A two-wheeled vehicle follow-up steering headlamp system based on six-axis attitude prediction

By combining six-axis attitude prediction and a temporal neural network model, the forward-looking deflection of the headlight axis of the two-wheeled vehicle was achieved, which solved the delay problem of the adaptive headlight system, ensured sufficient illumination of the inner path of the curve, and improved the safety of night driving.

CN122300636APending Publication Date: 2026-06-30TAIZHOU SUCHUANG TECH CO LTD
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Patent Information

Application Number
CN202610640449.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-11
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing adaptive headlight systems suffer from delays in sensor signal transmission, controller calculations, and motor execution, causing the light spot movement to lag behind the dynamic changes in the vehicle body, thus failing to completely eliminate blind spots when entering corners at high speeds.

Method used

By employing a multi-source perception module, a vehicle controller, and an optical axis execution module, and through a six-axis attitude prediction and a temporal neural network model, the future attitude of the vehicle is predicted in real time and the headlight optical axis is driven to deflect in advance, thus constructing a forward-looking active prediction mechanism.

Benefits of technology

Before the vehicle enters the curve, the headlight beam has already covered the potentially dangerous area, eliminating blind spots and improving nighttime driving safety.

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Abstract

This application discloses a two-wheeled vehicle adaptive headlight system based on six-axis attitude prediction. The system includes: a multi-source sensing module for collecting multi-source sensor data, including real-time six-axis attitude data and wheel speed data of the two-wheeled vehicle; a vehicle controller for generating a core motion state vector based on the multi-source sensor data through a dimension-reduced extended Kalman filter, and concatenating the core motion state vector with the six-axis attitude data to construct a temporal feature tensor; predicting the target attitude parameters corresponding to the target time slice based on the temporal feature tensor using a preset temporal neural network model; calculating the target optical axis deflection angle based on the target attitude parameters; and an optical axis execution module for driving the headlight optical axis to deflect to the target optical axis deflection angle. This application eliminates physical delay through feedforward prediction, actively compensates for body roll before the vehicle enters a curve, and covers blind spots, improving nighttime driving safety.
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