Vehicle Risk Analysis System Using Sensor Data Contextualization
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Solution Overview
Problem
Existing vehicle safety solutions primarily focus on post-event reporting and do not provide ongoing feedback to drivers to improve their driving operations, thereby failing to effectively mitigate risk situations in real-time.
Innovation Solution
A computer-implemented method and system that collects real-time sensor measurements from vehicles and occupants, contextualizes them with environmental and personal data, and executes personalized actions to reduce risk by matching sensor data against risk patterns and adjusting context to prevent accidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If vehicle sensors monitor and report past events to the driver, then the driver receives information about vehicle operation, but the system fails to provide ongoing feedback to improve driving operation in real-time
Solution Approach 1:
The system implements continuous feedback by monitoring sensor data in real-time, comparing current driving conditions against historical patterns and risk profiles, and providing immediate feedback to the driver through the user interface. This closed-loop feedback mechanism enables the driver to adjust behavior based on real-time risk assessments rather than receiving delayed post-event reports
Solution Approach 2:
The system performs preliminary risk assessment by analyzing sensor data before critical events occur. By continuously evaluating driving conditions against predefined risk patterns and contextual factors (weather, location, time), the system identifies potential risks in advance and provides early warnings to the driver, enabling preventive action rather than reactive reporting
2Reliability
If the system provides personalized real-time feedback and control actions, then the effectiveness of risk mitigation is improved, but the device complexity increases
Solution Approach 1:
The system employs a multi-functional architecture where a single risk assessment engine performs multiple functions: collecting sensor data, contextualizing information, identifying risk patterns, calculating risk scores, and triggering appropriate responses. This universal core module handles various risk scenarios (drowsiness, distraction, aggressive driving) through a unified framework, reducing overall system complexity compared to separate dedicated systems for each function
Solution Approach 2:
The system implements a nested architecture where multiple layers of analysis are organized hierarchically. The innermost layer collects raw sensor data, which is then contextualized by an intermediate layer that incorporates environmental and vehicle state information. The outermost layer performs high-level risk pattern matching and decision-making. This nested structure allows complex personalized feedback to be generated through systematic layering of processing functions rather than monolithic complexity
3Measurement precision
If the system collects and processes extensive context information and sensor measurements, then the precision of risk identification is improved, but the use of energy increases
Solution Approach 1:
The system implements selective data processing by focusing computational resources on the most critical risk factors and sensor inputs relevant to the current driving context. Rather than continuously processing all available sensor data at full resolution, the system dynamically adjusts the level of processing based on detected risk levels and contextual urgency, performing detailed analysis only when necessary to maintain precision while reducing overall energy consumption
Solution Approach 2:
The system dynamically changes processing parameters based on driving conditions and risk levels. During normal driving, lower-resolution processing is used to conserve energy. When risk indicators are detected or contextual factors suggest elevated risk (adverse weather, high-speed driving), the system increases processing intensity and data collection frequency. This adaptive parameter adjustment maintains measurement precision when needed while minimizing energy consumption during routine operation
Data Source
AI summary
A method for improving risk situations for vehicle occupants in a vehicle which includes: configuring a set of circumstances; defining a set of values for each circumstance where each value has a rate; collecting context information for the circumstances, values and rates; collecting real-time sensor measurements pertaining to a vehicle, a driver and vehicle occupants; retrieving risk patterns from a risk pattern database; matching the sensor measurements to the risk patterns to find a matching risk pattern having a risk similarity value; contextualizing the matching risk pattern by increasing the risk similarity value to result in a personalized risk value; comparing the personalized risk value to a threshold; and executing a context modifying action to lower the personalized risk value below a predefined threshold when the personalized risk value exceeds the predefined threshold.