A real-time map matching method for road network using driving scenario classification

Real-time map matching plays a critical role in intelligent transportation and autonomous driving. For complex road networks like elevated roads and overpasses, existing real-time matching algorithms have relatively lower accuracy due to the interference of parallel roads. Thus, a real-time map matching method combined with driving image classification is proposed. When the vehicle nears the elevated roads, the current trajectory point is matched by combining the scenario classification result with the vehicle's heading direction, the distance to the road segment, and the adjacency with the previous matching segment. For the experiment, three trajectories with high GNSS sampling rates were collected in Shanghai. Three indicators (match rate, recall, and precision) are used to evaluate the matching performance. The results show that the average matching rate, recall, and precision of the proposed method are 96.86%, 97.17%, 93.46%, which outperform the traditional real-time matching methods. As the sampling interval increases, the proposed method still performs well with three indicators. Comparing the matching results in complex areas such as elevated roads and intersections, as well as comparing the matching time, latency and memory consumption, this method can maintain good matching results.