Vehicle Headlamp Switching Using Ambient Light and Lamp Detection
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Solution Overview
Problem
Existing intelligent light switching methods for vehicles fail to accurately distinguish between high and low beams due to high camera module requirements, brightness calculation errors, and inaccurate lane line detection, leading to potential blinding of oncoming drivers.
Innovation Solution
An intelligent light switching method that combines ambient light brightness calculation and lamp source classification using a trained lamp source detection model, involving image processing to determine the vanishing point line and classify lamp sources accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If manual switching of lamps is used, then energy consumption can be controlled, but user convenience deteriorates due to frequent manual operations
Solution Approach 1:
The system enables automatic lamp switching by detecting user presence through image processing. The control unit automatically turns lamps on or off based on whether a person is detected in the monitoring area, eliminating the need for manual operation while ensuring energy efficiency. This self-service approach resolves the contradiction by making the system both convenient (automatic) and energy-efficient (on-demand).
Solution Approach 2:
The system continuously monitors the environment using imaging devices and provides feedback to the control unit. When a person is detected, the system responds by turning on the lamp; when no person is present, it turns off the lamp. This closed-loop feedback mechanism enables automatic adaptation to user needs, improving convenience while maintaining energy efficiency.
2Illumination intensity
If multiple lamps are switched on simultaneously, then illumination intensity improves, but energy consumption increases
Solution Approach 1:
The system divides the monitoring area into multiple regions and selectively activates only the lamps corresponding to areas where persons are detected. Instead of uniformly illuminating the entire space, it provides localized illumination where needed, thereby maintaining adequate illumination intensity in occupied zones while minimizing energy consumption in unoccupied areas.
Solution Approach 2:
The system applies partial action by switching on only the necessary subset of lamps rather than all lamps simultaneously. Based on the number and position of detected persons, it activates the minimum required number of lamps to provide sufficient illumination, avoiding excessive energy consumption while maintaining adequate lighting levels.
3Ease of operation
If lamp switching is automated based on person detection, then user convenience improves, but system complexity increases
Solution Approach 1:
The control unit serves multiple functions: it receives control signals, processes images from multiple imaging devices, determines person presence, and controls multiple lamps. By making the control unit multi-functional, the system achieves automated convenience without proportionally increasing overall system complexity, as one component performs multiple roles.
Solution Approach 2:
The system combines multiple imaging devices and control functions into a unified automated control system. By merging the detection and control functions into an integrated system managed by a single control unit, it achieves automation and user convenience while managing complexity through consolidation rather than proliferation of separate components.
Data Source
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AI summary
This application provides an intelligent light switching method and system, and a related device. The method includes: The intelligent light switching system obtains an image, where the image is shot by a video camera disposed at a fixed position of a vehicle, and lamp source information is recorded in the image; calculates an ambient light brightness value corresponding to the image; classifies, based on the lamp source information, a lamp source included in the image to obtain a classification result; and switches to a high beam or a low beam based on the ambient light brightness value corresponding to the image and the classification result. In the foregoing method, two types of information, namely, ambient light information and lamp source information are combined, so that more information related to a road at night may be used for reference during determining of whether to switch to a high beam or a low beam, thereby improving accuracy of light switching.