Enrolment options

MEC-E5012 - Vehicle Mechatronics: Control D, Lecture, 24.10.2022-28.11.2022

Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

The student
1. can recognise intelligent vehicle functions, ranging from non-autonomous to fully autonomous systems.
2. can design, simulate and implement autonomous vehicle localisation, mapping and navigation.
3. can perceive an intelligent vehicle system as a sum of subsystems and study their functionalities.
4. can work in a team that designs the control and analyses a autonomous miniature vehicle.
5. can evaluate and compare different autonomous vehicle control and performance, including the comparison of own design to scientific state-of-the-art.

Credits: 5

Schedule: 24.10.2022 - 28.11.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Jari Vepsäläinen, Kari Tammi

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    Week, Lecture, Exercise, Other
    - Introduction to intelligent vehicles, ADAS control exercises
    - Decision-making under uncertainty, Markov Decision process
    - Reinforced learning for autonomous vehicles, Reinforced learning (Python)
    - Introduction to Turtlebots (incl. videos), Turtlebots: SLAM & Navigation (Python), in addition to the lecture: 2 hours of videos
    - Advanced Turtlebot functions (incl. videos), Turtlebots: Basic Programming (Python)

    - Wrap up, no excercise, project presentations

Assessment Methods and Criteria

  • valid for whole curriculum period:

    1. Lecture quiz: weight about 20 %
    2. Exercises: weight about 50 %
    3. Project: weight about 30 %
    To pass the course at least 50 % of the points in all three categories much be achieved. The final grade is defined by the sum of points of each categories in respect to the weights given above. Peer evaluation may be used in the course.

Workload

  • valid for whole curriculum period:

    Learning activity: Workload calculation (hours), Remarks
    - Lectures: 6x2h
    - Independent videos: 2h
    - Learning portfolio (learning diary): 6x0.5h lecture quizzes
    - Computer exercises: 5x8h, Python and MATLAB exercises including 1,5h of contact sessions.
    - Group work (project): 60h, outcome: summary and slides
    - Wrap up (project gala): 3h

DETAILS

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    8 Decent Work and Economic Growth

    9 Industry, Innovation and Infrastructure

    11 Sustainable Cities and Communities

    13 Climate Action

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Autumn II
    2023-2024 No teaching

    Enrollment :

    RRegistration for courses will take place on Sisu (sisu.aalto.fi)

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof

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