An Automotive Headlight Leveling Control Strategy with Rough Road Identification

The Automotive Headlamp Leveling System (ALS), aims to ensure the stability of the illumination range of the headlamp by vertically rotate the headlamp using motors when the body attitude changes. This paper proposes an ALS scheme which obtains the body attitude from body height sensors and accordingly calculates the output angles of left headlamp and right headlamp respectively based on Euler transformation. Different from existing ALS, this scheme uses several strategies to improve the performance of ALS, including anti-shake function, working mode division and rough road identification. The purpose of rough road identification is to avoid unexpected vibration of headlamp caused by ALS on rough road and decision tree model is chosen for identification to improve the accuracy. Moreover, several field tests are performed to verify the validity and stability of the system.