Autonomous Vehicle Driving Map Validation for Fleet Road Events
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Solution Overview
Problem
Autonomous vehicles lack an effective system for sharing and reporting driving conditions within a fleet, leading to potential conflicts and obstacles going unaddressed due to the absence of a centralized information management system.
Innovation Solution
A method and system for receiving, validating, and combining information reports from autonomous vehicles to create a driving information map, which is periodically filtered and shared with vehicles within the fleet, utilizing sensor data to ensure accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles operate independently without centralized information sharing, then each vehicle maintains operational independence, but driving conditions and obstacles remain undetected across the fleet
Solution Approach 1:
The system segments information collection at the individual vehicle level while centralizing validation and map creation at the server level. Each vehicle independently collects sensor data and generates information reports about its local environment, which are then transmitted to the server for centralized processing. This segmentation allows vehicles to maintain operational independence while contributing to collective awareness.
Solution Approach 2:
The server acts as an intermediary between individual vehicles. It receives information reports from multiple vehicles, validates them against sensor data, combines validated reports into a comprehensive driving information map, and distributes relevant portions back to vehicles. This intermediary structure enables information sharing without requiring direct vehicle-to-vehicle communication or complex decentralized coordination.
2Loss of information
If all information reports from vehicles are collected and processed centrally, then comprehensive driving information is achieved, but data validation and processing time increase
Solution Approach 1:
Vehicles perform preliminary actions by collecting sensor data and generating information reports about their local driving conditions before transmitting to the server. This preliminary local processing reduces the burden on the central server, as data arrives in a pre-processed format ready for validation and integration into the driving information map.
Solution Approach 2:
The server validates and processes only the necessary portions of received information reports rather than all data from all vehicles equally. It selectively validates reports based on relevance to the driving information map being constructed, filtering out redundant or less critical information to optimize processing efficiency while maintaining information completeness.
3Productivity
If real-time information sharing is implemented across the fleet, then obstacle detection improves, but communication bandwidth and processing load increase
Solution Approach 1:
The system implements local quality by providing customized information to each vehicle based on its specific needs and location. The server determines which portions of the driving information map are relevant to each vehicle and transmits only those specific portions, rather than broadcasting the entire map to all vehicles. This reduces communication bandwidth usage and energy consumption while maintaining navigation efficiency.
4Reliability
If outdated information is retained in the driving information map, then historical data is preserved, but information accuracy and relevance decrease
Solution Approach 1:
The server implements periodic filtering of the driving information map at regular intervals or based on time thresholds. Outdated information reports are automatically removed from the map through this periodic cleanup process, ensuring that only current and relevant information is retained. This maintains information accuracy without requiring continuous manual intervention.
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.