  Vehicle localization is essential for intelligent transportation. However,
achieving low-latency vehicle localization without sacrificing precision is
challenging. In this paper, we propose a road-aware localization mechanism in
heterogeneous networks (HetNet), where distinct features of HetNet signals are
extracted for two-spatial-scale position mapping, enabling low-latency
positioning with high precision. Specifically, we propose a sequence
segmentation method to extract the low-dimensional positioning space on two
spatial scales. To represent roads and sub-segments according to HetNet
signals, we propose a salient feature extraction method to eliminate redundant
features and retain distinct features, thereby reducing feature-matching
complexity and improving representation accuracy. Based on the extracted
salient features, a two-spatial-scale localization algorithm is designed
through salient feature matching, which can achieve low-latency road-aware
localization. Furthermore, high-precision positioning is achieved by coordinate
mapping based on curve fitting. Simulation results show that our mechanism can
provide a low-latency and high-precision positioning service compared to the
benchmark schemes.
