Data-Driven Indoor Positioning Correction for Infrastructure-Enabled Autonomous Driving Systems: A Lifelong Framework

Abstract

Infrastructure-enabled autonomous driving systems have been increasingly applied in confined environments. Automated valet parking (AVP) in smart parking garages is one of the notable applications that require high-precision indoor positioning services. However, the performances of the existing wireless indoor positioning techniques, including Wi-Fi, Bluetooth, and ultra-wideband (UWB), tend to decline substantially as the working time increases and the building environment varies. In this paper, we propose a lifelong framework using crowdsourced data of fully instrumented autonomous vehicles(e.g. vehicles equipping with LiDAR), to maintain the availability and precision of indoor positioning systems. We establish a map-aided deep learning positioning correction model based on continuous data sequences, which utilizes convolution and long short-term memory (LSTM) modules to extract the spatial and temporal features of positioning errors. A local grid map generator is designed and embedded into the correction model to learn the influencing factors of errors from the building environment and facility. A deep-learning-based anomaly detector is designed to keep the lifelong stability of our framework. Based on the proposed method, we develop a lifelong UWB positioning correction system and apply it for the path tracking of AVP in a real underground parking garage. The test results show that the system can maintain positioning correction precision in the environment of varying sensor errors and reduce the positioning error by 60% and the tracking error by 40%. The study showcases an innovative infrastructure-enabled application that can accelerate the widespread use of autonomous driving systems.