Moving Body Behavior Prediction Device Using Environment-Specific Models
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Solution Overview
Problem
Existing moving body behavior prediction devices face challenges in accurately predicting the behavior of vehicles and pedestrians due to varying environmental factors and interactions, as they often rely on a single prediction model that may not be suitable for different travel environments, leading to potential unsafe vehicle behavior.
Innovation Solution
A moving body behavior prediction device that includes a travel environment recognition unit, a prediction model evaluation value storage unit, and a prediction model determination unit to select the most appropriate prediction model based on the recognized environment, ensuring safe and accurate behavior prediction by evaluating and selecting from pre-prepared safety models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single prediction model is used for all environments, then the device complexity is reduced, but the prediction accuracy and reliability deteriorate in varying travel environments
Solution Approach 1:
The patent divides the prediction model into multiple environment-specific models (first prediction model for highway, second prediction model for urban areas). Each model is specialized for particular travel environments, improving prediction reliability without requiring a single complex universal model. The segmentation is implemented through environment recognition that triggers selection of the appropriate pre-prepared model.
Solution Approach 2:
The patent prepares multiple prediction models in advance for different travel environments before actual use. The environment recognition unit identifies the current travel environment, and the system selects from pre-prepared models corresponding to that environment. This preliminary preparation eliminates the need for real-time model complexity while ensuring reliability through environment-matched predictions.
2Reliability
If multiple prediction models are prepared for different environments, then the prediction accuracy improves, but the device complexity increases
Solution Approach 1:
The patent implements a universal model selection mechanism that handles multiple environment-specific models through a single unified interface. The environment recognition unit and model selection logic serve as a universal layer that manages diverse prediction models without requiring separate management systems for each model, thus improving reliability through multi-environment coverage while controlling overall system complexity.
Solution Approach 2:
The patent introduces an environment recognition unit as an intermediary between the multiple prediction models and the behavior prediction output. This intermediary identifies the current travel environment and selects the appropriate pre-prepared model, simplifying the management of multiple models by providing a single point of control based on environment classification.
3Adaptability or versatility
If prediction models are selected based on real-time environment recognition, then the adaptability to varying environments improves, but the processing time and device complexity increase
Solution Approach 1:
The patent prepares multiple prediction models in advance for different travel environments before actual use. By pre-classifying models according to travel environments (highway, urban areas, etc.), the system eliminates real-time model training or complex selection algorithms, reducing processing time while maintaining environment adaptability through pre-matched model selection.
Solution Approach 2:
The patent applies different prediction models locally suited to specific travel environments rather than using a single global model. The environment recognition unit identifies the current local environment type, and the system selects the pre-prepared model with local quality optimized for that specific environment, achieving adaptability without excessive processing overhead.
Data Source
AI summary
A moving body behavior prediction device predicts the behavior of a moving body comprising: a travel environment recognition unit that acquires external information DS and recognizes the travel environment; a prediction model evaluation value storage unit that stores an evaluation value for each travel environment in relation to prediction models prepared in advance; a prediction model determination unit 112 that determines the prediction model corresponding to the travel environment recognized by the travel environment recognition unit from among the prediction models, the determination being performed on the basis of the travel environment recognized by the travel environment recognition unit and the evaluation value stored in the prediction model evaluation value storage unit; and a behavior prediction unit 113 that predicts the behavior of the moving body using the prediction model determined by the prediction model determination unit.