Driving Challenge System with Confidence-Based Personalization
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Solution Overview
Problem
Current challenge platforms lack personalized driving score goals based on individual performance and probability, leading to unfair challenges and inadequate promotion of safe driving behaviors.
Innovation Solution
A network-based system generates driving challenges by modeling driver data, predicting scores, and calculating confidence values to create unique, probability-based goals, ensuring fair and equitable challenges and rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static goals are set for all challenge participants, then the challenge platform is simple to operate, but the goals are unfair to many participants and do not account for individual performance levels
Solution Approach 1:
The patent segments the challenge goals from a single static target into multiple dynamic targets tailored to each participant's performance level and probability of achievement. The system divides participants into different challenge tiers based on their individual characteristics, allowing each person to have customized goals rather than all participants striving for the same unrealistic or too-easy target.
Solution Approach 2:
The challenge goals transition from static fixed values to dynamic values that adjust based on each participant's performance history and predicted probability of achievement. The system continuously updates individualized goals as participants improve or decline in performance, making the challenges adaptive rather than fixed.
2Reliability
If individualized driving challenges are created based on performance modeling and probability calculations, then fair and equitable challenges are achieved, but the system complexity increases significantly
Solution Approach 1:
The system uses automated machine learning models and probability calculations to self-generate individualized challenges without requiring manual intervention. The algorithms automatically analyze participant data, predict achievement probabilities, and set appropriate challenge targets, reducing the need for human operators to manually configure complex challenge parameters for each user.
Solution Approach 2:
The system manages complexity by dynamically adjusting key parameters such as challenge difficulty, target scores, and reward structures based on statistical models. Rather than creating entirely custom challenges from scratch, the system modifies standard challenge templates using calculated parameters like predicted probability of achievement and performance standard deviations.
3Adaptability or versatility
If challenge goals are based on predicted probability and confidence values, then personalized and fair challenges are created, but the computational requirements and data processing needs increase
Solution Approach 1:
The system performs preliminary data collection and baseline performance modeling during an onboarding or initial period, establishing predictive models before full challenge participation begins. This pre-processing of participant data and creation of baseline probability models reduces the computational burden during active challenge periods, as the heavy lifting of model generation occurs in advance.
Data Source
AI summary
Provided herein is a computer system for creating driving challenges for drivers. The computer system may include a processor in communication with a memory device, and the processor may be programmed to: (i) receive driving data associated with a driver, (ii) generate a first model that models the driving data associated with the driver, (iii) calculate a predicted driving score for the driver based at least in part upon the first model, (iv) generate a second model that predicts a confidence of the predicted driving score, (v) calculate a confidence value of the predicted driving score, wherein the confidence value is a squared error of the predicted driving score, and (vi) generate at least one driving challenge for the driver based at least in part upon the predicted driving score and the confidence value for that predicted driving score.