Road Surface-Aware Content Generation Using Machine Learning
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Solution Overview
Problem
Existing content generation systems fail to incorporate road surface data, which limits the reflection of real-world road conditions in generated content.
Innovation Solution
A content generation device and method that acquires road surface data through observation, analyzes it using a machine-learned model, and generates content reflecting the road surface conditions, health-related data, and map data to present relevant information to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple map data is used for content generation, then the system is easy to operate and quick to generate content, but the generated content cannot reflect real road surface conditions
Solution Approach 1:
The patent introduces road surface observation devices as intermediaries between the real road surface and the content generation system. These devices (cameras, sensors, LiDAR) capture road surface state data and transmit it to the content generation device, enabling accurate reflection of road conditions without requiring direct complex interaction with the physical road surface itself
Solution Approach 2:
The patent replaces direct mechanical interaction with road surfaces through automated observation devices and machine learning models. Instead of physically analyzing road surfaces through complex mechanical means, the system uses cameras and sensors to capture data, which is then processed by ML models to generate content, simplifying the overall system while improving accuracy
2Measurement precision
If road surface observation devices are added to capture accurate road surface data, then the measurement precision of road surface conditions improves, but the device complexity and cost increase
Solution Approach 1:
The patent makes the road surface observation devices multi-functional by using them for both content generation and navigation assistance. The same camera and sensor data used to generate accurate content also feeds into navigation systems, allowing one set of devices to serve multiple purposes and justify the added complexity through enhanced versatility
Solution Approach 2:
The patent implements feedback mechanisms where road surface observation data is continuously fed back into the content generation process and navigation system. This continuous feedback loop allows the system to adapt to changing road conditions in real-time, improving measurement precision while managing complexity through automated iterative processing
3Measurement precision
If machine learning models are used to process road surface data, then the content generation accuracy and relevance improve, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing road surface observation data and pre-training machine learning models before actual content generation is needed. Road surface data is collected and processed in advance, and models are trained on historical data patterns, so that during actual use the system can generate content more quickly by applying pre-prepared models to new data rather than training in real-time
Solution Approach 2:
The patent uses partial action by applying machine learning models selectively rather than universally. Instead of processing all road surface data through complex ML models, the system identifies key features and conditions that require ML processing while using simpler processing methods for routine cases, balancing accuracy with processing time through selective application of computational intensity
Data Source
AI summary
Content reflecting road surface data is generated. A content generation device of the present disclosure includes an acquisition unit for acquiring a road surface data indicating a state of a road surface through which a target user passes, and a content generation unit for inputting a prompt including the road surface data to a machine-learned content generation model to generate, by the content generation model, content to be presented to the user passing through the road surface.