Self-Driving Model Retraining From User Psychological Metrics
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Solution Overview
Problem
Current self-driving models lack personalization and often fail to account for individual user preferences and psychological impacts, leading to decreased trust and performance due to reliance on average user feedback, which can be time-consuming to collect and may not reflect diverse user experiences.
Innovation Solution
A computer-implemented method that computes psychological metrics from user sensor data to generate a personalized self-driving model by re-training the model based on user-specific datasets, adjusting driving actions to minimize negative psychological impacts and align with individual preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If self-driving models are modified based on average user feedback, then overall model performance may improve, but the modifications take a long time and do not reflect diverse user experiences
Solution Approach 1:
The system enables self-service by automatically collecting sensor data, computing psychological metrics, generating personalized datasets, and retraining models without manual intervention. The self-driving model autonomously adapts to individual user preferences through continuous monitoring of physiological signals and automated machine learning operations, eliminating the time-consuming manual feedback collection process.
Solution Approach 2:
The system implements continuous feedback loops by monitoring user physiological data (heart rate, skin conductance, etc.) in real-time, computing psychological metrics from this data, and using these metrics to automatically retrain the self-driving model. This closed-loop feedback mechanism allows the model to continuously adapt to user preferences without manual intervention, resolving the contradiction between model improvement and time consumption.
2Adaptability or versatility
If self-driving models use average driving styles and preferences, then modifications can be applied broadly, but the perceived performance decreases for individual users with unique preferences
Solution Approach 1:
The system applies local quality by creating personalized self-driving models for each individual user based on their unique physiological responses and psychological metrics. Instead of using a single average model for all users, the system generates user-specific datasets from individual sensor data and trains customized models that reflect each user's unique driving preferences and comfort thresholds, thereby improving perceived performance for each individual.
Solution Approach 2:
The system segments the user population into individual users with unique characteristics by computing separate psychological metrics for each person based on their physiological sensor data. This segmentation allows the creation of multiple personalized models rather than a single aggregate model, enabling the system to adapt to diverse user preferences while maintaining broad applicability across different individuals.
3Device complexity
If self-driving models do not account for individual psychological impacts, then the model structure remains simple, but user trust decreases due to uncomfortable driving decisions
Solution Approach 1:
The system changes parameters by incorporating psychological metrics (derived from physiological sensor data such as heart rate, skin conductance, and temperature) as new input parameters for the self-driving model. These psychological parameters enable the model to account for individual user comfort levels and emotional states, allowing it to make driving decisions that consider user trust and comfort without fundamentally altering the underlying model architecture.
Data Source
AI summary
In various embodiments, while a self-driving model operates a vehicle, a user monitoring subsystem acquires sensor data associated with a user of the vehicle and a vehicle observation subsystem acquires sensor data associated with the vehicle. The user monitoring subsystem computes values for a psychological metric based on the sensor data associated with the user. Based on the values for the psychological metric, a feedback application determines a description of the user over a first time period. The feedback application generates a dataset based on the description and the sensor data associated with the vehicle. Subsequently, a training application performs machine learning operation(s) on the self-driving model based on the dataset to generate a modified self-driving model. Advantageously, the dataset enables the training application to automatically modify the self-driving model to account for the impact different driving actions have on the user.