2022/23 Taught Postgraduate Module Catalogue

MECH5170M Connected and Autonomous Vehicles Systems

15 creditsClass Size: 120

Module manager: Dr Krzysztof Kubiak
Email: K.Kubiak@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2022/23

Pre-requisite qualifications

Basic Programming Skills

This module is not approved as an Elective

Module summary

This module provides comprehensive knowledge of Connected and Autonomous Vehicle technology. It will cover aspects of sensors selection, data acquisition, path planning, localisation, artificial intelligence algorithms, communication, testing and validation. The assessment of the module is based on a coursework assignment worth 40%, a practical component related to matlab/python programming worth 10% and an online exam worth 50% of the module mark.

Objectives

This module aims to provide a comprehensive understanding and in-depth knowledge of connected and autonomous vehicles. On completion of this module, students should be able to:
1. Recognise the distinctions between Advanced Driving Assistance Systems (ADAS), connected and autonomous vehicles, and describe the main system components.
2. Assess and compare the functionality of various automotive sensors, as well as their operating principles, performance, and limitations.
3. Show their understanding of autonomy levels, path planning, and localisation.
4. Assess the performance and security of embedded automotive vehicle control systems.
5. Investigate methods for system validation and testing including hardware/software in the loop.
6. Demonstrate knowledge of the regulatory framework, approval processes, and ethical issues.

Learning outcomes
On completion of this module, students should be able to:
1. Demonstrate in-depth knowledge and understanding of the autonomous vehicle system's design and functions.
2. Critically evaluate and compare different automotive sensors, working principles, performance, limitations, and sensor fusion strategies.
3. Select and analyse path planning algorithms and models, evaluate task allocation and judge its advantages and limitations.
4. Assess and critically evaluate embedded vehicle control systems in the context of connected and autonomous vehicle safety and security risks.
5. Understand and apply testing and validation strategies of sensors, models and programming code of autonomous systems.
6. Demonstrate knowledge and understanding of regulatory requirements, professional responsibilities, and the ethical implications of connected and autonomous vehicles.

Upon successful completion of this module the following UK-SPEC learning outcome descriptors are satisfied:
A comprehensive understanding of the relevant scientific principles of the specialisation (SM1m, SM7M)
Knowledge and understanding of mathematical and statistical methods necessary to underpin their education in their engineering discipline and to enable them to apply a range of mathematical and statistical methods, tools and notations proficiently and critically in the analysis and solution of engineering problems (SM2m)
A comprehensive knowledge and understanding of mathematical and computational models relevant to the engineering discipline, and an appreciation of their limitations (SM5m)
Understanding of concepts relevant to the discipline, some from outside engineering, and the ability to evaluate them critically and to apply them effectively, including in engineering projects (SM6m, SM9M)
Understanding of engineering principles and the ability to apply them to undertake critical analysis of key engineering processes (EA1m)
Ability to identify, classify and describe the performance of systems and components through the use of analytical methods and modelling techniques (EA2)
Ability both to apply appropriate engineering analysis methods for solving complex problems in engineering and to assess their limitations (EA3m, EA6M)
Understanding of, and the ability to apply, an integrated or systems approach to solving complex engineering problems (EA4m)
Apply advanced problem-solving skills, technical knowledge and understanding, to establish rigorous and creative solutions that are fit for purpose for all aspects of the problem including production, operation, maintenance and disposal (D4)
Knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations (D7m, D10M)
Apply their skills in problem solving, communication, information retrieval, working with others and the effective use of general IT facilities (G1)

Syllabus

1. Introduction to levels of autonomy, Advanced Driving Assistance Systems (ADAS), Connected and Autonomous Vehicles (CAV).
2. Sensors, sensor fusion, machine vision, perception, and visualisation.
3. Path planning, localisation, autonomy, decision making, and Artificial Intelligence algorithms.
4. Embedded Vehicle Control Systems, functional safety ISO26262, safety-critical systems including network and communication protocols.
5. Virtual Learning Environment, Software-in-Loop, Hardware-in-Loop Testing and Validation.
6. Regulatory framework and ethical challenges.

Teaching methods

Delivery type	Number	Length hours	Student hours
Class tests, exams and assessment	1	2.00	2.00
Lecture	9	2.00	18.00
Practical	1	2.00	2.00
Tutorial	2	2.00	4.00
Independent online learning hours			9.00
Private study hours			115.00
Total Contact hours			26.00
Total hours (100hr per 10 credits)			150.00

Private study

Students are expected to read/revise before and following lectures. They are also expected to solve tutorial questions. The practical is expected to take 10 hours to complete and the assignment is expected to take 40 hours to complete.

Opportunities for Formative Feedback

Formative feedback will be provided through tutorials and practical sessions where the module leader will be available to discuss the progress with individual students. Formative feedback will also be provided through response to questions send to module leader by email.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	Project Report Maximum of 2500 words	50.00
Total percentage (Assessment Coursework)		50.00

Resit opportunities will be provided for all assessment components to cover all learning outcomes: - Project Report will be similar in format but with a different topic for the assignment, - Practical will be available to complete again directly in the laboratory or remotely, - Standard exam will have a resit paper prepared for the resit session.

Exams

Exam type	Exam duration	% of formal assessment
Unseen exam	2 hr	50.00
Total percentage (Assessment Exams)		50.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 31/10/2022

Disclaimer

Browse Other Catalogues

• Undergraduate module catalogue
• Taught Postgraduate module catalogue
• Undergraduate programme catalogue
• Taught Postgraduate programme catalogue

Errors, omissions, failed links etc should be notified to the Catalogue Team.PROD