Safe Route Determination Using Geographic Risk Maps
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Solution Overview
Problem
Current automotive safety systems lack the ability to effectively determine safe routes by analyzing vehicle event data from non-collision situations, limiting their incorporation into route navigation to minimize safety risks.
Innovation Solution
A method and system for safe route determination that converts vehicle event data into a geographic risk map, allowing for the quantification of driving risk factors and routing vehicles based on these factors to minimize risk and maximize safety, using onboard vehicle systems and remote computing for real-time data processing and route optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle event data is collected and processed to determine safe routes, then route safety is improved, but computational resources and power consumption increase
Solution Approach 1:
The system segments the route determination process into multiple components: data collection by onboard systems, data transmission to remote servers, risk map generation by remote computing resources, and route optimization using pre-generated risk maps. This segmentation allows computationally intensive tasks to be performed remotely rather than consuming vehicle power.
Solution Approach 2:
A geographic risk map serves as an intermediary data structure that pre-processes and stores risk information from vehicle event data. Instead of performing complex real-time calculations in the vehicle, the system uses this pre-computed risk map to quickly determine safe routes, significantly reducing onboard computational requirements and power consumption.
2Measurement precision
If comprehensive vehicle event data is collected from all vehicles, then route determination accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the essential risk-related features from comprehensive vehicle event data to create the geographic risk map. By taking out and storing only the critical risk information rather than processing all raw data in real-time, the system achieves accurate route determination while simplifying the processing complexity.
Solution Approach 2:
The system performs preliminary processing of vehicle event data to generate geographic risk maps in advance, before route determination is needed. This preliminary action organizes and pre-computes risk information, reducing the complexity of real-time route determination while maintaining high accuracy.
3Reliability
If real-time route optimization is performed for all vehicles, then individual vehicle safety is improved, but system-wide computational load increases
Solution Approach 1:
The system merges individual vehicle risk data with aggregate fleet data to create comprehensive geographic risk maps. By combining data from multiple sources, the system improves individual route safety while distributing the computational load across the entire fleet, as each vehicle contributes data and receives optimized routes without bearing the full processing burden alone.
Data Source
AI summary
A method for safe route determination, including determining a geographic risk map for a geographic region, wherein the geographic risk map is determined based on a vehicle event dataset aggregated from a plurality of vehicles enabled with an onboard vehicle system, and automatically determining a route between two locations in the geographic region based on the geographic risk map.