Probabilistic Long-Term Vehicle Motion Prediction and Tracking in Large Environments

Abstract

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can easily be achieved by providing vehicles with a constant communication link to a control center and having the vehicles broadcast their position. The problem dramatically changes when vehicles operate within a large environment of potentially hundreds of square kilometers and in difficult terrain. This paper presents algorithms for long-term vehicle motion prediction and tracking based on a multiple-model approach. It incorporates a probabilistic vehicle model that includes the structure of the environment. The prediction algorithm evaluates the vehicle position using acceleration, speed, and timing profiles built for the particular environment and considers the probability that the vehicle will stop. A limited number of data collection points distributed around the field are used to update the vehicle position estimate when in communication range, and prediction is used at points in between. A particle filter is used to estimate the vehicle position using both positive and negative information (whether communication is possible) in the fusion stage. The algorithms presented are validated with experimental results using data collected from a large-scale mining operation.