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Intersection and Stop Bar Position Extraction From Vehicle Positioning Data
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DOI：10.1109/TITS.2020.3039357.png)
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Abstract
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Detailed road features like lane markers and stop bars are crucial for many recent Intelligent Transportation System (ITS) applications, especially for advanced driving assistant systems or autonomous vehicles. In this paper, a data-driven method is proposed to identify intersection areas and map stop bar positions without prior knowledge of road information. The proposed method includes 1) a novel and efficient approach to identify intersections by analyzing the entropy of vehicles' moving directions; and 2) a statistical model for estimating the number, coordinates, and directions of stop bars by evaluating the upstream vehicles' stopping locations. By applying the method to real-world vehicle positioning data collected at Ann Arbor, its applicability and robustness to handle data at an urban regional scale (a 1.2 km by 2 km rectangular area) are proven. The accuracy of intersection identification is 95.7% for trajectory covered regions. For stop bar positioning, the mean and standard deviation of the errors are 0.27 m and 0.32 m respectively, which satisfy most of the mobility and eco-driving connected and automated vehicle applications such as eco-approach and departure at signalized intersections.
Keywords:
Trajectory mining
lane-level road feature mapping
Gaussian mixture model (GMM)
entropy analysis
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