Automobile Risk Prediction Using Pre-stored User Behavior Data
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Solution Overview
Problem
Conventional automobile risk prediction systems are ineffective due to their inability to account for user identity and historical actions, leading to inaccurate risk assessments and reduced accident avoidance effectiveness.
Innovation Solution
A support actuation system that incorporates a sensor, processor, and memory to detect and analyze identity, environment, and behavior data, generating a total risk value based on historical actions, which triggers support actuation when the risk exceeds a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional risk prediction systems are used that do not incorporate user identity and historical actions, then the system complexity is reduced, but the measurement precision of risk assessment deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing user identity information and historical action data before risk assessment is needed. The database pre-stores environment risk values, event risk values, and behavior risk values associated with historical actions, so that when a risk assessment is required, the processor can quickly retrieve and combine these pre-prepared data elements to generate an accurate total risk value without complex real-time analysis
Solution Approach 2:
The system creates a digital copy of user behavior patterns by storing historical action data in the database. Instead of analyzing actual physical behavior in real-time, the processor works with copied data representations - identity data, behavior data, environment data, and event data - that replicate the essential characteristics of user interactions. This allows accurate risk assessment through data comparison and calculation without requiring complex real-time monitoring of actual user actions
2Measurement precision
If risk prediction systems incorporate user identity and historical actions, then the risk assessment accuracy is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing user identity information, historical action data, and pre-calculating risk values for different environments, events, and behaviors. The database is populated in advance with environment risk values, event risk values, and behavior risk values, so that during actual risk assessment, the processor only needs to retrieve and combine these pre-prepared values rather than collecting and analyzing raw data in real-time
Solution Approach 2:
The system creates efficient data copies by storing processed and structured information in the database. Instead of processing raw sensor data during risk assessment, the system works with copied and pre-processed data elements (identity data, behavior data, environment data, event data) that have already been organized and associated with risk values, significantly reducing the time required for actual risk calculation
3Reliability
If support actuation is provided based on total risk value threshold, then the reliability of accident avoidance is improved, but the loss of time for decision making increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing risk thresholds and support actuation criteria. The processor is programmed with predetermined thresholds for total risk values and the conditions under which support actuation should be provided. This allows the system to quickly compare calculated risk values against pre-set thresholds and immediately trigger appropriate support actions without requiring complex real-time decision-making algorithms
Solution Approach 2:
The system creates a simplified decision-making model by copying the essential risk assessment logic into a threshold-comparison framework. Instead of implementing complex real-time decision algorithms, the system uses copied data elements (identity data, behavior data, environment data, event data) combined with pre-established thresholds to make rapid support actuation decisions, maintaining reliability while minimizing decision time
Data Source
AI summary
A method and system for risk prediction for a support actuation system. The system includes a support actuation system for an automobile having a support actuation module and/or a risk prediction system. The risk prediction system includes a sensor, a processor, and/or a memory. The sensor detects images corresponding to identity data, environment data, event data, and/or behavior data, which are stored in the memory. The memory also stores a database including identities of users, environment risk values, event risk values, and/or behavior risk values. Using the identity data, the environment data, the event data, and/or the behavior data, the processor determines the environment risk value, the event risk value, the behavior risk value, and/or the total risk value for a user. When the total risk value is above a predetermined risk threshold, the support actuation module performs support actuation.