WM997-15 Smart, Connected and Autonomous Vehicle Fundamentals
Introductory description
Autonomous/Automated Vehicles (AVs) aim to enhance and improve the safety and efficiency of future mobility.
This module introduces and explores key subjects related to AV development, including understanding the complexity of autonomous vehicle systems, human factors influencing the design of future AVs, analysis of key perception sensors, applications of machine learning, and the role of networks and communications in supporting AVs.
These topics are presented from both theoretical and practical perspectives, encouraging independent critical evaluation of the subject matter.
Module aims
Smart, Connected and Autonomous Vehicle Fundamentals aims to introduce the students to the key challenges associated to smart, connected and autonomous vehicles: SAE levels of autonomy and their implications on safety; robustness of embedded systems; environmental perception and data science; connectivity and communication infrastructures; test techniques for SCAVs; new mobility models and human factors. Learning is enhanced through tutorials and the understanding of unique experimental facilities such as 3xD (Drive-in Driver-in-the-loop Driving) simulator facility.
This module aims to provide the students with the critical knowledge associated with current and future technical challenges of smart, connected and autonomous vehicles and their importance for electrification.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
This module covers the topics are related to: system complexity in Automated Vehicles (AVs) Levels of Autonomy • AV testing • Supply chain and business models of mobility as service; Safety standards and analysis techniques; • trust, AV stakeholders, wellbeing and sensing the human • Perception Sensors • Wired(CAN, Lin,...) and wireless (LTE, 5G, DSRC)communication for AVs • Data science basis for machine intelligence and neural network
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate an in-depth knowledge of key principles underpinning human interaction and apply it to compare/criticise the design. [AHEP: M1, M3, M4, M5]
- Evaluate and compare the performance of different automotive perception sensors. [AHEP: M1, M2, M3]
- Demonstrate a critical high-level understanding of challenges associated with data science and machine learning techniques, in the context of safe Automated Vehicles. [AHEP: M1, M2, M3, M6]
- Demonstrate an applied knowledge of automotive system complexity and their testing. [AHEP: M1, M2, M6]
- Critically evaluate wired and wireless communication technologies in the SCAV context. [AHEP: M1, M3, M6]
- Collaborate as a team to apply acquired knowledge in critically evaluating and implementing technical choices in the design of autonomous vehicles (AVs). [AHEP: M1, M4, M5, M16, M17]
Indicative reading list
https://warwick.rl.talis.com/lists/E7D477CC-571D-F7D9-23E9-4206AD1CB56A.html
Subject specific skills
The student will gain many skills relates to autonomous vehicles. They will gain the knowledge on system complexity in AVs and their requirements, get experience and understanding of test techniques for AVS; apply that t understanding to create testing scenarios, also using dedicated simulation software; understand the importance of human factors and how they inform design; knowledge of supply chain in the automotive industry and business model of mobility as services; understand and evaluate different perception sensors; understand AV wireless and wired communications; understanding of data science basis for machine intelligence and the concept of neural network.
Transferable skills
Team work - work effectively in a group or team to achieve goals
Personal motivation, organisation and time management skills
Research and analytical skills
Project and program management skills,
The ability to gather and interpret information
Industry knowledge by guest lecturer
Study time
| Type | Required |
|---|---|
| Lectures | 20 sessions of 1 hour (13%) |
| Tutorials | 8 sessions of 1 hour (5%) |
| Supervised practical classes | 4 sessions of 1 hour (3%) |
| Online learning (independent) | 10 sessions of 1 hour (7%) |
| Other activity | 30 hours (20%) |
| Private study | 18 hours (12%) |
| Assessment | 60 hours (40%) |
| Total | 150 hours |
Private study description
In-depth reading around the subject
Other activity description
Guest Lectures or Group Activities , visit to 3XD Simulator;
15 Hours of student self-guided study to prepare for the IMAs. Guidance on topics to be studied is provided during lectures (with some extra contents on moodle) and IMA instructions.
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group A2
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Post Module assignment: Smart, Connected and Autonomous Vehicle Fundamentals | 80% | 48 hours | Yes (extension) |
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The Post-Module Assignment is designed based on the intended learning outcomes of the module. Students will be given questions related to the topics covered during the module, focusing on the key concepts and skills taught. |
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| In Module assessments | 20% | 12 hours | No |
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The Post-Module Assignment is designed to assess students based on the intended learning outcomes of the module, requiring them to demonstrate their design skills for smart, connected, and autonomous vehicles (SCAV) through group presentations. The group is expected to effectively utilize the specified self-study hours (12 hours) to prepare their SCAV design presentation. This comprehensive assessment approach ensures that the group is evaluated not only on their theoretical knowledge but also on their ability to communicate, justify, and apply their SCAV design choices in a professional and engaging manner. This assessment will use the peer evaluation process. |
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Feedback on assessment
IMAs: Feedback will be provided promptly, including comments on the submission along with the graded mark.
PMA: Written feedback will be given using the WMG feedback template. It will address each submitted answer/Learning outcome and include comments on presentation, structure, and grammar.
There is currently no information about the courses for which this module is core or optional.