Adapting Road Infrastructure for Autonomous Vehicles (EPSRC DTP)


Contact Dr Yue Huang ( to discuss this project further informally. Other supervisors will be Dr Phillip Wheat and Dr Judith Wang (School of Civil Engineering)

Project description

Autonomous vehicles are emerging as a solution to human driver limitations, such as imprecise control of speed and lane position. They also provide opportunities for more efficient use of the road space reducing congestion. This new vehicle technology however, comes with new and different challenges to road design and management, travel demand forecast and safety. 

At present, most work is focused on changes to the vehicles. For example, manufacturers are focusing on making future cars for existing roads, and little work has been done on how the roads should adapt to new vehicles. The shared mobility concept, as part of the benefits of having autonomous vehicles, will change the way we predict traffic growth, but would we need more lanes when vehicle overtaking may become unnecessary? 

Likewise, many fundamental principles of highway design developed in the 20th century were based on human factors, such as brake reaction time. The assumptions that highway engineers made in the past to derive design standards for road geometry, pavement, etc., need to be revisited. 

Signal control and high friction pavement are commonly used at junctions, for safety and capacity enhancement, but what demand for infrastructures like these will be when autonomous vehicles might interlace efficiently at these critical locations? Speed limits can potentially increase to reflect higher safety standards of future cars, but the social (e.g. fuel cost) and environmental (e.g. noise, emissions) implications need to be considered. Being almost a permanent asset, the road’s whole life maintenance needs must be evaluated, as revenues from emission-based taxes (e.g. fuel duty, vehicle excise duty) are projected to decrease. 

There will be a lengthy transition period where the roads are shared by conventional and autonomous vehicles. Future-proofing design, with minimal cost and disruption, is needed for a smart, flexible and dynamic road infrastructure. Good adaptation of roads will in return, encourage the uptake of autonomous vehicles and the roll-out of supporting infrastructure.

Candidate will look into current design standards for roads and junctions, such as the Design Manual for Roads and Bridges (DMRB), and investigate the implications of having autonomous vehicles for road infrastructure. The design principles of autonomous vehicles in relation to road performance and demand for enabling infrastructure (e.g. 5G) will be studied.

The project expects to generate evidence-based recommendations to amendments of current road design standards. In the likely case of carrying out a location-specific case study, candidate will need local knowledge and support with primary data. Survey, in person or on-line, is a common way of obtaining primary data. 

The project will focus on passenger vehicles, but results should be interested by other road users, such as public transport and freight. 

Entry requirements

You must have achieved a bachelor degree with a 2:1 (hons) or equivalent, or a good performance in a Masters level course preferably in a quantitative discipline. We also recognise relevant industrial and academic experience

If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.

How to apply

Formal applications for research degree study should be made online through the university's website.

If you require any further information, please contact the Graduate School Office e:, or t: +44 (0)113 343 35326.

We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.