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Smart, Connected and Autonomous Vehicles - MSc (Full-Time, 2018 Entry)

With the advent of smart, connected and autonomous vehicles on the horizon of technical advancements, the automotive industry is facing a developmental challenge. How do we develop a robust technical infrastructure to support the anticipated explosive growth in smart vehicular functions, communications systems and driverless cars? This demands a comprehensive understanding of the technology and a bottom-up approach ensuring robustness and dependability of Electronics, Communications (e.g. V-2-V, V-2-I) and Control Systems.

This course is designed for graduates of engineering, computer science, physics, mathematics. It is particularly suitable for those with a background in electronics, electrical engineering, control systems, or communications who want to play a role in the development of connected and autonomous vehicles, and the Intelligent Transportation Systems Network.

Through this MSc we aim to address the knowledge-gap in the areas of machine learning, automated control strategies, connectivity, and communication infrastructure, cyber-security protocols, emerging automotive networks and robust automotive embedded systems within the context of smart, connected and autonomous vehicles.

This MSc uses unique experimental facilities which enable academics and industry practitioners to work together. It has extensive industrial support with the Industry Advisory Board consisting of Jaguar Land Rover (JLR), RDM and other industrial stakeholders.

Core Modules:
Modules are generally taught in intensive one-week blocks, from Monday to Friday, 9.00am – 6.30pm. These one-week sessions (nine in total over the academic year) are scheduled at intervals from October through to June. Weekend sessions are also occasionally required.

Core modules are compulsory and relate specifically to this course.

  • Sensor and Sensor Fusion
  • Human-Technology Interaction
  • Robust Automotive Embedded Systems
  • Networks and Communications for the Connected Car
  • Machine Intelligence and Data Science


Optional Modules:

Four elective modules to be selected from a wide list of options.The full list of modules offered can be reviewed here.

* The modules mentioned above may be subject to change. Please read our terms and conditions for more detailed information.

Teaching:
This course has two components – a taught component and a research component (dissertation) each accounting for 50% of your time and effort.

For the taught component, we blend lectures with workshops, practical exercises, demonstrations, case studies, syndicate exercises, extended surgery time, and reviews.

Class sizes are kept small with around 30 students in each, to encourage interaction. Larger scale lectures are delivered for some modules and are backed up by seminar and syndicate activities.

Our module leaders have extensive industry experience. Guest speakers from industry also contribute regularly, bringing real-world insight into your learning experience.

For the research component, each student undertakes a major individual project, which will develop your research and analytical skills and enable you to specialise. This project accounts for 50% of your overall credits and is submitted in the form of a dissertation of approximately 20,000 words, followed by an oral examination or viva at the end of the year.

Leveraging the close partnerships that WMG has with key organisations within the automotive sector, it is envisaged that your project will have an industrial sponsor, enabling you to work in close collaboration with an industry partner. This valuable experience will further your transferrable skills development, and expand your networking opportunities and understanding in a professional research and development environment.


Assessment:
There are no written exams. After each module you will be assessed by a written post-module assignment (PMA). This typically requires 40 – 60 hours of work and consolidates your learning. Some modules will also include an in-module individual or group assessment or test.

9 x Post Module Assignments (3,000 – 4,000 words each) (50% of Masters credit).

20,000 dissertation (50% of Masters credit).

Graduate Destinations:

Graduates of this MSc will understand a myriad of factors contributing towards the performance and dependability of connected and autonomous vehicles and will be well placed to continue professional work within R&D.

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Essential information

Duration

Full-time: 1 year

Entry requirements

2:ii undergraduate degree (or equivalent) in Engineering, Maths, Science or
Technology subjects.

English Language requirements

Band A

Department of study

Warwick Manufacturing Group

Location of study

University of Warwick

Course fees

Full time:
Home/EU: £13,440
Overseas: £24,640

Find out more about fees and funding.

 

Department website

Order a PG magazine

Application information

 

This information is applicable for 2018 entry.