Smart Mirror Image Customization With AI Scenario Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing smart mirrors in vehicles lack customization options and connectivity, with complex and inefficient training procedures for neural networks.
Innovation Solution
A method for driver assistant image customization using a data processing device that applies artificial intelligence to transform vehicle-related images based on user-specified customization schemes, employing generative adversarial networks (GANs) trained on a server for various adaptation modes, and integrating with human-machine interfaces and vehicle sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing smart mirrors use basic image display without customization, then device complexity is reduced, but adaptability and user personalization capability deteriorate
Solution Approach 1:
The system dynamically adjusts image parameters based on user-selected customization schemes. The image processing pipeline transitions from static basic display to dynamic adaptive processing, where transformation operations (sharpness, brightness, color, etc.) are applied in real-time according to user preferences and driving conditions.
Solution Approach 2:
The smart mirror system integrates multiple functions beyond basic reflection: it incorporates camera-based image capture, AI-driven image transformation with multiple customization schemes, connectivity features for remote configuration, and adaptive processing for different driving scenarios. This multi-functional integration enhances adaptability while managing complexity through unified system architecture.
2Measurement precision
If neural networks are trained locally in vehicles, then customization precision is improved, but training time and computational resources increase
Solution Approach 1:
The system performs image transformation preprocessing by applying multiple customization schemes (sharpness, brightness, color adjustments) before final display. This preliminary processing pipeline prepares images in advance with various transformation options, reducing the need for extensive real-time neural network training in the vehicle.
Solution Approach 2:
A server acts as an intermediary between data collection and local processing. The server receives images from vehicles, performs centralized neural network training with high computational resources, and distributes trained models back to vehicles. This mediator architecture enables high transformation quality without requiring lengthy local training periods.
3Adaptability or versatility
If multiple customization schemes are implemented, then user personalization is improved, but device complexity and processing requirements increase
Solution Approach 1:
The image processing system is segmented into multiple independent transformation modules, each handling a specific customization aspect (sharpness adjustment, brightness control, color modification, etc.). This segmentation allows the system to apply only the necessary transformations based on user selection, reducing overall processing complexity while maintaining comprehensive customization capability.
Solution Approach 2:
The system adjusts image parameters (sharpness, brightness, color saturation, contrast) through controlled transformations rather than complete reprocessing. By modifying individual parameters independently, the system achieves diverse customization effects with reduced computational complexity compared to full image regeneration for each customization scheme.
4Adaptability or versatility
If existing systems use fixed image processing, then processing speed is maintained, but adaptability to different scenarios deteriorates
Solution Approach 1:
The image processing system transitions from fixed static processing to dynamic adaptive processing. The system automatically selects and applies appropriate customization schemes based on detected driving conditions (day/night mode, weather conditions, driving scenario), enabling scenario adaptation while maintaining processing efficiency through pre-configured transformation pipelines.
Data Source
AI summary
A method for a driver assistant image customization of vehicle-related images, a data processing circuit, a computer program, a computer-readable medium, and a vehicle, can include, with at least one sensing device of the vehicle, obtaining an input image to be customized. With at least one human-machine-interface of the vehicle or connected thereto an input determining at least one customization scheme to be performed is received. Using at least one data processing circuit of the vehicle applying artificial intelligence the input image is transformed according to the at least one customization scheme into a transformed output image. With at least one smart mirror of the vehicle or a mobile device connected to the at least one data processing circuit the transformed output image is outputted. The at least one customization scheme includes a plurality of different types of adaptation modes.