Adaptive Geospatial Mapping for AV-Aware Route Avoidance
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Solution Overview
Problem
Conventional geospatial mapping systems fail to consider road safety, efficient traffic management, and vehicle insurance premiums in high-traffic areas due to the presence of autonomous vehicles (AVs), lacking the ability to detect and incorporate AV data into route planning and infrastructure management.
Innovation Solution
A computer-implemented method and system that collects AV sensor suite data, determines AV locations via GPS and sensor data, and generates routes to avoid AVs, integrating this data into geospatial mapping systems for non-AVs, utilizing neural networks and SLAM algorithms for real-time mapping and route planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional geospatial mapping systems are used, then basic navigation functionality is provided, but road safety and traffic management are not improved due to lack of AV data integration
Solution Approach 1:
The patent combines GPS data, sensor suite data from autonomous vehicles, and digital mapping systems into an integrated geospatial mapping system. This merging of multiple data sources enables comprehensive road safety monitoring and traffic management by consolidating information that was previously分散 in separate systems.
Solution Approach 2:
The system implements feedback mechanisms by continuously receiving sensor data from autonomous vehicles, processing this information through neural networks, and using the results to update digital maps and generate improved routing recommendations. This closed-loop feedback enables real-time adaptation to changing road conditions and AV traffic patterns.
2Measurement precision
If real-time sensor data from autonomous vehicles is collected and processed, then route planning accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the essential and relevant features from the complex sensor suite data received from autonomous vehicles. By selectively extracting critical information rather than processing all raw sensor data, the system maintains high route planning accuracy while reducing computational complexity and processing requirements.
Solution Approach 2:
The system introduces digital mapping systems and neural networks as intermediary layers between raw sensor data and route planning outputs. These intermediaries process, filter, and interpret sensor data, transforming complex raw inputs into meaningful routing information that improves accuracy without proportionally increasing system complexity.
3Productivity
If digital mapping systems integrate AV location data to generate avoidance routes, then traffic management efficiency is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data and maintaining updated digital maps with AV location information before routing decisions are needed. This advance preparation ensures that when route planning is required, the system can quickly generate avoidance routes using pre-processed data, improving traffic management efficiency without excessive processing delays.
Data Source
AI summary
A computer-implemented method may include receiving, by a processor set, sensor suite data from an autonomous vehicle (AV); determining, by the processor set, an AV location based on the sensor suite data; determining, by the processor set, a non-AV location based on global positioning system data; mapping, by the processor set, a digital environment based on the sensor suite data and the non-AV location; generating, by the processor set, a route in the digital environment based on the sensor suite data, the AV location, and the non-AV location, wherein the route in the digital environment is configured to avoid the AV location; and communicating, by the processor set, the route to a device of the non-AV.