Predictive Hazard Alerting via Sensor Fusion and Proximity Filtering
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Solution Overview
Problem
Current systems fail to effectively track and alert drivers to moving hazards such as drunk or impaired drivers, providing timely and accurate information to avoid potential dangers on the road.
Innovation Solution
A system and method for predictive locating, reporting, and alerting of moving objects, which includes receiving and merging data from sensors and users to determine potential paths and interactions, using machine learning and AI to project future locations and alert users, while filtering out unnecessary warnings based on geographic mediums and user proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system sends alerts to all users about moving hazards, then more users are notified of potential dangers, but false warnings increase and users experience alert fatigue
Solution Approach 1:
The system applies local quality by customizing alert notifications based on each user's specific location, proximity to the hazard, and potential interaction risk. Instead of uniform alerts to all users, the system tailors notifications to those most likely to be affected, reducing false warnings while maintaining comprehensive coverage for at-risk users.
Solution Approach 2:
The system changes parameters by dynamically adjusting alert thresholds and notification criteria based on multiple factors including user location, hazard movement predictions, interaction probability, and environmental conditions. This parameter-based filtering ensures alerts are sent only when genuine risk exists, eliminating false warnings.
2Measurement precision
If the system uses complex machine learning models to predict object locations, then prediction accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the prediction task by dividing the complex prediction problem into multiple simpler sub-tasks: basic movement pattern recognition, geographic medium analysis, speed and acceleration modeling, and interaction probability calculation. Each segment can be processed independently with appropriate complexity, reducing overall system complexity while maintaining high prediction accuracy through cumulative results.
3Measurement precision
If the system processes real-time sensor data from multiple sources, then data accuracy improves, but data processing time and computational load increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data as it arrives, performing initial validation, filtering, and normalization before main analysis. Historical sensor data is pre-aggregated and stored in optimized formats, allowing the real-time processing stage to focus only on critical computations, thereby reducing processing time while maintaining high data accuracy through multi-stage validation.
Data Source
AI summary
Systems and corresponding methods are provided for moving object predictive locating, reporting, and alerting. An exemplary method includes receiving moving object data corresponding to a moving object; receiving sensor data from a sensor; merging the received moving object data and received sensor data into a set of merged data; and based thereon, automatically determining one or more of: a predicted location or range of locations for the moving object, a potential path of travel or area for the moving object, and a potential for interaction between the moving object and subject objects. The method can include automatically generating and providing alerts based on the determining. Alert can be configured for users having potential for interaction with the moving object. A method may include receiving sensor data from third parties, and provide information generated by the system pertaining to moving objects to other third parties.