AR-HUD Light Intensity Adaptive Control Based on Fuzzy Neural Network

The light environment along the heavy-haul railway is complex and changeable, and the Augmented Reality Head-up Display (AR-HUD) of the heavy-haul train will be seriously interfered, which will reduce the efficiency of the driver's virtual image information acquisition, and further affect the driving safety. To solve this problem, a Method of model building is proposed, which is AR-HUD image generating unit (PGU) adaptive control of light intensity model based on fuzzy neural network (FNN). The precise control of PGU projection light intensity ensures the clarity of virtual image display information of AR-HUD. Firstly, the relationship between the AR-HUD display clarity, and the input and output data of the light intensity control module based on empirical knowledge is researched and analyzed. Then the accurate control model based on data-driven and FNN is established, then the adaptive output of PGU projection for AR-HUD system facing complex and varied light environment is studied and verified by experiment. The experimental data show that the model has strong adaptability to varied light environments, and its root-mean-square error is 0.002311. It effectively solves the interference caused by complex light environments to the clarity of AR-HUD display information.