This Post Graduate Diploma specialises in the design and development of Advanced Driver Assistance Systems, the underlaying technology of smart and autonomous vehicles. This part-time programme brings together interdisciplinary concepts such as computer vision, artificial intelligence, vehicle dynamics and advanced sensor systems to provide current engineers with the skills required to design the next generation of automotive technology.
This NFQ Level 9, 60 ECTS Credits Post Graduate Diploma has been developed in collaboration with industry and is aimed at Electronic, Computer, Mechanical and Mechatronic Engineers who wish to develop the skills required to design the next generation of technology for smart and autonomous vehicles.
The programme is run over one year (Springboard places) and two years part time with 60 credits of taught modules primarily delivered online with some on-campus workshops.
Springboard+ 2021 Approved Course
A limited number of funded places are available on the Springboard+ 2021 programme. Applicants must apply directly to Springboard+ to avail of the places. Level 6 programmes are FREE to all successful applicants. Level 7, 8 and 9 programmes are 90% funded for those in employment and 100% funded for those on a qualifying social welfare payment. Full details on all eligibilty criteria are avaialble on the Springboard+ website www.springboardcourses.ie
Key Course Information
Live Lectures: Live lectures normally take place between 6pm and 10pm, Monday to Thursday but this may vary depending on the availability of specific lecturers. If the Live Classroom scheduled times for the live online lectures do not suit you, recordings will be made available through Moodle.
Study hours: Whether you are studying part-time online, blended or full-time online, it is very important that you allocate enough study time to your online course to stay focused, reduce stress and achieve your goals. For part-time online or blended learning, it is recommended that you should try to allow for 5-6 hours per week per 5 credit module to your studies.
Application Closing Date : 31st August 2022
Graduates with a Level 8 Honours Degree 2:1 or above in Electronic Engineering, Mechatronic Engineering, Mechanical Engineering, Computer Science or a related discipline are eligible to apply for this programme.
Programming knowledge (Ideally C++) and Level 8 Engineering Maths are pre-requisites to the course.
Applicants who do not meet these criteria but have the willingness to address them will be considered. Candidate interviews and entrance exams will be used to assess suitability for the programme.
In addition, international students, whose first language is not English, will be required to prove their English competency through previous examination results, recognized English language tests such as IELTS (6.5 or equivalent required) and through oral communication skills at interview.
Upon completion students will be eligible to attain a MEng in Connected and Autonomous Vehicles by completing an additional 30-Credit Research Dissertation in the Field of Connected and Autonomous Vehicles.
Students will find employment in Senior Design Positions in Electronic, Mechanical, Mechatronics and Embedded Systems engineering for highly regulated industries. Although primarily directed at the automotive sector, many of the skills such as Machine Learning, Pattern Detection and Computer Vision are highly sought after for R&D roles in other industries such as the medical, agricultural and high-volume manufacturing industries.
“Why would an actuary working in insurance undertake the Postgraduate Diploma in Connected and Autonomous Vehicles (C&AV)?”
John Caslin, who works for a leading Irish life assurance company highlights the three main transferrable skills gained through studying Connected and Autonomous Vehicles with Online & Flexible Learning at ATU Sligo.
John Caslin graduated as an engineer from Trinity College before qualifying as an actuary. Having acquired knowledge and skills in machine learning, John recognised the benefits of gaining a formal quantification. Here he outlines how skills in machine learning, vehicle data science, and cybersecurity are applicable in the insurance industry:
Firstly, autonomous vehicles such as self-driving cars use computer vision as one means of perceiving their environment. Machine learning interprets computer vision and data from other sensors to decide on safe courses of action. Computer vision and machine learning have significant applications in the insurance industry, in investment management, and in the automation of mundane tasks. This course provides probably the best coverage of these two subjects because of their safety critical applications in autonomous vehicles.
Secondly, motor insurers are at a strategic junction as the access to the data captured by a vehicle’s manufacturer gives it deep knowledge of the risk profile of the driver and hence a pricing edge over a traditional motor insurer. In September 2020, K.com reported that Tesla was taking steps towards
becoming a motor insurer in its own right. The Postgraduate Diploma in C&AV provides a deep understanding of that data set which could be used in underwriting motor insurance proposals.
And finally, the modern car has so many attack surfaces from a cybersecurity perspective that protecting it from cyber attacks is critical for safe driving. The Postgraduate Diploma in C&AV provides significant insights into the cybersecurity risks of motor vehicles which are critical for underwriting. At least one motor insurer in the Irish market has already excluded loss, damage, and liability for a cyber attack on insured private motor vehicles.
Per 5 Credit Module: €750
This programme has been submitted for Springboard funding for the academic year 2022/2023. We will know the outcome of this submission in May/June 2022. In the meantime you can register your interest with us on the link: Springboard Register Your Interest - Institute of Technology Sligo (itsligo.ie)
Further information on Springboard funding can be found at www.springboardcourses.ie
|Applied Linear Algebra||05|
|ADAS and Autonomous System Architecture||05|
|Vehicle Dynamics and Control||05|
|Multiple View Geometry in Computer Vision||05|
|Automotive System Safety & Cybersecurity||05|
|Applied Statistics and Probability||05|
|Modelling, Simulation and Test Methods for Advanced Driver Assistance Systems||05|