Predictive Cornering Light Control with Genetic Parameter Tuning
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Solution Overview
Problem
Current predictive dynamic cornering light systems require extensive time and resources for optimization, and their performance is influenced by varying driving conditions and individual driver styles, making it challenging to achieve optimal illumination adaptively.
Innovation Solution
A method involving a cornering light control unit, classification unit, and control parameter optimization unit that uses genetic algorithms and AI to automatically classify and optimize cornering light control parameters based on real-time data, allowing for continuous adaptation to individual driving conditions and styles without human supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional test drive methods are used to optimize cornering light control parameters, then optimization can be achieved, but it requires considerable time and resources including preparation and data analysis
Solution Approach 1:
The system performs self-optimization by automatically evaluating its own cornering light control performance using recorded driving data. The cornering light control unit analyzes its own output values against actual driving situations and autonomously adjusts control parameters without requiring external testing teams or manual intervention, thus eliminating time and resource costs while maintaining optimization quality
Solution Approach 2:
The system implements a feedback mechanism where the cornering light control unit continuously monitors the performance of its cornering light function by comparing output values with actual driving conditions. This feedback loop enables automatic identification of optimization opportunities and real-time parameter adjustment, replacing time-consuming manual test drives with instantaneous self-evaluation and self-correction
2Adaptability or versatility
If cornering light control parameters are optimized for general conditions, then initial functionality can be achieved, but performance cannot adapt to individual driving conditions and styles
Solution Approach 1:
The system records driving history parameter values and video data during normal operation to build a database of individual driving patterns and conditions. By preparing this data in advance, the system can later use it for targeted optimization of cornering light control parameters specific to each driver's style and typical driving situations, enabling personalization without adding operational complexity
Solution Approach 2:
The system dynamically adjusts cornering light control parameters based on detected driving patterns and conditions. By changing parameters such as light direction angles, switching thresholds, and illumination intensity based on individual driver behavior data, the system achieves high adaptability to different driving styles while maintaining the same underlying control architecture, thus avoiding increased system complexity
Data Source
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AI summary
A method (100) for automatically optimizing a predictive dynamic cornering light function of a vehicle lighting system comprises setting up (103) a cornering light control unit for controlling the lighting system, with initial cornering light control parameter values as the control parameter values to be used, setting up (104) a classification unit for automatically classifying a performance behavior of the cornering light control unit into a desired and at least one further performance behavior class depending on output values of the control unit, setting up (105) a control parameter optimization unit for determining updated control parameter values depending on input values of the control unit and the associated classifications of the performance behavior, as well as acquiring (106) driving profile parameter values as the input values during a journey, and determining (107) output values of the control unit.automatic classification (108) of the performance behavior depending on the determined output values, determination (109) of updated control parameter values and adaptation (110) of the control parameter values to be used to the updated control parameter values, wherein the determination (109) of updated control parameter values includes applying a genetic algorithm (113) with which a frequency of a classification of the performance behavior in the desired performance behavior class is increased.