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Driving events, such as maneuvers at slow speed and turns, are important fordurability assessments of vehicle components. By counting the number of drivingevents, one can estimate the fatigue damage caused by the same kind of events.Through knowledge of the distribution of driving events for a group ofcustomers, the vehicles producers can tailor the design, of vehicles, for thegroup. In this article, we propose an algorithm that can be applied on-board avehicle to online estimate the expected number of driving events occurring, andthus be used to estimate the distribution of driving events for a certain groupof customers. Since the driving events are not observed directly, the algorithmuses a hidden Markov model to extract the events. The parameters of the HMM areestimated using an online EM algorithm. The introduction of the online EM iscrucial for practical usage, on-board vehicles, due to that its complexity ofan iteration is fixed. Typically, the EM algorithm is used to find the, fixed,parameters that maximizes the likelihood. By introducing a fixed forgettingfactor in the online EM, an adaptive algorithm is acquired. This is importantin practice since the driving conditions changes over time and a single tripcan contain different road types such as city and highway, making theassumption of fixed parameters unrealistic. Finally, we also derive a method toonline compute the expected damage.

Online estimation of driving events and fatigue damage on vehicles