F1 Driving

Laps with alpha frequency

Race driving is a high value and competitive area where the combined performance of highly skilled drivers and sophisticated machines are challenged to the extremities. Identifying good driving practice and skills can be beneficial and cost effective in many ways. The research concerns identifying potential world champions at early stage, tailored training that adapts to vehicle’s performance, etc. The novelty of this work comes from fusing brain activity (e.g. cognitive workload) and contextual information. As driving is a free-form and highly dynamic activity it is hard to interpret human responses in a granular detail without developing a time-phased contextual understanding of the scene. Experience from emotive CAD research, and by employing UbiITS framework, I set up experiments to capture and fuse brain activity and car telemetry. I developed bespoke signal processing algorithms able to reveal the presence of strong visually recognisable patterns led on to building up a robust statistical model. This research is still progressing, possibly moving towards real-world, real-car driving with real-time access to onboard diagnostic data. The work had been demonstrated to Ross Brawn, (Mercedes F1).

Laps with beta frequency