The subject covers the linear algebra required for post-graduate engineering courses. The learner will gain the expertise to interpret the linear algebra models used in the engineering literature. It will also enable learners to model problems using linear algebra methods.

1. Solve systems of linear equations and analyse the solutions.

2. Analyse affine transformations in three dimensions.

3. Interpret the linear algebra in state of the art research publications andreproduce findings.

4. Explain the use of vector spaces in analysing solutions to systems of equations.

5. Use projections to find the least squares solution of overdetermined systems.

6. Decompose matrices into their singular value decompositions and interpret.

7. Apply matrices to the Fourier transform, graphs and networks.

ADAS and Autonomous System Architecture provides the learner with an appreciation for the bigger picture of the automotive industry. The student will gain an understanding of the multi-disciplinary nature of the industry, as well as knowledge of its supply chain. Different system architectures and design constraints are introduced.

1. Demonstrate an understanding of the automotive landscape, including its supply chain, and the associated roles and responsibilities.
2. Assess Automotive System Architectures, past, present and future. Create an accurate conclusion on where the technology is currently at, both from hardware and software viewpoints.
3. Apply the theory of Vehicle Networking to critically analyse the data flow of increasingly complex future vehicle networks.

4. Conduct an analysis of the design constraints within automotive and use this information to appraise the decision-making behind current design.
5. Effectively collaborate and communicate with the engineering community on what is topical in the automotive industry.

This module introduces the topic of machine learning algorithms (algorithms that learn from data), with the first part of the module dedicated to the standard shallow forms of machine learning before moving on to Deep Learning and Convolutional Neural Networks for use in computer vision tasks, particularly recognition, classification and localisation. The emerging topic of Deep Reinforcement Learning will be briefly introduced. The module will look at training strategies and frameworks for Deep Learning. As well as the technical/scientific elements, students will reflect on the ethical implications of machine learning.

1. Compare state of the hand engineered detectors with machine learning techniques in terms of performance on appropriate metrics and data sets and determine the appropriateness of each for safety critical applications.
2. Apply transfer learning to adapt a pre-trained network to a new classification problem.
3. Assess the validity of various cost functions to specific machine learning problems.
4. Effectively collaborate and communicate with others in the timely development of solutions to machine learning problems, including reports and software.
5. Design, test and evaluate deep network architectures.
6. Appreciate the data rights of citizens and the constraints these apply to the use of pattern detection in real world scenarios.

This module looks at the computer vision required to understand the structure of a real-world scene given several images of it.

Introduces key 2D-Image Processing, segmentation and features detection techniques, camera intrinsic and extrinsic parameters and multiple view geometries.

1. Select and apply 2D Image processing techniques to appropriate problems.
2. Evaluate the applicability of various 3D image processing techniques to specific problems, based on the review of key academic papers.
3. Create or reconstruct 3D Scene geometries using various methods.
4. Effectively collaborate and communicate with others in the timely development of solutions to Computer vision problems, including reports and software.
5. Identify the key metrics that are used to measure and compare the effectiveness of state of the art computer vision techniques, and use these metrics to evaluate the performance of emerging computer vision techniques previous state of the art.

This module introduces systems engineering concepts such as the modelling and simulation of driver assistance functions as well as an overview of the test and validation requirements and processes for autonomous vehicles.

1. Critically evaluatemodel-based approaches for the development and test of advanced driver assistance systems and autonomous vehicles.

2. Summarise the System Engineering process in the development of technology for autonomous vehicles

3. Use computer aided tools to model and simulate real-world scenarios for the development of advanced driver assistance systems
4. Evaluate goal orientated architecture and the levels of abstraction for modelling of advanced driver assistance systems

5. Compare validation requirements, technologies and methods for advanced driver assistance systems and autonomous vehicles.

This module covers the state of the art theory and algorithms for multi-modal sensor fusion in autonomous vehicles with application to localisation, navigation and tracking problems.

1. Evaluate the strengths and weaknesses of common sensor technologies to the development of effective multimodal sensor architectures.
2. Apply common sensor fusion algorithms for localisation, navigation and tracking applications in the automotive environment.

3. Critically evaluate sensor fusion networks and their applicationsin the automotive environment

4. Communicate the process of design, testing and evaluation of a Sensor Fusion-based system to an audience of peers

5. Understand and articulate the key concepts of advanced sensor fusion research presented in recent literature