2 - HARDWARE AND SOFTWARE OF AUTONOMOUS AND CONNECTED VEHICLES
(LESSON/PRACTICE/WORKSHOP HOURS 10/0/8)

4 (2 HOURS LABORATORY): HARDWARE ARCHITECTURE OF A SELF-DRIVING VEHICLE: EXAMPLE OF MIVIA CAR
5 (3 HOURS LABORATORY): NAVIGATION SOFTWARE OF A SELF-DRIVING VEHICLE: EXAMPLE OF MIVIA CAR
6 (2 HOURS LESSON): STATE ESTIMATION WITH GNSS AND IMU: PRINCIPLES
7 (3 HOURS LESSON): STATE ESTIMATION WITH GNSS AND IMU: KALMAN FILTER
8 (2 HOURS LESSON): LONGITUDINAL AND LATERAL CONTROL: PRINCIPLES
9 (3 HOURS LABORATORY): LONGITUDINAL AND LATERAL CONTROL IN PRACTICE: EXAMPLE MIVIA CAR
10 (2 HOURS LESSON): PRINCIPLE OF OPERATION OF SENSORS (CAMERA, LIDAR, RADAR, SONAR) AND THEIR POSITIONING

KNOWLEDGE AND UNDERSTANDING: KNOWLEDGE OF THE HARDWARE AND SOFTWARE ARCHITECTURE OF SELF-DRIVING VEHICLES, ABILITY TO DESIGN THE POSITIONING OF SENSORS ON THE VEHICLE AND KNOWLEDGE OF THE PRINCIPLES FOR STATE ESTIMATION AND FOR LONGITUDINAL AND LATERAL CONTROL OF A VEHICLE
APPLIED KNOWLEDGE AND UNDERSTANDING: ABILITY TO DESIGN AUTONOMOUS NAVIGATION HARDWARE AND SOFTWARE

TEACHING UNIT 3 - CREATION OF SOFTWARE FOR AUTONOMOUS DRIVING: AUTOWARE
(LESSON/PRACTICE/WORKSHOP HOURS 5/0/3)

11 (3 HOURS LESSON): HIERARCHICAL PLANNING AND MISSION PLANNING IN AUTOWARE
12 (2 HOURS LESSON): BEHAVIOR PLANNING AND LOCAL PLANNING IN AUTOWARE
13 (3 HOURS LABORATORY): AUTOWARE DEMONSTRATION

KNOWLEDGE AND UNDERSTANDING: KNOWLEDGE OF THE SOFTWARE MODULES THAT ALLOW THE CREATION OF AN AUTONOMOUS DRIVING SYSTEM
APPLIED KNOWLEDGE AND UNDERSTANDING: ABILITY TO DESIGN AND IMPLEMENT AN AUTONOMOUS DRIVING ALGORITHM IN AUTOWARE

TEACHING UNIT 4 - SIMULATION OF AUTONOMOUS DRIVING SOFTWARE: CARLA
(LESSON/PRACTICE/WORKSHOP HOURS 2/8/0)

14 (2 HOURS LESSON): INTRODUCTION TO CARLA
15 (3 HOURS PRACTICE): VEHICLE CONTROL IN CARLA
16 (2 HOURS PRACTICE): SENSORS AND ENTITIES IN CARLA
17 (3 HOURS PRACTICE): AUTONOMOUS NAVIGATION IN CARLA

KNOWLEDGE AND UNDERSTANDING: KNOWLEDGE OF THE CARLA SIMULATOR FOR THE SIMULATION OF AUTONOMOUS DRIVING ALGORITHMS
APPLIED KNOWLEDGE AND UNDERSTANDING: ABILITY TO DEVELOP AND SIMULATE AUTONOMOUS DRIVING SOFTWARE IN THE CARLA SIMULATION ENVIRONMENT

TEACHING UNIT 5 - FINAL PROJECT
(LESSON/PRACTICE/WORKSHOP HOURS 0/0/4)

18 (2 HOURS WORKSHOP): IMPLEMENTATION OF THE PROJECT
19 (2 HOURS WORKSHOP): IMPLEMENTATION OF THE PROJECT

KNOWLEDGE AND UNDERSTANDING: UNDERSTANDING THE SPECIFICATIONS OF THE FINAL PROJECT
APPLIED KNOWLEDGE AND UNDERSTANDING: ABILITY TO DESIGN AND IMPLEMENT AN ALGORITHM FOR AUTONOMOUSLY DRIVING A VEHICLE IN A GROUP

TOTAL HOURS LESSON/PRACTICE/WORKSHOP 25/8/15
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 CARLA.

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 ACHIEVEMENT OF THE TEACHING OBJECTIVES IS CERTIFIED BY PASSING AN EXAM WITH AN EVALUATION OUT OF THIRTY. THE EXAM INCLUDES THE DISCUSSION OF A PROJECT CARRIED OUT IN GROUPS (WITH GROUPS OF 3-4 PEOPLE) AND AN INDIVIDUAL ORAL EXAM.
THE REALIZATION OF THE PROJECT IS AIMED AT DEMONSTRATING THE ABILITY TO APPLY THE KNOWLEDGE THROUGH THE CREATION OF AN AUTONOMOUS NAVIGATION ALGORITHM, VERIFIED IN A SIMULATED ENVIRONMENT. THE DISCUSSION OF THE PROJECT INCLUDES A PRACTICAL DEMONSTRATION OF THE REALIZED SYSTEM AND THE DEFENSE OF THE DESIGN CHOICES DESCRIBED IN THE PROJECT REPORT.
THE ORAL EXAM AIMS TO VERIFY THE LEVEL OF KNOWLEDGE AND UNDERSTANDING OF THE TOPICS COVERED IN THE COURSE, AS WELL AS THE STUDENT'S PRESENTATION ABILITY.
Texts
LECTURE NOTES PROVIDED DURING THE COURSE.

THE TEACHING MATERIAL IS AVAILABLE ON THE UNIVERSITY E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT) ACCESSIBLE TO STUDENTS USING THEIR OWN UNIVERSITY CREDENTIALS.
More Information
THE COURSE IS HELD IN ENGLISH
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