Module code: ENGM298

Module Overview

An introduction to the technology and concepts that underpin the design of intelligent vehicles. The module begins with an introduction to intelligent vehicle technologies and their subsystems, such as sensors, perception, and localisation. It then proceeds to cover the design of intelligent vehicle decision-making systems, which includes mission planning, behavioral planning, and motion planning. The latter part of the course delves into the design and implementation of control and prediction systems for intelligent vehicles. The final lecture introduces advanced topics in intelligent vehicle design.

Module provider

Mechanical Engineering Sciences

Module Leader

FALLAH Saber (Mech Eng Sci)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 95

Lecture Hours: 11

Laboratory Hours: 11

Guided Learning: 11

Captured Content: 22

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

Indicative content includes: History of intelligent vehicles, Introduction to sensing, perception and localisation systems, Decision-making algorithms for mission, behavioural and motion planning systems, Principles of motion control for intelligent vehicles.

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Coursework 100

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate an understanding of principles, methodologies, mathematical modelling, coding and system design in the final assessment (coursework). The project coursework allows students to model intelligent vehicles, to design their driving systems and to analyse behaviour of intelligent vehicles. Thus, the summative assessment for this module consists of one coursework [ Learning outcomes 1, 2, 3, 4, 5]. Formative assessment and feedback:  formative verbal feedback is given in tutorials and lectures, written feedback is given on the coursework assessments including detailed comments on the coursework report. 

Module aims

  • Provide a general understanding of the concept of intelligent vehicles and their functionalities
  • Introduce the fundamental design of intelligent vehicles and develop skills to explain and critically evaluate and appropriately interpret their behavior.
  • Acquire first-hand skills in modelling and control of intelligent vehicles through the problem-based learning approach

Learning outcomes

Attributes Developed
001 Demonstrate a comprehensive understanding of principles and concepts relating to intelligent vehicles K M3
002 Identify and analyse the performance requirements of intelligent vehicle subsystems C M13
003 Recognise the need for models of intelligent vehicles in order to analyse their behaviour C M2
004 Apply mathematical and scientific models to control problems of intelligent vehicles CPT M1
005 Undertake a brief research topic and model an intelligent vehicle system to analyse and evaluate its performance PT M3

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy are designed to: introduce fundamentals of intelligent vehicles design through theory with practical examples. This is delivered principally through lecture classes and lab sessions. It concludes with a coursework project involving theoretical and practical procedures. The learning and teaching methods include pre-recorded lectures, captured contents, Q&A sessions, and PC lab sessions (in groups) and guided learning (recommended reading resources, optional reading resources and external online resources).

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: ENGM298

Other information

The School of Mechanical Engineering Sciences is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This moduleis designed to allow students to develop knowledge, skills, and capabilities in the following areas:

Digital Capabilities: In this module, students will learn about the cutting-edge digital technologies (hardware-sensors and software-algorithms) used in intelligent vehicle design. This module will equip students with skills in writing Python codes that enable intelligent vehicles to perceive the world, make decisions, and execute commands. students will also discover how AI and Data Science can turn the concept of intelligent vehicles into a reality. The course employs digital tools for learning and collaboration, including online learning platforms and simulation software for intelligent vehicle design.  


Employability: The module provides students an interactive and immersive learning approach. A key element of the course involves the analysis of real and adapted case studies, which will be used as the foundation for in-class discussions. This interactive approach extends further as students will also have numerous opportunities to participate in team exercises and activities. This is designed to enhance not only their theoretical knowledge, but also their teamwork and problem-solving skills in a collaborative environment.


Global and cultural capabilities: Upon successful completion of the module students will gain insights into international best practices, learning from the concepts, issues, and practices prevalent in other countries or cultural contexts. This is achieved through incorporating case studies reported in recent research papers from different countries and from different cultural backgrounds. This diverse set of perspectives provides students with a comprehensive view of global practices in intelligent vehicle design. The module prepares students for the global, multidisciplinary, and technology-driven nature of their future careers in intelligent vehicle design.


Resourcefulness and Resilience: This course on intelligent vehicle design is deeply rooted in student participation and continual improvement. From the outset, students are actively encouraged to take a problem-solving approach. Before introducing any solutions or methodologies, students are invited to share their thoughts, ideas, and potential ways to address the issues at hand. This practice not only develops students’ problem-solving abilities, but it also fosters an environment of collaborative learning and critical thinking.



Programmes this module appears in

Programme Semester Classification Qualifying conditions
Advanced Mechanical Engineering MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module
Mechanical Engineering MEng 2 Optional A weighted aggregate mark of 50% is required to pass the module
Automotive Engineering MEng 2 Optional A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.