Context Awareness Assisted Integration System for Land Vehicles

Abstract

Accurate context awareness of land vehicles can assist integrated navigation systems. Motion behavior recognition is one context awareness of vehicles, based on which constraint information helps reduce the impact of short-term blockage of navigation signals on radio-frequency-based positioning systems. To improve the reliability of behavior recognition, we proposed a machine learning-based vehicle motion behavior recognition and constraint method (MLMRC). The machine learning-based recognition process is directly driven by raw data from low-cost MEMS-IMU, while the traditional threshold-based method relies on previous experience. Four categories of constraint information—sensor error calibration, velocity constraint, angle constraint, and position constraint—were constructed from the recognition results. Both the simulated vehicle experiment and real vehicle experiment demonstrate the performance of the MLMRC method. When there is a short-term blockage, the MLMRC method can reduce the positioning error from 17.2% to 38.3% compared with the traditional method, which effectively improves positioning accuracy and provides support for autonomous vehicles in complex urban environments.