HMM Based Autarkic Reconstruction of Motorcycle Behavior from Low-Cost Inertial Measurements
Based on autarkic data from a low-cost, 6-axes inertial measurement unit (IMU), which is fixed onto and power-supplied by the motorcycles battery, we reconstruct forward velocity and elementary driving behavior of a motorcycle using a Hidden Markov Model (HMM). The notorious drift problem of integrated IMU data is mastered by using the voltage fluctuations of the motorcycle’s battery as a stabilizing external signal. Despite the structural simplicity of the algorithm and the relatively low performance of the IMU, the proposed off-line estimator is, after a short learning phase, accurate for a large class of motorcycles.
Unable to display preview. Download preview PDF.
- Palmer, S., Black-Box technology and its implications to the Auto-Insurance Industry, Communication of the Injury Sciences LLC, 2002.Google Scholar
- Waegli, A. et al., Accurate Trajectory and Orientation of a Motorcycle derived from low-cost Satellite and Inertial Measurement Systems, Proceedings of 7th ISEA CONFERENCE Biarritz, 2008.Google Scholar
- Kumagai, T. et al., Prediction of driving behavior through probabilistic inference. Proceedings of the Eight International Conference on Engineering Applications of Neural Networks, 2003.Google Scholar
- Cossalter, V., Motorcycle Dynamics, LULU press, Italy, 2006.Google Scholar