Data Generation Through Motorcycle Crash Simulations For Airbag Trigger Algorithms


Crashing during motorcycling, in sport or recreation, is highly likely to cause injury. Advanced safety equipment can reduce injury and prevent death in the event of a crash. Currently REV'IT! is developing motorcyclist Personal Protective Equipment (PPE) in the form of an electronically triggered airbag worn by a rider. REV'IT! has a seat at the European body for standardization for motocyclist PPE (CEN/TC162/WG9) as well as the ad-hoc group 'Airbags'. The body is aiming to develop an EN standard for electronically triggered Airbags.

Such airbags can be broken down into various components: the inflatable, the canisters, the garment, the hardware (electronics), and the software. At this moment in time REV'IT! is / has been focusing on the bladder, garment, canisters and hardware, and although REV'IT! has been logging rider data in the MotoGP for a couple of years, it is lacking reliable crash data from non-extreme sports in order to develop an algorithm for crash detection that is able to detect and react to an evolving accident. Collecting reliable crash data highly depends on the amount of crashes recorded, the environmental influences, the sort of accident, etc.

In order to accelerate on the amount of crash data, REV'IT! and TUD would like to investigate if computer simulated accident scenarios can provide the necessary data to (initially) base an algorithm upon. Currently multi-body simulation software such as MADYMO is widely used for accident reconstruction and simulation.

The student will:

  • Research setup (possible and common crash scenario's – e.g. analysis results from European research projects RIDERSCAN, PIONEERS)
  • Determine, reconstruct and simulate crash scenarios
  • Collect crash data and compare between simuluation and MotoGP logged data
  • Benchmark simulated crash data to real-life and/or conditioned crashes (=crash tests)