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Course syllabus - Design of autonomous systems

Scope

7.5 credits

Course code

DVA472

Valid from

Autumn semester 2018

Education level

Second cycle

Progressive Specialisation

A1F (Second cycle, has second-cycle course/s as entry requirements).

Main area(s)

Computer Science

School

School of Innovation, Design and Engineering

Ratified

2018-02-01

Status

This syllabus is not current and will not be given any more

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2018-01-09
    • Latest update: 2019-01-18

    Books

    Thrun, Sebastian; Burgard, Wolfram; Fox, Dieter

    Probabilistic robotics

    Cambridge, Mass. : MIT Press, cop. 2005 - xx, 647 s.

    ISBN: 0262201623 LIBRIS-ID: 10196274

    Chapter 3

    Articles

    AutoRIO: An Indoor Testbed for Developing Autonomous Vehicles

    International Japan-Africa Conference on Electronics, Communications and Computations (JAC-ECC),

    Web Addresses

    Deep Learning

    MIT Press,

    URL: Link

Objectives

The aim with the course is to give the students ability to control dependability for autonomous systems built with essential building blocks such as data fusion, computer vision, and artificial intelligence for perception and decision making.
 
 
 

Learning outcomes

After completing the course, the student shall be able to:
 
1. explain guidance strategies for autonomous vehicles
2. summarize the theory behind and the implementation of sensing and perception of autonomous vehicles
3. exemplify how data fusion of different sensor data can be applied
4. compare possible architectures for autonomous systems with long response times
5. explain the theory and design of energy management and harvesting methods in autonomous systems
6. program heterogeneous embedded systems for autonomous applications, and
7. discuss methods for analysis and verification of autonomous systems

Course content

Data fusion and computer vision, Artificial intelligence for perception and decision making, Redundancy techniques for reliability and fail-safe designs for autonomous systems, Heterogeneous sensing and computing systems, and energy management.

Tuition

Lectures, project and laboratory work.

Specific requirements

At least 160 credits of which at least 30 credits computer science, and/or 30 credits in electronics, and Autonomous vehicles 5 credits or equivalent.

​In addition, Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English, exemption is made from the requirement in Swedish course B/Swedish course 3.

 

Examination

Laboratory work (LAB1), 3 credits, examines the learning objectives 2, 3, 4 and 6, Marks Fail (U) or Pass (G)
Project (PRO1), 3 credits, examines the learning objectives 1-4 and 6-7, Marks Fail (U), 3, 4 or 5
Written assignment (INL1), 1,5 credits, examines the learning objectives 1, 5 and 7, Marks Fail (U), 3, 4 or 5
 

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Grade

Pass with distinction, Pass with credit, Pass, Fail