Abstract
In this study, we propose a new simple degree-of-freedom fluctuation model that accurately reproduces the probability density functions (PDFs) of human–bicycle balance motions as simply as possible. First, we measure the time series of the roll angular displacement and velocity of human–bicycle balance motions and construct their PDFs. Next, using these PDFs as training data, we identify the model parameters by means of particle swarm optimization; in particular, we minimize the Kolmogorov–Smirnov distance between the human PDFs from the participants and the PDFs simulated by our model. The resulting PDF fitnesses were over 98.7 % for all participants, indicating that our simulated PDFs were in close agreement with human PDFs. Furthermore, the Kolmogorov–Smirnov statistical hypothesis testing was applied to the resulting human–bicycle fluctuation model, showing that the measured time responses were much better supported by our model than the Gaussian distribution.
Highlights
Bicycles provide a useful means of short-distance transportation, and their utilization is expected to contribute to building a healthy and environmentally friendly society [1]
We propose a simple stochastic model that allows us to simulate the latter type of bicycle motion, i.e., involuntary fluctuated human–bicycle balance motions
We identified the model parameters based on the measured probability density functions (PDFs) as training data, using particle swarm optimization (PSO) to minimize the Kolmogorov–Smirnov (KS) distance between the measured PDFs and those simulated by our proposed human–bicycle model
Summary
Bicycles provide a useful means of short-distance transportation, and their utilization is expected to contribute to building a healthy and environmentally friendly society [1]. The Japan National Police Agency reported that over 83% of bicycle accidents in Japan in the last ten years have involved automobiles [2] To avoid such accidents, autonomous vehicle technology will play an important role; if it can predict bicycle motions, the resulting self-driving cars may reduce such accidents. Bicycle motions with human riders in traffic seem to be broadly classified into two types: voluntary and involuntary The former comprises purposeful motions such as right and left turning at a street intersection. Google [3] reported that their sensors can detect a cyclists’ hand signals as an indication of an intention to make a turn or shift over The latter comprises unconscious motions such as human fluctuated balance motions, which have already been found universally in human quiet standing [4,5,6], human stick balancing [7,8], human visuomotor tracking [9,10,11], and so on
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