Outline

Overview

This unit aims at the practising ability of students on utilizing intelligent information engineering techniques for solving practical problems in the latest applications of AI, e.g., Autopilot. Students will get programming skills for many tasks related to automatic driving, including lane detection, traffic sign detection, pedestrian detection, and path planning. Lane detection, traffic sign detection, pedestrian detection, and path planning are information processing techniques that will help students to learn how to use the Video Intelligence and Signal Understanding approaches for many practical problems. All students will be involved in designing mini-projects and a large project. The unit is project-oriented. Students will run their programs on simulated environment. The course will be taught through lectures mainly on how to accomplish the goal for the mini-project and the final project. A specific lab design will be provided to students for hands-on design. Communication skills will be tested through several project presentations. Some teaching will be provided by intelligent information engineers working in the industry.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Students must have a good understanding of Linear algebra and basic mathematics, Basic Programming skills in C, Python or Matlab
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Ali Shakiba, ali.shakiba@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam xx	30%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
In-class quiz	Lab Quiz A series of multiple-choice question quizzes delivered through Canvas during scheduled lab sessions (The lowest two quiz marks will be excluded). AI use is permitted only when accompanied by the required AI Journal(s). Details are available on Canvas.	10%	Multiple weeks	In-lab MCQ quiz via Canvas, 5-10 minutes	AI allowed
Outcomes assessed:
Experimental design	Lab milestones Continuous individual progress gates tracking software engineering proficiency. Marks are awarded objectively based on individual lab milestones. AI use is permitted only when accompanied by the required AI Journal(s). Details are available on Canvas.	5%	Multiple weeks	-	AI allowed
Outcomes assessed:
Out-of-class quiz	Early Feedback Quiz Multiple-choice question quiz via Canvas. AI use is permitted only when accompanied by the required AI Journal(s). Details are available on Canvas.	5%	Week 03	15-20 minutes	AI allowed
Outcomes assessed:
Experimental design	Assignment 1 Code submitted via GitHub. Report submitted on Canvas followed by demonstration and Q&A. AI use is permitted only when accompanied by the required AI Journal(s). Details are available on Canvas.	20%	Week 07	-	AI allowed
Outcomes assessed:
Experimental design	Assignment 2 Code submitted via GitHub. Report submitted on Canvas followed by demonstration and Q&A. AI use is permitted only when accompanied by the required AI Journal(s). Details are available on Canvas.	30%	Week 13	-	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ? = early feedback task ?

Assessment summary

Two group projects:

- Assignment 1: A group project focusing on the design and implementation of a modular intelligent information engineering and local perception baseline within a standardised simulation environment.

- Assignment 2: An advanced group project giving students the freedom to choose their own advanced algorithmic focus or alternative simulation platforms, scaling up their initial baseline to solve complex, high-level autonomous driving or robotic navigation tasks.

The assessment approach of this unit covers both group and individual contributions, examining the technical merit, any extensive studies, completeness of documentation as well as the presentation of the solution.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Result name	Mark range	Description
Assignment 1	0-100	The first group project requires students to design and implement fundamental autonomous navigation and local perception functionality within a simulation environment. Details are available on Canvas.
Assignment 2	0-100	The second group project gives students the freedom to scale up their baseline systems by integrating advanced algorithmic strategies or deploying their logic across alternative simulation and hardware platforms. Details are available on Canvas.
Final Exam	0-100	The final secure, individual examination tests the student's theoretical mastery and analytical capabilities. A minimum of 30% is required to pass the course.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

20% per day

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Lecture 1: Introduction and Overview	Lecture (2 hr)
	Lab 1: Overview and System Initialisation	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 02	Lecture 2: Computer Vision & Representation Learning	Lecture (2 hr)
	Lab 2: Traffic Sign Classification and Experiment Managing	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 03	Lecture 3: Road & Lane Detection	Lecture (2 hr)
	Lab 3: Road and Lane Detection and Data version Control	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 04	Lecture 4: Object Detection & Instance Segmentation	Lecture (2 hr)
	Lab 4: Object Detection and CI/CD Pipelines	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 05	Lecture 5: Transformers & Video Understanding	Lecture (2 hr)
	Lab 5: Video Processing and Model Profiling	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 06	Lecture 6: Object Tracking & 3D Perception	Lecture (2 hr)
	Lab 6: Object Tracking and Regression Testing	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 07	Lecture 7: Localisation, Mapping & LiDAR	Lecture (2 hr)
	Lab 7: Assignment 1 Demonstrations	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 08	Lecture 8: Trajectory Tracking & Control Loops	Lecture (2 hr)
	Lab 8: Trajectory Tracking	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 09	Lecture 9: Planning & Decision Making	Lecture (2 hr)
	Lab 9: Planning and Decision Making	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 10	Lecture 10: Deep Reinforcement Learning	Lecture (2 hr)
	Lab 10: Deep Reinforcement Learning Agent Training	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 11	Lecture 11: VLLMs & End-to-End Driving	Lecture (2 hr)
	Lab 11: Imitation Learning	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 12	Lecture 12: Emerging Trends	Lecture (2 hr)
	Lab 12: System Monitoring and Drift Detection	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)
Week 13	Lecture 13: Career Paths & Wrap Up	Lecture (2 hr)
	Lab 13: Assignment 2 Demonstrations	Practical (2 hr)
	Independent study and project work	Self-directed learning (6 hr)

Attendance and class requirements

Active participation in laboratory classes is expected, as quizzes, milestones, demonstrations, and assessment activities occur during scheduled sessions.
For the AI-allowed activities, AI use is permitted only when accompanied by the required AI Journal(s). Details are available on Canvas.
Assessment code must be submitted through the prescribed GitHub repositories and comply with academic integrity requirements.
Students should regularly check Canvas for assessment information, software setup instructions, and course updates.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

The reading list is available on Canvas.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

LO1. Analyse and evaluate appropriate intelligent information engineering techniques and MLOps strategies to process video intelligenc, signal understanding streams, and sensor-acquired data.
LO2. Design, deploy, and evaluate intelligent information engineering frameworks to solve complex perceptual and spatial tracking problems on a simulated robotic vehicle.
LO3. Synthesise advanced programming pipelines and algorithms to execute research-aligned tasks in autonomous driving, including trajectory planning, localisation, and end-to-end policy optimisation.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering. Stage 1 Competency Standard for Professional Engineer (UG) - 1.2 (L2). Mathematical and computational methods. (Level 2- Attaining required standard (Bachelor Honours standard)) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline. 1.2 (L3). Mathematical and computational methods. (Exceeding required standard) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline. 3.4 (L2). Information skills. (Level 2- Attaining required standard (Bachelor Honours standard AQF8)) Professional use and management of information.
	Engineers Australia Curriculum Performance Indicators - 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 4.2. Ability to use a systems approach to complex problems, and to design and operational performance. 5.5. Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings. Stage 1 Competency Standard for Professional Engineer (UG) - 2.1 (L2). Complex problem-solving. (Level 2- Attaining required standard (Bachelor Honours standard AQF8)) Application of established engineering methods to complex engineering problem solving 2.2 (L2). Use of engineering techniques, tools and resources. (Level 2- Attaining required standard (Bachelor Honours standard AQF8)) Techniques, tools and resources 2.3 (L3). Engineering design. (Level 3- Exceeding required standard) Application of systematic engineering synthesis and design processes.
	Engineers Australia Curriculum Performance Indicators - 2.4. Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity. 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria. 4.4. Skills in implementing and managing engineering projects within the bounds of time, budget, performance and quality assurance requirements. Stage 1 Competency Standard for Professional Engineer (UG) - 1.4 (L2). Discipline research knowledge. (Level 2- Attaining required standard (Bachelor Honours standard AQF8)) Discernment of knowledge development and research directions within the engineering discipline 2.4 (L2). Engineering project management. (Level 2- Attaining required standard (Bachelor Honours standard AQF8)) Application of systematic approaches to the conduct and management of engineering projects 3.6 (L2). Team skills. (Level 2- Attaining required standard (Bachelor Honours standard AQF8)) Effective team membership and team leadership.

Engineers Australia Curriculum Performance Indicators -

Competency code	Taught, Practiced or Assessed	Competency standard
2.1		Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks.
5.1		An appreciation of the scientific method, the need for rigour and a sound theoretical basis.
5.8		Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.

Stage 1 Competency Standard for Professional Engineer (UG) -

Competency code	Taught, Practiced or Assessed	Competency standard
1.2 (L2)		Mathematical and computational methods. (Level 2- Attaining required standard (Bachelor Honours standard)) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.3 (L2)		Specialist discipline knowledge. (Level 2- Attaining required standard (Bachelor Honours standard)) In-depth understanding of specialist bodies of knowledge within the engineering discipline.
2.1 (L2)		Complex problem-solving. (Level 2- Attaining required standard (Bachelor Honours standard AQF8)) Application of established engineering methods to complex engineering problem solving

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

In line with the rapid evolution of autonomous driving technologies and industry practices, this unit undergoes regular reviews to ensure alignment with modern engineering standards. For this offering, the curriculum has been updated to place a stronger emphasis on contemporary MLOps practices, such as automated model profiling, data version control, and regression testing, alongside advanced end-to-end AI paradigms. To support these practical software architectures and facilitate rapid prototyping, the lab environment has been expanded to integrate lightweight, highly accessible simulation tools that streamline student implementation. Furthermore, our evaluation framework continues to balance collaborative engineering projects with rigorous metrics for individual technical accountability.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ELEC5308: Intelligent Information Engineering Practice

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links