Driving Assistance Geofence Model for Accident Avoidance
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Solution Overview
Problem
Current route planning systems do not effectively utilize historical accident data to provide drivers with routes that minimize the probability of delays due to accidents, lacking awareness and avoidance of accident-prone areas.
Innovation Solution
A system that generates a model using historical accident data to determine geofences and assign scores, allowing for proactive route planning and real-time guidance to avoid accident-prone areas by integrating historical accident data with real-time traffic and road conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current route planning systems use only real-time traffic data without historical accident data, then the system complexity remains low, but the ability to avoid accident-prone areas is insufficient
Solution Approach 1:
The system performs preliminary analysis of historical accident data to identify accident-prone areas and creates geofences in advance. This allows the route planning system to proactively avoid hazardous locations before accidents occur, rather than merely reacting to real-time traffic conditions. The geofences are pre-established based on historical patterns, enabling predictive route planning that improves safety without requiring complex real-time processing of accident data.
2Productivity
If the system integrates historical accident data with real-time traffic conditions, then route efficiency improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential information from historical accident data by creating simplified geofence representations of accident-prone areas. Instead of processing and analyzing raw accident data in real-time, the system extracts the spatial patterns and represents them as predefined geofences with risk scores. This extraction approach maintains route efficiency by using compact geofence data during route planning while minimizing the data processing burden, as the complex historical analysis is performed only during the geofence creation phase.
3Loss of information
If the system creates detailed geofences based on historical accident data, then awareness of hazardous locations increases, but the model complexity increases
Solution Approach 1:
The system applies local quality by creating geofences with varying levels of detail and risk scores based on the specific characteristics of each accident-prone area. Rather than using a uniform model for all locations, the system tailors the geofence properties (such as risk score, size, and priority) to the local accident patterns and severity. This allows the system to maintain comprehensive information about hazardous locations while managing model complexity through localized, context-appropriate representations.
Data Source
AI summary
A tangible, non-transitory machine-readable medium includes machine-readable instructions that, when executed by one or more processors, cause the one or more processors to receive a plurality of data inputs from one or more databases, determine a plurality of geofences based at least in part on the plurality of data inputs, and determine a plurality of scores based at least in part on the plurality of data inputs. Each score of the plurality of scores is associated with a geofence of the plurality of geofences. The machine readable instructions, when executed by the one or more processors, also cause the one or more processors to generate a model comprising the plurality of geofences and the plurality of scores, and to output a driving route based at least in part on the plurality of geofences and the plurality of scores.