Course syllabus

Course-PM

MMS210 Connected fleets in data-driven engineering - lp4 (7.5 hp)

The course is offered by the Department of Mechanical Engineering.

Contact details

Please contact the course staff for any further questions about the teaching or course subjects. If a meeting is required, please make contact by email to request an appointment (during office hours).

Examiner and lecturer

Ola Benderius, 031-772 2086, ola.benderius@chalmers.se

Teaching assistant

Vivien Lacorre, vivien.lacorre@chalmers.se

Tarun Kadri Sathiyan, tarun.sathiyan@chalmers.se

Study administrator

Jamal Nasir, jamal.nasir@chalmers.se

Course purpose

The purpose of the course is to aid engineers in data-driven decisions connected to vehicle features and development. By also adding continuous experimentation and remote system monitoring, the course will be vital in engineering processes around functional safety. Finally, as fully connected fleets also represent structural changes to transportation in society, it also includes deep and thorough discussion around automated data-collection connected to human activities, as a way to shift previous collect-all strategies into sound ethical engineering principles as endorsed by our governments through recent legislation such as GDPR. 

Course design

The course consist of lectures, group work sessions, and a lab sessions including field work. In the lectures the theoretical parts will be covered which will provide the students with a broad overview of the subject area. Then, the students are expected to apply the knowledge from the lectures in the group work and the lab work. The exercises involves instruction for the lab assignment and related field work.

The group work sessions are mandatory, as well as the lab work. All parts are graded by submitted reports.

Note: Group work and lab field work needs to be done on-site. Remote presence will not be accepted.

Schedule

TimeEdit

Lectures (preliminary):

Wed	25/3	10:00-11:45	HC2	Lecture 1: Introduction
Wed	1/4	10:00-11:45	HC2	Lecture 2: Data-driven engineering
EASTER
Wed	22/4	10:00-11:45	HC2	Lecture 3: Connected fleets frontend–backend
Wed	29/4	10:00-11:45	HC2	Lecture 4: Data enrichment and sensor fusion
Wed	6/5	10:00-11:45	HC2	Lecture 5: DevOps and software engineering with connected fleets
Wed	13/5	10:00-11:45	HC2	Lecture 6: Introduction to cybersecurity, ethics and legal frameworks
Wed	20/5	10:00-11:45	HC2	Lecture 7: Operational design and data-driven requirements engineering

Lab work (preliminary):

Each lab session includes a mandatory step-by-step instruction to be solved with a computer, in group. In addition, the sessions are a natural meeting point for project-related questions and support.

Wed	25/3	13:15-15:00	ES61, ES62, ES63	Lab 1: Logger basics, project proposal
Wed	1/4	13:15-15:00	ES61, ES62, ES63	Lab 2: Working with remote data and visualization
EASTER
Wed	22/4	13:15-15:00	ES61, ES62, ES63	Lab 3: Collecting data with the logger and access it on the cloud
Wed	29/4	13:15-15:00	ES61, ES62, ES63	Lab 4: Data-driven engineering, example with raw data
Wed	6/5	13:15-15:00	ES61, ES62, ES63	Lab 5: Data-driven engineering, example with a Kalman filter
Wed	13/5	13:15-15:00	ES61, ES62, ES63	Lab 6: OTA updates of vehicle ECUs
Wed	20/5	13:15-15:00	ES61, ES62, ES63	Lab 7: To be announced/Extra

Project presentations:

Mon	13/4	10:00-11:45	HC2	Project presentation 1 (WIP): Groups 1-9
Wed	15/4	10:00-11:45	HC2	Project presentation 1 (WIP): Groups 10-18
Wed	15/4	13:15-15:00	HC3	Project presentation 1 (WIP): Groups 19-28
Mon	25/5	10:00-11:45	HC3	Project presentation 2: Groups 1-9
Wed	27/5	10:00-11:45	HC2	Project presentation 2: Groups 10-18
Wed	27/5	13:15-15:00	HC2	Project presentation 2: Groups 19-28

Group work:

Mon	30/3	10:00-11:45	HC3	Group work 1
EASTER
Mon	20/4	10:00-11:45	HC2	Group work 2
Mon	27/4	10:00-11:45	HC2	Group work 3
Mon	4/5	10:00-11:45	??? (will check)	Group work 4
Mon	11/5	10:00-11:45	???	Group work 5 (spare)

Extra:

In case some slot needs to be rescheduled.

Mon

18/5

10:00-11:45

HC2

Extra

Course literature

Lecture notes, source code templates, and web material. The material will be made available via the course web page.

Learning objectives and syllabus

• Apply large-scale fleet monitoring, and describe involved technologies and how logged data can be used in the engineering process
  Describe properties of hardware components needed in each unit of a connected fleet, including their software life-cycles.
• Describe properties of core software components needed in a backend environment, connected to the engineering process.
• Apply and monitor over-the-air updates to a fleet of mobile systems, and describe limitations from different underlying technologies.
• Apply software development connected to continuous integration and continuous deployment in heterogeneous ECU networks.
• Describe relevant cybersecurity measures to safeguard the connected fleet and the its generated data.
• Describe ethical aspects of fleet monitoring and over-the-air updates, and how such concepts can be combined with ethical engineering related to governmental intentions as defined by, for example, GDPR.
• Define complete engineering process involving all learning outcomes from the course.

Examination form

The examination consists of graded project work, and a series of group work.

See the following subsections for details regarding assessment and grading. Refer also to the below section regarding plagiarism and the use of tools such as ChatGPT.

Grades

The grades that are given in this course are the following: 5, 4, 3, U (not passed).

Project assignment including field work

The mandatory project assignment will be carried out in groups of 4-5 students, where each group focuses on one specific type of vehicle (e.g. ferry, bicycle, e-scooter, car, truck, tram, train, drone) chosen by the students (with some set conditions from the course examiner). The lab involves field work that is scheduled by each group separately. Groups are responsible to acquire access to relevant vehicles for field measurements, but in special cases the course staff can provide help. Measurements should ONLY be done on vehicles in normal and safe traffic conditions, and relevant driver's licenses should be ensured! A report explaining the work process and work outcome, including pictures from active field work showing all group members, should be submitted before the deadline, and will be graded with 3, 4, or 5. A minimum grade of 3 is required in order to pass the course. The project assignment correspond to 70% of the final course grade. For late submissions, the grade may be affected. Groups can be suggested by students, but the examiner will ultimately decide group compositions based on vehicle selection.

Group work

There are four scheduled mandatory group work/discussion in the course. The group work correspond to 30% of the final course grade, where the four results are averaged. The groups will be randomized, and different every time. The grade of the group work is the mean grade on all group submissions. After each group discussion, two groups will be selected on random to present their key points in class. Each group work session is outlined as: 10 min introduction, 60 min of discussion and report writing (including break), 20 min of wrap up and student presentations (two groups).

Final course grade

The student’s final course grade will be determined according to the grade of the lab work (3, 4, or 5) and the mean rounded grade of the four exercises (3, 4, or 5), combined as 0.7*<lab assignment> + 0.3*<group work mean>. The resulting value, rounded to the nearest integer value, is the final individual grade of the course. The following table show the resulting grades.

Project	Group work	Final grade
3	3	3
3	4	3
3	5	4
4	3	4
4	4	4
4	5	4
5	3	4
5	4	5
5	5	5

Regarding plagiarism

Briefly, plagiarism occurs when someone present ideas, concepts, texts, or other structures from someone else, as their own. I.e. without appropriately acknowledging the original source. See further in the document about Academic integrity and honesty at Chalmers (link).

The reports (and other submitted materials such as e.g. programming code) should be original work in order to be passed. Therefore, all reports and texts that you submit for grading and examination will be checked with Ouriginal, a tool to detect plagiarism.

Avoid using tools like ChatGPT without understanding the underlying principles. It's very likely that you will get errors in your solution by trusting such tool. If you use such tools, you MUST document and submit ALL dialogue with the bot, and how the answers were incorporated in your solution.

Note: All suspected cases of plagiarism (not only those detected by Ouriginal) will be reported to the university disciplinary committee (disciplinnämnden)!