Road Boundary Line Mapping Through Lane Conflict Resolution
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Solution Overview
Problem
Existing vehicle systems face challenges in accurately detecting boundary lines on roads due to sensor errors and conflicting detections, particularly in complex scenarios, leading to reduced reliability and safety in automated driving applications.
Innovation Solution
An estimation system uses a similarity metric to compare line pairs derived from detected keypoints, resolves lane conflicts by optimizing parameters, and generates maps with adjusted boundary lines, improving accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inputs are used for selecting lines in complex road segments, then accuracy of boundary line detection is improved, but costs and delays increase
Solution Approach 1:
The system performs preliminary automated detection of boundary lines using sensor data and machine learning models before manual verification. This preliminary action filters out obvious cases that don't require manual input, reserving manual verification only for complex or ambiguous segments, thereby reducing overall time and cost while maintaining accuracy.
Solution Approach 2:
The system uses automated algorithms and machine learning models to self-detect and classify boundary lines in road segments. The automated system serves itself by identifying which segments require manual verification and prioritizing them, reducing dependency on continuous manual intervention and accelerating the overall process.
2Productivity
If automated detection systems are used for boundary lines, then productivity is improved, but measurement precision deteriorates in complex scenarios
Solution Approach 1:
The system introduces an intermediary verification layer that automatically detects boundary lines using machine learning, then flags uncertain or complex segments for additional verification. This intermediary process maintains high productivity for straightforward cases while ensuring precision for complex scenarios through selective human or enhanced automated review.
Solution Approach 2:
The system dynamically adjusts detection parameters and confidence thresholds based on the complexity of road segments. For complex scenarios, it lowers confidence thresholds to trigger additional verification, while for simple segments, it maintains high thresholds for rapid automated processing, thus balancing productivity and precision adaptively.
3Ease of operation
If sensor data is used to detect road lines, then ease of operation is improved, but reliability deteriorates due to errors and conflicts
Solution Approach 1:
The system implements feedback mechanisms where detected boundary lines are continuously validated against multiple sensor inputs and historical data. Detection results feed back into the system to refine algorithms and identify patterns of errors, improving reliability over time while maintaining automated operation. Conflicts in sensor data trigger re-detection or verification protocols.
Solution Approach 2:
The system uses composite detection approaches by fusing data from multiple sensor types (cameras, LIDAR, radar) and multiple processing algorithms to detect boundary lines. This composite approach compensates for weaknesses in individual sensors or algorithms, providing more reliable and consistent detection results while maintaining ease of automated operation.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to comparing detected line pairs on a road and detecting lane conflicts for identifying boundary lines. In one embodiment, a method includes comparing a similarity metric for different line pairs derived from detected keypoints. The method also includes detecting lane conflicts for vehicles identified with the line pairs using the similarity metric. The method also includes resolving the lane conflicts by comparing parameters of the line pairs that overlap. The method also includes generating a map with boundary lines adjusted for the lane conflicts.