Course Title: Autonomous Vehicles
Part A: Course Overview
Course Title: Autonomous Vehicles
Credit Points: 12.00
Course Coordinator: Dr Hamid Khayyam
Course Coordinator Phone: +61 3 9925 4630
Course Coordinator Email: hamid.khayyam@rmit.edu.au
Course Coordinator Location: RMIT Bundoora Campus
Course Coordinator Availability: by appointment
Pre-requisite Courses and Assumed Knowledge and Capabilities
Pre-Requisite Courses:
Successful completion of MANU2206 Autonomous Systems or MIET2370 Mechatronics Principals or Equivalent.
Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.
For your information go to RMIT Course Requisites webpage.
Course Description
This course will introduce the various concepts and components of the autonomous navigation process for autonomous vehicles.
You must have a good understanding of the introductory and intermediate courses in control systems, vehicle kinematics, and be able to develop a model of a system for navigation. System components include various types of sensors and actuators and state-of-the-art technologies.
The course will cover the application of the autonomous navigation process to the state-of-the-art driverless vehicles, as well as automated vehicles. The main concepts covered include locomotion, autonomous navigation and intelligent path planning and perception.
In the laboratory practicals, you will devise strategies and create behaviours to be exhibited by an autonomous vehicle. The driverless vehicle equipped with the autonomous behaviours that you have designed and created, is expected to perform a complex set of tasks autonomously.
Objectives/Learning Outcomes/Capability Development
Program Learning Outcomes
This course contributes to the program learning outcomes for the following program:
BH070 Bachelor of Engineering (Mechanical Engineering) (Honours)
PLO1: Demonstrate an in-depth understanding and knowledge of fundamental engineering and scientific theories, principles and concepts and apply advanced technical knowledge in specialist domain of engineering.
PLO2: Utilise mathematics and engineering fundamentals, software, tools and techniques to design engineering systems for complex engineering challenges.
PLO4: Apply systematic problem solving, design methods and information and project management to propose and implement creative and sustainable solutions with intellectual independence and cultural sensitivity.
PLO7: Collaborate and contribute as an effective team member in diverse, multi-level, multi-disciplinary teams, with commitment to First Nations peoples and globally inclusive perspectives and participation.
Course Learning Outcomes
Upon completion of this course, you will be able to:
- Analyse and apply hardware and software applications used in driverless vehicles
- Compare and contrast different sensors for state estimation, localization and mapping for unknown construction environments
- Utilise visual perception technology to optimise self driving vehicles
- Analyse path planning autonomous vehicles in complex environments
- Evaluate and discuss collaboration and communication technology through autonomous driving
Overview of Learning Activities
You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both.
Lectorials will be supplemented by lab experiments and will be combined. In addition to lecture slides and recorded videos, you will be provided with notes and opportunities to discuss their use in class, during lectorials and consultations. You will demonstrate your ability to apply the acquired knowledge through completion of written tests and assignments.
During lectorials you will be provided with examples of industry relevant applications, and simulation studies will be conducted. MATLAB and SIMULINK platform will be briefly reviewed in lectorials for real time implementation of autonomous vehicle behaviours. This is intended to aid your understanding of the theory, design, algorithms and programming procedures of automotive systems and controls.
You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course Site.
Overview of Learning Resources
Learning resources include the electronic learning package, lecture notes, class materials, recommended references, and other resources as advised by course coordinator. A detailed list of prescribed and recommended texts may be found on the course Canvas shell (accessed via myRMIT). Assignments, selected lecture notes and slides, examples of relevant MATLAB programs, SIMULINK designs, etc., will be generally provided through Canvas.
RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course Site.
There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.
Overview of Assessment
This course has no hurdle requirements.
Online quizzes – 15%
The quizzes will be designed in Canvas in multiple-choice questions format, aiming to assess your understanding of the learning material and continuously increase your knowledge in this domain.
This assessment task supports CLOs 1, 2 & 3.
Lab tests – 35%
During each laboratory session, you will work as part of a team, on implementing various autonomous driving components on a real-world vehicle downscaled to bench size. At the end of each session, the lab test will assess your understanding of how theory is put into practice and the implementation methodology. This understanding is expected to be the result of your own investigations and effective teamwork and communication.
This assessment task supports CLOs 1, 2, 3, 4 & 5.
Final Assessment – 50%
This will be in the form of a written test under timed conditions either online or on-campus. There will be questions that assess your understanding of fundamental theories delivered in lecture recordings and practical implementation of algorithms practiced during the laboratory sessions.
This assessment task supports CLOs 1, 2, 3, 4 & 5.
If you have a long term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.