Cross-Mobility Takeover Prediction via Transfer Learning
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Solution Overview
Problem
Existing systems for predicting driver takeovers are limited in their ability to generalize across different types of mobility, particularly because most research and data collection have focused on cars, while micro-mobilities like e-scooters are less understood and less experienced by users.
Innovation Solution
A method for predicting takeover events across mobilities using a deep neural network (DNN) and transfer learning, which monitors responses during takeover events in both car and micro-mobility simulations, performs statistical analysis to find patterns and deviations, and forms takeover predictions on different types of micro-mobility vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing takeover prediction systems focus only on car data, then prediction accuracy for cars is improved, but adaptability to micro-mobility vehicles deteriorates
Solution Approach 1:
The patent applies transfer learning to create a universal takeover prediction model that can function across multiple mobility types (cars, e-scooters, e-bikes). The model trained on car data is transferred to micro-mobility contexts, enabling single model to serve multiple functions and mobility types without requiring separate specialized models for each vehicle category.
Solution Approach 2:
The patent modifies the prediction model by incorporating mobility-type specific parameters and adjusting training data composition. By changing the parameters used in the neural network (including mobility type indicators and adjusted feature sets), the system adapts from car-specific predictions to cross-mobility predictions while maintaining accuracy.
2Ease of manufacture
If data collection is performed only on traditional cars, then model training is simplified, but measurement coverage for future mobilities is insufficient
Solution Approach 1:
The patent performs preliminary data collection and model training on well-established car datasets before deploying to micro-mobility applications. By establishing a robust baseline model on car data first, the system creates a reliable foundation that can then be transferred to newer mobility types, ensuring both training simplicity and future reliability.
Solution Approach 2:
The patent uses car driving data as a proxy or copy to represent micro-mobility driving patterns. By copying the fundamental driver behavior patterns observed in cars and applying them to micro-mobility contexts through transfer learning, the system achieves reliable predictions for new mobility types without requiring extensive new data collection.
3Measurement precision
If separate models are developed for each mobility type, then mobility-specific accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a single universal takeover prediction model that handles multiple mobility types through transfer learning. This eliminates the need for maintaining separate specialized models for cars, e-scooters, and e-bikes, reducing system complexity while preserving the ability to achieve high accuracy across different mobility contexts through the unified model's adaptive capabilities.
Data Source
AI summary
A method for predicting takeover events across mobilities may monitor responses during takeover events in a car simulation from a plurality of participants. The method may monitor responses during takeover events in a micro-mobility simulation from the plurality of participants. The method may perform statistical analysis to find patterns and deviations between characteristics extracted from the responses during the takeover events in the car simulations and characteristics extracted from responses during the takeover events in the micro-mobility simulations. The method may form takeover predictions on a different type of micro-mobility vehicle using predictive modeling from the characteristics extracted from the responses during the takeover events in the car simulations and characteristics extracted from responses during the takeover events in the micro-mobility simulations. The method may integrate transfer learning in the predictive modeling.