Previous real-time map matching algorithms for in-vehicle navigation systems had some efficiencies and defects on time lagging and low accuracy. As a response, this paper proposes a new algorithm that integrates STP (spatio-temporal proximity) and IWC (improved weighted circle), in which the new algorithm proposes STP to dynamically refine candidate matching roads, and IWC to adaptively identify the optimal matching road. Specifically, three spatio-temporal proximity indicators are defined in STP to build a three-dimensional stereoscopic cone, and then the two-dimensional projection of the cone are adopted to dynamically select the candidate matching roads. Further, by adaptively setting the weight, the IWC algorithm is developed to integrate three new parameters to adaptively determine the optimal matching road. The test results show that the matching accuracy of the algorithm is over 95%, much higher than that of the existing algorithm, which demonstrates the feasibility and efficiency of the new algorithm.

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Previous real-time lane-level positioning algorithms for in-vehicle navigation systems have problems of inaccurate positioning and insufficient robustness. Therefore, this paper proposes a new lanelevel positioning algorithm integrating Improved filter method, Curve Circle method and Improved Unet (ICCIU) method. Specifically, in ICCIU algorithm, we design improved filter method to improve the accuracy of original position by combining three filters to enhance image features, propose curve circle method to achieve real-time curve positioning by introducing two movement indicators to get precise curve position and design a light improved Unet method that integrates residual thought and cascading thought to detect the lane, and integrates two new parameters to get more accurate position by reducing the horizontal position errors The experiment results show that evaluation indicators of the improved Unet method are over 20% higher than those of the existing algorithms, the running time of single point positioning is about 28ms and lane-level accuracy is over 96% used by ICCIU, which demonstrates the pretty performance both in feasibility and efficiency of the new algorithm. INDEX TERMS Lane-level positioning, improved filter method, curve circle, improved Unet, ICCIU.

Autonomous localization without local wireless facilities is proven as an efficient way for realizing location-based services in complex urban environments. The precision of the current map-matching algorithms is subject to the poor ability of integrated sensor-based trajectory estimation and the efficient combination of pedestrian motion information and the pedestrian indoor network. This paper proposes an autonomous multi-floor localization framework based on smartphone-integrated sensors and pedestrian network matching (ML-ISNM). A robust data and model dual-driven pedestrian trajectory estimator is proposed for accurate integrated sensor-based positioning under different handheld modes and disturbed environments. A bi-directional long short-term memory (Bi-LSTM) network is further applied for floor identification using extracted environmental features and pedestrian motion features, and further combined with the indoor network matching algorithm for acquiring accurate location and floor observations. In the multi-source fusion procedure, an error ellipse-enhanced unscented Kalman filter is developed for the intelligent combination of a trajectory estimator, human motion constraints, and the extracted pedestrian network. Comprehensive experiments indicate that the presented ML-ISNM achieves autonomous and accurate multi-floor positioning performance in complex and large-scale urban buildings. The final evaluated average localization error was lower than 1.13 m without the assistance of wireless facilities or a navigation database.