The automotive sector is changing rapidly. Car manufacturers and technology companies are developing new autonomous technologies that will redefine the future of transport. With the rapid adoption of smart vehicle functions, industry requires a unique set of skills from its engineers and programmers. The MSc in Connected and Autonomous Vehicle Engineering (Automotive) will develop your technical and transferable skills in autonomous navigation, sensing and perception, systems integration, human factors, and ethical/legal frameworks, to prepare you for a career within the automotive sector.

Overview

  • Start dateOctober
  • DurationOne year full-time
  • DeliveryTaught component (50%), Group project (10%), Individual research project (40%)
  • QualificationMSc
  • Study typeFull-time
  • CampusCranfield campus

Who is it for?

This course is suitable for engineering, science, mathematics and computing graduates alongside experienced engineers who are interested in a career in the automotive or intelligent mobility sectors. The course is intended to equip its graduates with skills that will be of immediate use but will also develop them for senior technical and business leadership roles in future. With the growing demand for highly skilled professionals both within automotive manufacturers and the high technology supply chain, successfully completing this course will provide a distinctive skill set that graduates will find useful in securing employment globally.


Why this course?

Cranfield has a long and excellent track record in graduate courses for the automotive industry, a strong track record in research, and strong links and collaborations with the automotive OEM and Tier 1 companies and their supply chains. Cranfield has an exceptionally high level of engagement with industry, and our graduates are highly valued.

As a postgraduate-only university, Cranfield University is well suited to the needs of those studying at Masters’ level, and has excellent facilities to support teaching and learning. We are located near Milton Keynes, which is emerging as a centre of excellence for connected and autonomous vehicles. We are placed centrally in the Oxford-Cambridge ‘arc’, noted for its enterprise in technology.

Cranfield has recently opened a new Intelligent Mobility Engineering Centre (IMEC) and the Multi-User Environment for Autonomous Vehicle Innovation (MUEAVI, a test ground for connected and autonomous vehicle engineering), both of which are used to support teaching across the automotive subject spectrum.

Informed by Industry

The MSc in Connected and Autonomous Vehicle Engineering (Automotive) is directed by an Industrial Advisory Panel comprising senior engineers from the automotive sector. This maintains course relevancy and ensures that graduates are equipped with the skills and knowledge required by leading employers. You will have the opportunity to meet this panel and present your individual research project to them at an annual event held in July. Panel members include:

Mr Rod J Calvert OBE (Chair), Automotive Management Consultant
Mr Steven Miles, Ford Motor Company Ltd
Mr Clive Crewe, AVL
Mr Peter Stoker, Millbrook
Mr Stefan Strahnz, Mercedes-AMG Petronas Motorsport
Mr Simon Dowson, Delta Motorsport
Mr Paul McCarthy, JCB Power Systems
Mr Steve Swift, Emerald Automotive
Mr Doug Cross, Flybrid Automotive Ltd
Mr Steve Henson, Barclays
Dr Leon Rosario, Ricardo
Mr David Hudson, Tata Motors
Mr Tobias Knichel, Punch Flybrid Limited
Mr Iain Bomphray, Williams Advanced Engineering
Mr Keith Benjamin, Jaguar Land Rover

Course details

The course will include ten taught compulsory modules, which are generally delivered from October to March. Planned module titles include:

• Fundamentals of Road Vehicle Engineering
• Path Planning, Autonomy and Decision Making
• Sensors, Perception and Visualization
• Systems Engineering
• Implementation of Embedded Systems
• Transport System Optimization
• Human Factors, Human-Computer Interaction and ADAS Systems
• Networked Systems and Cybersecurity
• Ethics, Safety and Regulation
• Technology Strategy and Business Models

Course delivery

Taught component (50%), Group project (10%), Individual research project (40%)

Group project

The course will contain a challenging group design project with a multidisciplinary engineering focus and an in-depth individual design project. Where possible, connected and autonomous vehicles from research projects will be used to support learning.

Individual project

After having gained an excellent understanding of methods and applications, you will work full-time (May to September) on an individual research project. This research project will allow you to delve deeper into an area of specific interest, taking the theory from the taught modules and joining it with practical experience. A list of suggested topics is provided, and includes projects proposed by staff and industry sponsors, associated with current research projects.  

It is clear that the modern design engineer cannot be divorced from the commercial world. In order to provide practice in this matter, a poster presentation and written report will be required from all students, and the research findings presented to the academic staff as well as the Industrial Advisory Panel members.

Modules

Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.


Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course.

Fundamentals of Road Vehicle Engineering

Module Leader
  • Dr Marko Tirovic
Aim

    This module aims to:

    • Provide students with an understanding of vehicle concepts and designs, including major systems, assemblies and components.
    • Enable students to establish approaches and procedures to analysing and predicting vehicle performance.
    • Enable students to critically evaluate the integration of different alternative powertrain options
       
Syllabus
    • Basic vehicle characteristics: Vehicle concepts, static and dynamic loads and weight distributions, front, rear and all-wheel drive.  Adhesion coefficient and influencing factors. Traction, braking and resistance to motion. 
    • Legislation: Introduction to regulations.
    • Internal Combustion Engines: Types and characteristics: torque, power and fuel consumption. Emissions. Drive Cycles.
    • Electric motors and drives: Types and characteristics: torque, power and efficiency.
    • Vehicle performance:  Maximum speed, hill start and climbing.  Fixed and variable gear ratios: number and distribution of gear ratios.  
    • Braking performance: Brake force distribution.  Calculation of required braking characteristics.  Brake and braking system designs and characteristics. 
    • Driveline: Manual & automatic transmissions.
    • Hybrid and electric vehicles: Basic definitions, HEV and EV architectures, advantages and disadvantages.  Electrical and mechanical energy storage technologies including battery management considerations. 
    • Vehicle as a complex system: Understanding conceptual and compatibility issues regarding vehicle structure, engine, transmission, suspension, packaging and influence on vehicle performance.  
       
Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Assess the advantages, disadvantages and limitations of various vehicle concepts and various vehicle, system and assembly designs using valid criteria.
  2. Propose quantified high-level powertrain designs to meet specified requirements with appropriate use and sizing of internal combustion engines, electric motors, batteries and transmission components.
  3. Contrast common hybrid vehicle architectures taking into account their performance characteristics and underpinning technologies.
  4. Estimate resistances to motion, assess powertrain system characteristics, and calculate fundamental vehicle performance (max. speed, acceleration, gradient, fuel economy, battery capacity / driving range, etc.). 
     

Path Planning, Autonomy and Decision Making

Module Leader
  • Professor Hyo-sang Shin
Aim
    In modern autonomous systems, it is essential to design an appropriate decision making algorithm and also path planning. Therefore, this module aims to deliver not only fundamental and critical understanding of classical and advanced decision making and path planning theories, but also evaluation of their nature, purposes, pros and cons, and characteristics. This should enable students and course delegates to critically select and design appropriate decision making and path planning algorithms for their specific autonomous systems.
Syllabus

    Course content includes:

    • Principles of decision making and path planning for autonomous vehicles;
    • Decision making approaches, including:
      • Approximation approach;
      • Heuristic approach;
    • Path planning approaches such as:
      • Graph-based approach;
      • Point guidance approach;
      • Path planning and following approach;
    • The key performance indices for verification and validation of decision making and path planning;
    • Application examples of task allocation and path planning algorithms.
Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Critically understand the requirements for autonomy;
  2. Appraise the key principles of path planning and examine their properties;
  3. Analyse the characteristics, purposes, and design procedures of task allocation and path planning, judge advantages and limitations;
  4. Evaluate challenging problems in the task allocation and path planning approaches for autonomous systems, assess current and propose alternative solutions;
  5. Judge appropriate task allocation and path planning algorithms given a specific decision making problem;
  6. Verify and validate the performance of decision making and path planning algorithms.
 

Sensors, Perception and Visualisation

Module Leader
  • Dr Luca Zanotti Fragonara
Aim
    The aim of this module is to provide an overview of sensors and sensor technology, various architectures, algorithms and applications in the context of autonomous vehicles perception and visualisation. The module aims also to give the students an understanding of the appropriate tools for error analysis and diagnostic statistics enabling them to critically evaluate the performance of a sensing technology in autonomous vehicles. 
Syllabus
    • Introduction to sensors, perception and visualisation for autonomous vehicles (1 lecture)
    • Autonomous vehicle sensors and imaging (4 lectures)
    • Sensor integration architectures and multiple sensor fusion (4 lectures)
    • Demo: indoor autonomous surveillance (3 lectures)
    • AI algorithms for sensing and imaging: supervised learning―neural networks (7 lectures)
    • AI algorithms for sensing and imaging: supervised learning―lab (3 lectures)
    • AI algorithms for sensing and imaging: unsupervised learning―clustering (3 lectures)
    • Case study: multiple sensor fusion (3 lectures)
     

Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Analyse the main practical applications of autonomous vehicle sensing and imaging, assess their advantages and limitations;
  2. Evaluate the main engineering challenges of autonomous vehicle sensor technology;
  3. Appraise qualitatively the functions and capabilities of the main sensor technologies used in autonomous vehicles, assessing current and further developments;
  4. Judge the performance of multi-sensor fusion architectures in a real-case scenario.
 

Systems Engineering

Aim
    This module aims to enable students to apply the principles of systems engineering to system problems.
Syllabus

    Topics covered by the course include:

    • Systems challenges
    • The systems process
    • Understanding systems
      • Capability need and requirements
      • System design and architecture
      • System evaluation, verification and validation
    • The impact of organisation on systems engineering
    • People, skills and competencies in systems engineering  
Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Illustrate common deficiencies in system design using case studies;
  2. Formulate approaches to system design based upon capability, behavioural principles and architectural considerations;
  3. Collect comprehensive system requirements from stakeholders formulate their flow-down into the systems design;
  4. Design verification strategies for proving the systems design;
  5. Organise projects to achieve a successful systems design.
 

Embedded Vehicle Control Systems

Module Leader
  • Dr Stefano Longo
Aim

    Within the context of modern automotive control system, the aim of this module is for students to critically evaluate the different technologies and methods required for the efficient vehicle implementation, validation and verification of the automotive mechatronic system.

Syllabus

    Course content includes:

    • A review of modern automotive control hardware requirements and architectures
    • The evaluation of current and future vehicle networking technologies including, CAN, LIN, MOST and Flex-ray
    • The evaluation of control rapid prototyping techniques to design and calibrate the control algorithm
    • The use of modern validation and verification methods, such as software-in-the-loop, and hardware-in-the-loop techniques
    • The role of Functional Safety and ISO26262 within the overall control system life-cycle
    • The evaluation of the interdependency between software engineering and control system design within the automotive industry including the use of software auto-coding techniques for production and the use of advanced test methods for the validation of safety-critical systems


Intended learning outcomes

On successful completion of this module a student should be able to:
1. Analyze the components of an automotive control systems and its implementation.
2. Design and implement a digital controller.
3. Evaluate the effect of sampling times, communication delays and quantization errors in a feedback loop.
4. Write efficient Matlab code for data coding/decoding and control algorithm implementation.
5. Interpret the purpose of the ISO26262 functional safety standard and the AUTOSAR standardized automotive software design.

Transport System Optimisation

Module Leader
  • Dr Abbas Fotouhi
Aim
    The aim of this module is for students to expand their knowledge in the field of transportation systems with a focus on road vehicles and traffic flow theory. In addition, this module includes general topics about optimisation techniques and their applications in transportation systems.

Syllabus
    • Modelling the motion of a single vehicle,
    • Effects of characteristics of vehicles, driver and environment on vehicle movement,
    • Modelling of vehicle interactions and movement of groups of vehicles,
    • Traffic operational performance characteristics and measures,
    • Traffic simulation techniques and tools,
    • Different road types including freeways, signalised and unsignalised intersections, networks and their principles,
    • An introduction to optimisation techniques,
    • Case studies of applying optimisation techniques in transportation systems.
     

Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Critically evaluate models of vehicle interactions and the movement of groups of vehicles;
  2. Analyse traffic flow performance characteristics and measures based on traffic operational analysis techniques;
  3. Appraise different road types including motorways, signalised and unsignalised intersections and networks, and compare their operational principles based on an established criteria;
  4. Formulate most appropriate optimisation techniques in transportation systems, discuss their benefits and limitations.
 

Human Factors, Human-Computer Interaction and Advanced Driver Assistance Systems (ADAS)

Module Leader
  • Dr Lisa Dorn
Aim

    To provide students with an understanding of human factors in human-computer interaction and ADAS systems.

Syllabus
    • Introduction to Human Factors
    • Human Performance: Perception and Attention 
    • Situation Awareness and Error 
    • Human Reliability: Driver Workload and Fatigue 
    • Emotion and Motivation in Design
    • Human-Computer Interaction and the User Experience
    • Transition Control from Manual to Autonomous: Human Capabilities and Attentional Factors 
    • Cognitive and Psychological Effects on Driver Distraction using Assistive Technologies: Behavioural Adaptation and Unintended Consequences
    • Trust in Autonomous Vehicles and Assistive Technology
    • Designing ADAS Systems with the Human in Mind 
    • The Systems Approach to Improving Safety
    • Driverless Vehicles and Ethical Dilemmas: Human Factors and Decision Making Software
    • Application of Human Factors in Autonomous Vehicles Research
     

Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Analyse and evaluate human limitations in perceptual and attentional capabilities in the driving context.
  2. Appraise the potential effects of ADAS technologies on human performance and error.
  3. Assess psychological responses to assistive technologies and automated vehicles, estimate their potential benefits and limitations.
  4. Rate human performance measurement methodologies in terms of their utility in the prediction and reduction of driving error.
  5. Create and appraise a demonstration of the use of human factors research to maximise user experience within the context of an automated vehicle.
 

Networked Systems and Cybersecurity

Module Leader
  • Dr Philip Nobles
Aim
    The aim of this module is to provide students with an understanding of the operation and vulnerabilities of communications and network systems within future connected vehicles, covering both on-board internal and inter-vehicle communication networks. 
Syllabus
    • History  of cyber against connected vehicles, e.g. examples of hacks (e.g. Jeep Cherokee)
    • Network principles, e.g. protocols, addressing, reliable communications (including time sensitive/critical i.e. databus principles, error detection/correction), routing, physical layer (e.g. rf range)
    • Vehicle databuses and information architectures, e.g CANBus
    • Security engineering, e.g. CIA, encryption, vulnerabilities, exploits, patching, reverse engineering
    • Principles of vehicle systems/security
      • Typical systems and how they are connected
    • Typical vulnerabilities in connected vehicle systems, e.g. attack surfaces, tools and techniques, example vulnerabilities and exploits (same examples as the start but in greater depth, plus any current at time of module delivery)
    • Demonstration - Hacking the Keeloq
    • Discussion - “Just another computer?” (how IT cybersecurity practices may be applied to vehicle cybersecurity)
    • Solutions, advances and future technologies
      • e.g. MilCAN, automotive Ethernet.
     

Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Analyse and evaluate the network components of vehicle system architectures and the methods used to secure them.
  2. Appraise and rate cyber vulnerabilities and how these impact on the security and/or safety of connected vehicles.
  3. Analyse the networks of a notional connected vehicle, rate potential attacks and judge most appropriate mitigations.

Ethics, Safety and Regulation

Module Leader
  • Dr Colin Pilbeam
Aim
    To provide experienced engineers working on connected and autonomous vehicles (CAV) with an understanding of safe working practices, and of governance and regulatory issues, and appreciation of the ethical dilemmas surrounding CAV to allow them to take up leadership roles in the industry. 
Syllabus
    • Review of different forms of ethics.
    • Assessment of ethical dilemmas and development of ethical reasoning skills.
    • Characterising the multi-level challenges (individual, organizational and societal) associated with assuring the safety of CAV.
    • International and national regulatory frameworks that underpin CAV and their safe operation.
    • Structure and processes of effective governance.
    • Design of safe operating systems and the human-technical interface.
    • Managing and leading paradox.
Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Analyse and critically evaluate the safety challenges associated with CAV
  2. Evaluate and rate the ethical implications of alternative automotive technologies
  3. Appraise the requirements of regulatory frameworks relevant to CAV, assess the trends and future developments 
  4. Formulate appropriate response to regulatory requirements
  5. Construct governance arrangements that ensure the safety of CAV.

Technology Strategy and Business Models

Aim
    The aim of this module is to provide students with a broad-based view of technology strategy and the role of business models in supporting the introduction and development of new technologies.
Syllabus
    • Research has shown that managers struggle with developing a clear technology strategy and lack the tools, techniques and understanding to achieve this. 
    • The nature of technology and how best to manage it is often unclear.
    • The challenge for managers is a broad one; they need to build an organisational culture that embraces new ideas, they must understand the potential opportunities of new technologies, they should select the most appropriate concepts for implementation successfully to the market.
    • The syllabus will cover:
    1. Introduction: content, style of teaching and learning; expectations of students and faculty; project teams; course assessment; reading; etc; 
    2. Understanding technology strategy. What is technology strategy and what role does it play in an organisation? The "dimensions" of technology strategy. The degrees of innovation in technology strategy: radical, disruptive, and incremental. How can innovation be managed? 
    3. Business models and how to create them; the role of business models in technology strategy. 
    4. Customer-focused approaches to technology strategy. Developing original approaches to solve the customer’s problems;
    5. Technology strategy based on innovation and imitation, who wins?
    6. A design approach to technology strategy.
    7. People and organisation: building the right culture to deliver a technology strategy; the importance of the learning organisation.
       
     

Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Analyse and critically evaluate the key ideas that underpin technology strategy and business models.
  2. Appraise, rate and evaluate the use and range of technology strategies within a particular technological context.
  3. Assess the extent to which a technology strategy is effectively supported by an organisation’s structure, processes and culture.
  4. Critically analyse the use and development of different business model frameworks.
  5. Appraise the use and development of specific business models used in technology strategy, judge the advantages and limitations.
 

How to apply

Online application form. UK students are normally expected to attend an interview and financial support is best discussed at this time. Overseas and EU students may be interviewed by telephone.