Discriminating subsequent lane-crossing and driver-correction events using trajectory models of lateral slopes

In this paper, we propose a new framework to discriminate the initial maneuver of lane-crossing event from driver-correction event, which is the primary reason for false warnings of the lane departure prediction systems. The proposed algorithm validates the beginning episode of the trajectory of driving signals whether it will cause a lane crossing event or not, by employing driver behavior models of directional sequence of piecewise lateral slopes (DSPLS) representing lane-crossing and driver-correction events. The framework utilizes only common driving signals, and allows adaptation scheme of driver behavior models to better represent individual driving characteristics. The experimental evaluation shows that the proposed DSPLS has detection error as low as 17% equal error rate. Furthermore, the proposed algorithm reduces the false alarm rate of the original lane departure prediction system from 38.8% to 6.1% with less trade-off for the prediction accuracy.