Enhanced Lane Tracking Algorithm Using Ego-Motion Estimator for Fail-Safe Operation

For automated driving technology to move beyond the proof stage and into actual automated driving, it is important to verify the safety of the automated driving system. In this paper, a method to ensure lateral control safety for the lane detection function is proposed to address a failure of the image-sensor-based automated driving system, which is the system with the highest possibility for practical mass production. The proposed algorithm consists of three parts: the first part is the ego-motion estimator that estimates the movement of a vehicle; the second is an integrated lane detection sensor module, which response to failure by estimating lane information at the corresponding time; the last is a lane estimator, which tracks lane coefficients based on constant lane widths. A combination of these three modules and the prediction of the lane coefficient, C3, in the virtual sensor, which was not reflected in our previous study, enables a more robust response to lane detection failure. The performance difference between the proposed algorithm and an existing algorithm was confirmed by simulated evaluations for identical situations based on actual data. Through this result, it was confirmed that not only could near-lane estimation accuracy but also lane estimation accuracy at a far distance could be improved when compared to the existing algorithm. The results of this study are expected to help obtain lateral safety during minimal risk maneuver. The proposed algorithm may also contribute to ensuring the safety of image-sensor-based automated driving systems.