Powered Two-Wheeled Vehicles Steering Behavior Study: Vision-Based Approach

International audienceThis paper presents a vision-based approach to prevent dangerous steering situations when riding a motorcycle in turn. In other words, the proposed algorithm is capable of detecting under, neutral or over-steering behavior using only a conventional camera and an inertial measurement unit. The inverse perspective mapping technique is used to reconstruct a bird-eye-view of the road image. Then, filters are applied to keep only the road markers which are, afterwards, approximated with the well-known clothoid model. That allows to predict the road geometry such that the curvature ahead of the motorcycle. Finally, from the predicted road curvature, the measures of the Euler angles and the vehicle speed, the proposed algorithm is able to characterize the steering behavior. To that end, we propose to estimate the steering ratio and we introduce new pertinent indicators such that the vehicle relative position dynamics to the road. The method is validated on the advanced simulator BikeSim during a steady turn

Two-wheeled vehicles black-box sideslip angle estimation

This paper deals with sideslip angle estimation of powered two-wheeled vehicles. Since available sensors used to directly measure this variable are bulky and expensive, estimation algorithms based on on-board measurements have been developed. These algorithms are mainly devoted to four-wheeled vehicles whereas the sideslip estimation for two-wheeled vehicles is still an open topic. This paper presents a Neural Network estimation algorithm that uses on-board standard measures available in modern motorbikes and studies the role of the most significant signals for the estimation. The employed black-box approach does not require the derivation of any physics-based model of the motorcycle dynamics and thus is meant as a valid tool for a preliminary insight in such estimation problem. The experimental data collected cover a rich amount of manoeuvres that are used to train the network and several other manoeuvres have been used to analyse its performances