Leveraging Smartphones for Vehicle Lane-Level Localization on Highways

When vehicle road-level localization cannot satisfy people's need for convenience and safety driving, lane-level localization becomes a corner stone in Intelligent Transportation System. Existing works of tracking vehicles on lane-level mostly depend on pre-deployed infrastructures and additional hardwares. In this paper, we utilize smartphones to sense driving conditions for vehicle lane-level localization on highways. By analyzing driving traces collected from real driving environments, we find that each type of lane-change has its unique pattern on the vehicle's lateral acceleration. Based on this observation, we propose a <italic>Lane-Level Localization</italic> (<inline-formula><tex-math notation="LaTeX">$L^3$</tex-math><alternatives> <inline-graphic xlink:href="yu-ieq1-2776286.gif"/></alternatives></inline-formula>) system, which can perform real-time vehicle localization on lane-level only using smartphones when vehicles are driving on highways. Our system first uses embedded sensors in smartphones to capture the patterns of lane-change behaviors. Then, a <italic>Finite State Machine </italic> is employed to track vehicles on lane-level leveraging the patterns. Extensive experiments demonstrate that <inline-formula><tex-math notation="LaTeX">$L^3$</tex-math><alternatives> <inline-graphic xlink:href="yu-ieq2-2776286.gif"/></alternatives></inline-formula> is accurate and robust in real driving environments. The experimental results show that, on average, <inline-formula><tex-math notation="LaTeX">$L^3$ </tex-math><alternatives><inline-graphic xlink:href="yu-ieq3-2776286.gif"/></alternatives></inline-formula> achieves the accuracy of 91.49 percent on lane change detection and 90.31 percent on lane-level localization.

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