Driver Model Transfer Learning Across Vehicle Types
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Solution Overview
Problem
Existing information processing systems face challenges in forecasting behaviors for vehicles, particularly when dealing with scarce data from certain vehicle types, such as luxury vehicles, where building a driver model is difficult due to limited travel histories.
Innovation Solution
An information processing system that employs transfer learning, where a processor receives travel histories from both abundant and scarce vehicle types, builds a driver model for the scarce type by leveraging the model from the abundant type, and unifies parameter formats to facilitate learning across different vehicle types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If travel histories are collected only from vehicles of a specific type (e.g., luxury vehicles), then the driver model accuracy for that type improves, but the system cannot build adequate models for vehicle types with scarce data
Solution Approach 1:
The patent creates a universal driver model framework that can serve multiple vehicle types. By building a base driver model from abundant data (regular vehicles) and enabling it to be adapted to different vehicle types through transfer learning, the system achieves multi-functionality. The same modeling framework and processing methodology can be applied across luxury vehicles, regular vehicles, and other vehicle types, resolving the contradiction between specialized accuracy and broad applicability.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the input data characteristics rather than changing the fundamental modeling approach. By modifying the travel history parameters (such as vehicle type classification, speed ranges, acceleration patterns) fed into the existing driver model framework, the system adapts to different vehicle types. This allows the same model structure to produce type-specific accurate predictions without requiring separate models for each vehicle category.
2Quantity of substance
If transfer learning is performed from regular vehicles to luxury vehicles, then driver models for luxury vehicles can be built with limited data, but the model may inherit biases from the source vehicle type
Solution Approach 1:
The patent applies partial action by performing transfer learning selectively rather than completely replacing the source model. The system uses the pre-trained driver model from regular vehicles as a foundation and performs partial retraining or fine-tuning using the limited luxury vehicle travel histories. This partial adjustment allows the model to adapt to luxury vehicle characteristics while retaining the robust patterns learned from the larger regular vehicle dataset, thereby maintaining reliability with reduced data requirements.
3Measurement precision
If separate driver models are built for each vehicle type, then model accuracy for each type improves, but the system complexity and development time increase
Solution Approach 1:
The patent implements a universal driver model architecture that can function across multiple vehicle types through parameter adjustments and transfer learning. Instead of maintaining separate independent models for luxury vehicles, regular vehicles, and other types, the system uses a single adaptable framework. This universal model reduces the number of models to maintain while achieving vehicle-type-specific accuracy through data-driven adaptation, directly resolving the contradiction between specialization and complexity.
Solution Approach 2:
The patent merges the driver modeling approaches for different vehicle types into a unified framework. By combining the training data from multiple vehicle types and using transfer learning to share knowledge across them, the system creates an integrated model that serves all vehicle types. This merging eliminates the need for separate development and maintenance cycles for each vehicle type while preserving the ability to achieve type-specific accuracy through selective adaptation.
Data Source
AI summary
An information processing system receives first travel histories from vehicles that belong to vehicle type A, learns based on the first travel histories to build a first driver model that represents relation between travel situations and behaviors of the vehicles that belong to a first vehicle type, receives second travel histories from vehicles that belong to vehicle type X that is different from vehicle type A, and performs transfer learning in which the second travel histories are used for the first driver model to build a second driver model that represents relation between travel situations and behaviors of the vehicles that belong to vehicle type X.