Autonomous Vehicle Context Map Inconsistency Detection
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Solution Overview
Problem
Autonomous vehicles face navigational errors and safety issues due to inaccuracies in context maps, which are not updated to reflect changes in road features over time, leading to inconsistencies between the mapped environment and the actual environment.
Innovation Solution
The implementation of a change detection system onboard autonomous vehicles, which uses sensors to compare real-time data with context maps, identifies inconsistencies, and determines resolutions to ensure safe operation, either by adjusting the vehicle's autonomy system or seeking assistance from a teleoperations system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If context maps are used for navigation, then routing accuracy is improved, but map inaccuracies lead to navigational errors and safety issues
Solution Approach 1:
The system continuously compares sensor data from the actual environment with context map data, detecting inconsistencies and feeding this information back to update the context map. This feedback loop ensures map accuracy is maintained over time, resolving the contradiction between initial routing accuracy and long-term navigational safety.
Solution Approach 2:
The system performs preliminary consistency checks between sensor data and context map data before navigation decisions are made. By proactively identifying and flagging inconsistencies before they cause navigational errors, the system prevents safety issues while maintaining routing accuracy.
2Measurement precision
If context maps are updated frequently, then map accuracy is improved, but system complexity and computational load increase
Solution Approach 1:
The system extracts only the specific inconsistent features detected by comparing sensor data with context map data, rather than updating the entire map. This targeted approach maintains map accuracy while reducing computational complexity and processing requirements.
Solution Approach 2:
The context map is segmented into discrete features (road signs, lane markings, etc.), allowing individual features to be updated independently based on detected inconsistencies. This segmentation enables efficient, localized updates rather than comprehensive map regeneration, reducing system complexity.
3Reliability
If inconsistencies are detected and resolved in real-time, then navigational safety is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs partial consistency checks focused on critical safety-related features rather than comprehensive validation of all map data. This selective approach maintains navigational safety by verifying essential elements while reducing overall processing time.
Solution Approach 2:
The system uses an intermediary consistency check layer that quickly compares sensor data with context map data to identify potential inconsistencies. This intermediary step filters out obvious matches quickly and only triggers detailed resolution processes for actual inconsistencies, reducing average processing time while maintaining safety.
Data Source
AI summary
According to one aspect, a method includes operating a vehicle in a first environment, the vehicle including a change detection system and at least one sensor configured to obtain data associated with the first environment, wherein operating the vehicle in the first environment includes obtaining the data associated with the first environment using the at least one sensor. The method also includes obtaining a context map associated with the first environment and comparing, using the change detection system, the data associated with the first environment and the context map to identify at least a first inconsistency between the data associated with the first environment and the context map. While the vehicle is operating, a resolution for the first inconsistency is determined, and the vehicle takes an action based on the resolution.