Autonomous Vehicle Road Event Validation for Shared Driving Maps
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Solution Overview
Problem
Existing autonomous vehicle systems lack an effective mechanism for sharing and validating driving condition reports among vehicles within a fleet, leading to potential inaccuracies and inefficiencies in navigating dynamic environments.
Innovation Solution
A control system that receives, validates, and combines driving condition reports from multiple autonomous vehicles, using sensor data to create a driving information map, which is periodically updated and filtered to remove outdated information, and shares relevant updates with the fleet.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles independently navigate without sharing information, then each vehicle maintains operational independence, but navigation accuracy and safety deteriorate due to lack of shared driving condition data
Solution Approach 1:
The system segments information sharing by creating distinct communication modules: vehicles publish their sensor data and driving condition reports to specific topics, while separate subscription modules receive and process relevant information. This segmentation allows independent vehicles to share information without requiring complex centralized coordination, improving navigation reliability through data sharing while maintaining operational independence.
Solution Approach 2:
The patent introduces an intermediary validation mechanism where a control system receives, validates, and combines driving condition reports from multiple vehicles before distributing them to the fleet. This intermediary layer filters and consolidates raw sensor data into validated driving information maps, enabling accurate navigation information sharing while reducing the complexity of direct peer-to-peer communication between all vehicle pairs.
2Loss of information
If vehicles share all sensor data with the fleet, then information completeness improves, but data validation accuracy deteriorates due to inability to verify report accuracy
Solution Approach 1:
The system implements feedback mechanisms where vehicles publish their sensor data and driving condition reports to a shared communication channel, and other vehicles can subscribe to verify and cross-validate this information. The control system receives feedback from multiple sources about the same driving conditions, allowing it to validate reports by comparing consistent information across multiple vehicle reports before adding them to the driving information map.
Solution Approach 2:
The control system merges and combines driving condition reports from multiple autonomous vehicles into a consolidated driving information map. By combining multiple independent reports about the same road conditions, the system achieves both information completeness (aggregating data from all vehicles) and validation accuracy (cross-verifying consistency across multiple sources) simultaneously.
3Loss of time
If the system continuously updates the driving information map with new reports, then information currency improves, but processing efficiency deteriorates due to constant data validation and map updates
Solution Approach 1:
The control system implements periodic filtering and updating of the driving information map rather than continuous real-time updates. The system periodically reviews and filters the driving information map to remove outdated information reports while retaining current valid data. This periodic action maintains information currency by regularly updating the map with new validated reports while improving processing efficiency by avoiding constant continuous updates.
4Reliability
If the system validates all information reports using sensor data, then information accuracy improves, but computational resources are excessively consumed
Solution Approach 1:
The control system applies partial validation by validating information reports based on their relevance and consistency rather than performing exhaustive validation on all received data. The system validates reports that contribute new information or contradict existing map data, while accepting consistent redundant reports without full validation. This partial action maintains information accuracy for critical data while reducing computational resource consumption by avoiding unnecessary validation of already-verified information.
Data Source
AI summary
Example systems and methods allow for reporting and sharing of information reports relating to driving conditions within a fleet of autonomous vehicles. One example method includes receiving information reports relating to driving conditions from a plurality of autonomous vehicles within a fleet of autonomous vehicles. The method may also include receiving sensor data from a plurality of autonomous vehicles within the fleet of autonomous vehicles. The method may further include validating some of the information reports based at least in part on the sensor data. The method may additionally include combining validated information reports into a driving information map. The method may also include periodically filtering the driving information map to remove outdated information reports. The method may further include providing portions of the driving information map to autonomous vehicles within the fleet of autonomous vehicles.