Online map-matching assisted by object-based classification of driving scenario

Abstract

Different types of roads in complex road networks may run side-by-side or across in 2D or 3D spaces, which causes mismatched segments using existing online map-matching algorithms. A driving scenario that represents the driving environment can inform map-matching algorithms. Images from vehicle cameras contain extensive information about driving scenarios, such as surrounding key objects. This research utilized vehicle images and developed an object-based method to classify driving scenarios (Object-Based Driving-Scenario Classification: OBDSC) to calculate the probabilities of the current image in predefined types of driving scenarios. We implemented an online map-matching algorithm with the OBDSC method (OMM-OBDSC) to obtain optimal matching segments. The algorithm was tested on nine trajectories and OpenStreetMap data in Shanghai and compared with five benchmark algorithms in terms of the match rate, recall and accuracy. The OBDSC method is also applied to the benchmark algorithms to verify the effectiveness of map matching. The results show that our algorithm outperforms the benchmark algorithms with both the original interval and downsampled intervals (96.6%, 96.5%, 93.7% on average with 1–20 s intervals for the three metrics, respectively). The average match rate has improved by 8.9% for all benchmark algorithms after the addition of the OBDSC method.