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Using an AI App to Encourage Safer Driving in Non-Western Countries

Project Description

We have been developing smartphone apps which encourage safer driving. These apps use GPS to gather data on driving behaviour, analytics to identify patterns of unsafe driving, and natural language generation (NLG) to communicate this information to drivers in a manner which encourages people to change their behaviour (Braun et al 2018). Recently we have started looking at including other data sources (in addition to GPS), such as dashcam data and phone usage whilst driving.

All of this work has focused on driving in UK. We would like to explore applying our ideas in other countries, especially non-Western ones, where there are different rules and expectations about driving behaviour, and also in many cases considerably more traffic-related deaths (for example, according to WHO Thailand has 10 times more road-related deaths per capita than UK). We expect that the input data will be similar to the UK, but different analytics and NLG will be needed.

The student doing this project will of course need a good knowledge of computer science, as well as some knowledge of data analytics, AI, and NLP. He or she will also need a deep knowledge of the culture of the country he is focusing on. He or she will need to travel to this country to perform field work (eg, talk to drivers, driving instructors and police).

This project will investigate how agents operating as part of a group should learn behaviours that benefit the group as a whole. These behaviours are commonly referred to as norms, and describe obligations, permissions and prohibitions on actions in specific contexts.

Existing work on norm synthesis is primarily symbolic, and this project will investigate whether more complex norms can be synthesised than existing approaches, and how well deep reinforcement learning techniques perform to synthesise norms.

Candidates should have (or expect to achieve) a UK honours degree at 2.1 or above (or equivalent) in computing or related subject.

Candidates should be very familiar with a non-Western country with the ability to travel to this country for fieldwork.

A knowledge of Data analytics, AI and natural language processing would be beneficial.


• Apply for Degree of Doctor of Philosophy in Computing Science
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV
• Details of 2 academic referees

Informal inquiries can be made to Professor E Reiter ( with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ()

Funding Notes

This project is advertised in relation to the research areas of the discipline of Computing Science. The successful applicant will be expected to provide the funding for Tuition fees, living expenses and maintenance. Details of the cost of study can be found by visiting View Website. THERE IS NO FUNDING ATTACHED TO THESE PROJECTS. Applicants should also be aware that Additional Research will be required(above Tuition Fees and Living Expenses) for fieldwork totalling £8,000 over 3 years.


D Braun, E Reiter, A Siddharthan (2018). SaferDrive: An NLG-based behaviour change support systems for drivers. Natural Language Engineering 24:551-588. (

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