Motion estimation by integrated low cost system (Vision and MEMS) for positioning of a scooter "vespa".

In the automobile sector, especially in these last decade, a growing number of investigations have taken into account electronic systems to check and correct the behaviour of drivers, increasing road safety. The possibility to identify with high accuracy the vehicle position in a cartographic reference frame for driving directions and best-route analysis is also another topic which attracts lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate for each time instant the position, orientation and velocity of the system. For this low cost GPS and MEMS sensors can be used. Comparing the dynamics of a four wheel vehicle to the dynamics of a two wheel vehicle, the latter has a higher degree of complexity, The degrees of freedom are more numerous, since the scooter can twist sideways and thus have a roll angle. Also a slight pitch angle which has to be considered because the wheel suspensions have a higher degree of movement in respect to four wheel vehicles. In this paper an accurate real-time reconstruction of the dynamics of “Vespa” scooter is presented. A Bayesian filter provides the means for integrating the data from MEMS. With the same method the acquisition of the roll angle with the vision algorithm proposed by Frezza and Vettore (2001) will permit a control and an assessment for increasing the accuracy of vehicle position.