Monitoring High-Speed Road Surface Friction Using Vehicle Braking Sensors

This project will explore the potential for Highways England to complement routine monitoring of road surface skidding resistance (using slow-speed test vehicles), with assessments made by interpreting the outputs of vehicles braking at high-speed during day-to-day driving. If successfully implemented, this approach will improve road safety maintenance by providing an almost continuous source of friction data, at network level, from actual road users, in real driving conditions, as supplementary information to routine annual measurements.
Modern cars and trucks are now routinely equipped with many sensors, used in engine management, safety and active suspension and braking systems etc., along with positioning capability. When these sensor data are collected for many vehicles together, they can also provide a rich source of information for management of the highway infrastructure. At the Nottingham Transportation Engineering Centre we have demonstrated the feasibility of interpreting this vehicle sensor data for road engineering tasks, such as safety risk assessment, roughness measurement and for estimating the impact of road condition on fuel consumption, using machine learning data analysis techniques.
In this project, working with industry and academic partners, we will collect vehicle braking, wheel speed and position data from a fleet of modern cars in everyday use. The outputs will be validated using experimental measurements with an instrumented vehicle on test tracks. Advanced data analysis will be used to interpret the data to provide information about the skidding resistance condition of the road network. This will provide the evidence for recommendations to Highways England concerning the adoption of this approach in managing the highway asset in England.
This is an exciting PhD project that will provide academic challenges in vehicle and road engineering and advanced data analysis, while providing the opportunity to work with academics in engineering and computing and partners in the vehicle and civil engineering industries.
We are seeking talented candidates with:
• First or upper second class degree in engineering, computing, mathematics or similar discipline
• First rate analytical and numerical skills, with a well-rounded academic background
• Ability to undertake and analyse experimental data
• Ability to learn and undertake advanced data analysis techniques
• Ability to liaise with academic and industry partners
• Background with relevant software (e.g. R, MATLAB, GIS packages etc.)
• A driven, professional and self-dependent work attitude
• The ability to produce high quality presentations and written reports
This is an excellent opportunity to work with the engineering industry to develop a novel monitoring system using advanced analysis methods for big data, and to kick-start both an academic and industry track record.