Cross-Platform Trust Scoring for Adaptive Mobility Automation
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Solution Overview
Problem
The challenge of quantifying and predicting human trust across different mobility platforms, such as vehicles and electronic scooters, is complex due to varying contexts and definitions of trust, which affects the acceptance and usage of these platforms.
Innovation Solution
A computer-implemented method and system that calculates an estimated trust score using a trust model based on the sequence of automation experiences across mobility platforms, allowing for the modification of platform operations to enhance user trust, including adjusting automation aids and transparency levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a trust model is applied to calculate trust scores across different mobility platforms, then user trust and acceptance are enhanced, but the system complexity increases due to needing to categorize platforms and track automation experience sequences
Solution Approach 1:
The patent segments mobility platforms into distinct categories (e.g., autonomous vehicles, robotic delivery systems, drones) and tracks automation experiences separately for each category. This segmentation allows the trust model to handle complexity by breaking down the overall system into manageable categorical components, where trust scores are calculated and transferred within defined category boundaries.
Solution Approach 2:
The system performs preliminary categorization of mobility platforms and establishes trust score baselines before actual automation experiences occur. By pre-defining categories and initial trust parameters, the system reduces runtime complexity when calculating trust scores during actual operations, as the framework for evaluation is already in place.
2Measurement precision
If trust scores are calculated based on sequences of automation experiences across multiple mobility platforms, then the accuracy of trust prediction improves, but the data processing requirements and computational load increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific mobility platform categories and their associated automation experiences rather than processing all possible data uniformly. The trust model calculates scores with high precision for each category based on relevant experience sequences, while using simplified transfer rules for cross-category predictions, thus optimizing computational load while maintaining accuracy where it matters most.
3Ease of operation
If operations of mobility platforms are dynamically modified based on estimated trust scores, then user acceptance and interaction success improve, but the control system complexity increases
Solution Approach 1:
The patent implements dynamics by making mobility platform operations adaptable based on calculated trust scores. The control system dynamically adjusts automation behavior, information disclosure, and user interaction protocols in response to real-time trust assessments. This allows the system to optimize user acceptance without requiring completely rigid pre-programmed control structures, as the trust-based feedback loop enables flexible adaptation.
Data Source
AI summary
Systems and methods for determining trust across mobility platforms are provided. In one embodiment, a method includes receiving first mobility data for a first automation experience of a user with a first mobility platform. The method also includes receiving a swap indication for a second automation experience of the user with a second mobility platform after the first automation experience. The method further includes selectively assigning the first mobility platform to a first mobility category and the second mobility platform to a second mobility category different than the first mobility category. The method yet further includes calculating an estimated trust score for the second automation experience by applying a trust model based on the first mobility category, the second mobility category, and a sequence of the first automation experience and the second automation experience. The method includes modifying operation of the second mobility platform based on the estimated trust score.