Telematics-Based Virtual Character Training for Driver Risk Awareness
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Solution Overview
Problem
Individuals may not fully appreciate the risks of vehicular operation, leading to a need for technologies that increase awareness and safety by training virtual characters using telematics data from real trips.
Innovation Solution
A system and method that uses telematics data from real vehicle trips to train virtual characters, granting in-game resources based on skill points, and generating virtual occurrences to enhance virtual skills and vehicle operation awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If telematics data from real trips is used to train virtual characters, then driver awareness and safety are enhanced, but system complexity and data processing requirements increase
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios by processing telematics data into virtual occurrences. Real trip data is transformed into virtual character training experiences, allowing drivers to learn from realistic situations without the risks of actual accidents. This copying approach enhances safety while managing system complexity through data abstraction.
Solution Approach 2:
The system introduces a virtual character as an intermediary between real telematics data and driver training. The virtual character absorbs and processes raw telematics data, transforming it into meaningful training scenarios. This intermediary layer simplifies the overall system architecture by decoupling data collection from training delivery.
2Manufacturing precision
If virtual occurrences are generated based on telematics data, then training realism is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of telematics data by pre-identifying and categorizing relevant driving scenarios during normal data collection. This preparation work is done in advance, so when virtual occurrences need to be generated for training, the processing time is reduced while maintaining high realism through the pre-processed scenario library.
3Productivity
If in-game resources are granted based on skill points, then user motivation is increased, but game economy complexity increases
Solution Approach 1:
The system implements a feedback loop where driver performance in virtual training scenarios generates skill points, which are converted into in-game resources. This feedback mechanism motivates users to improve their driving skills while the automated conversion process manages game economy complexity through clear, rule-based resource allocation.
Data Source
AI summary
A computer-implemented method can include determining, based at least in part upon telematics data associated with one or more real trips by a user operating a real vehicle, a plurality of skill points associated with a plurality of real skills. The method can also include generating one or more virtual occurrences to be encountered by a virtual character having a plurality of virtual ratings associated with a plurality of virtual skills. The method can further include determining one or more outcomes associated with the one or more virtual occurrences. The method can additionally include determining a first quantity of a first virtual resource at least for obtaining outcome-modifying items. The method can also include updating a character profile of the virtual character with a selected outcome-modifying item obtained with the first quantity of the first virtual resource. The method can further include presenting the character profile, as updated, to the user. Other embodiments are disclosed.