This module will provide the learner with the necessary research skills to undertake a level 9 research project. The learner will: Study the different paradigms and methodologies of the research study. Study the different methods of data collection and data analysis associated with the chosen approach. Learn how to analyse research publications. Disseminate research in terms of reports and journal publications. Effectively communicate their research outcomes.

1. Critically evaluate existing knowledge and its application to the student’s chosen research area.

2. Develop a critical awareness of current problems and/or new insights, generally informed by the forefront of research in their chosen area.

3. Analyse paradigms of research enquiry and explicate where chosen paradigms fit within their research area of interest.

4. Expertly identify, discuss and propose a range of data collection and analysis tools and techniques relevant to their study.

5. Demonstrate a critical understanding of appropriate project management skills to ensure successful completion of level 9 research project.

6. Communicate effectively their research outcomes.

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 involves modelling and analysis of vehicle dynamics including drag, tyre friction and vibration and the effect of these on the vehicle perfomance and driver experience. A number of electronic dynamic assist strategies are explored and the student will develop the skills to evaluate the effectiveness of such strategies (e.g. braking control, stability control, self-steering response).

1. Develop mathematical models describing the dynamics of a vehicle taking account of drag and tyre properties.
2. Model the vehicle suspension system and analyse the vertical vibrations of a vehicle.
3. Evaluate the effect of the dynamics of a vehicle and environment on the driver experience.
4. Evaluate and critique a range of electronic dynamic assistance strategies in terms of driver experience.
5. Use computer aided tools to model and analyse the dynamics of a vehicle with and without driver assist technologies.

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 aims to provide the learner with an up to date, comprehensive knowledge of what constitutes an Intelligent Transport System. The module looks at various vehicle connections such as V2V, V2I, V2P and ultimately V2X and encourages the learner to analyse their impact on the driver experience and society at large. The critical aspects of wireless communication is considered in the context of an Intelligent transport System.

1. Identify and describe an Intelligent Transport System (ITS), and distinguish between its constituant parts.
2. Discuss the technologies used in Connected Vehicles, and integrate this knowledge with that already gained on sensor technologies.
3. Develop an understanding of the basics of wireless communications, with focus on the critical evaluation metrics as appropriate for Vehicle Communication technologies.
4. Discuss the application categories for Vehicle Communication Technologies, both safety and non-safety, and determine their associated challenges.
5. Research the V2X technology evolution, and form an opinion on where the technology is going.
6. Assess the societal impact of Intelligent Transport Systems.

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