Adaptive Vehicle Beam Pattern Control for Driver Tendencies
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Solution Overview
Problem
In the trend of vehicle electrification, the increasing number of customizable components and functions in user setting modes for drivers makes it difficult to operate, and existing systems lack a driver-centric approach to optimize beam patterns based on individual driving tendencies.
Innovation Solution
An apparatus and method that analyze a driver's tendency using machine learning models and sensors to adjust beam patterns automatically, including controlling shadow zone areas and recovery times, without requiring direct driver input in user setting modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of customizable components and functions in user setting mode increases, then the adaptability to driver preferences improves, but the ease of operation deteriorates
Solution Approach 1:
The system automatically analyzes driving information and adjusts beam patterns without requiring manual driver input. The driver-centric machine learning model performs self-service by autonomously customizing lighting settings based on observed driving tendencies, eliminating the need for drivers to navigate complex setting menus while maintaining high adaptability.
Solution Approach 2:
The system performs preliminary analysis of driving information and pre-adjusts beam patterns before the driver would need to manually configure them. By proactively analyzing driving tendencies and preparing optimal beam patterns in advance, the system resolves the contradiction between offering many customization options and maintaining ease of operation.
2Illumination intensity
If beam pattern adjustment parameters are increased for better visibility, then the illumination intensity improves, but the glare to other parties increases
Solution Approach 1:
The system applies different quality settings to different regions of the beam pattern. By analyzing driving information and determining appropriate shadow zone areas and recovery times for specific regions, the system provides high illumination intensity where needed while maintaining low glare in areas affecting other road users, resolving the contradiction between visibility and glare reduction.
Solution Approach 2:
The system dynamically adjusts beam pattern parameters including shadow zone area and recovery time based on real-time driving information analysis. This dynamic adaptation allows the system to optimize illumination intensity and glare reduction according to current driving conditions, driver tendencies, and environmental factors, simultaneously achieving improved visibility and reduced glare.
3Object-affected harmful factors
If the shadow zone area is expanded to reduce glare, then the glare to other parties decreases, but the visibility for the driver is reduced
Solution Approach 1:
The system dynamically adjusts the shadow zone area based on analyzed driving tendencies and current driving conditions. For drivers with conservative tendencies, the system expands the shadow zone to reduce glare, while for aggressive drivers or in specific situations, it reduces the shadow zone to maintain visibility. This dynamic adjustment resolves the contradiction by adapting the shadow zone size to specific driver behaviors and contexts.
Solution Approach 2:
The system changes multiple beam pattern parameters simultaneously including shadow zone area, recovery time, and illumination distribution. By coordinating changes across these parameters rather than adjusting shadow zone area in isolation, the system maintains driver visibility while reducing glare to other parties, resolving the contradiction through multi-parameter optimization.
4Object-affected harmful factors
If the recovery time is extended to prevent glare, then the glare to other parties decreases, but the response time of the beam pattern is reduced
Solution Approach 1:
The system dynamically adjusts recovery time based on driving information analysis and detected driver tendencies. The machine learning model identifies situations where extended recovery time is necessary to prevent glare versus situations where faster response is prioritized, allowing the system to optimize the balance between glare prevention and response speed according to specific driving contexts.
Solution Approach 2:
The system coordinates changes in recovery time with adjustments in other beam pattern parameters such as shadow zone area and illumination intensity. By making coordinated parameter changes, the system compensates for the reduced response speed caused by extended recovery time, maintaining overall beam pattern effectiveness while preventing glare to other parties.
Data Source
AI summary
Disclosed are an apparatus and method for controlling a beam pattern. The apparatus includes an output device that outputs a beam pattern, and a controller that analyzes a driving tendency of a driver and adjusts the beam pattern based on the analyzed driving tendency of the driver.