Sensor fusion approaches to guideway and obstacle detection in the autotaxi system

The Autotaxi system is a safety critical sensor system that is being specially developed to perform the sensing required for an autonomous vehicle to drive safely along a dedicated paved guideway network and to avoid collision. Therefore, the host vehicle is equipped with a set of sensors used to detect and track any object of interest in the field of view and to describe the guideway in which the vehicle is driving. In this work, a decentralized architecture referred to as sequential pair wise track-to-track fusion is proposed to solve the multiple-sensor multiple-target tracking data fusion problem under the context of the Autotaxi system. The approach consists of four basic stages: data alignment, redundancy elimination logic, Kalman filtering with resetting and track-to-track association and fusion. A coefficients based fusion approach is proposed to give solution to the multiple sensor guideway data fusion problem. Results from the latest test trials carried out at the Cardiff test track are presented.