Driving Lane Determination via Map-Sensor Matching Points
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately determining driving lanes, especially in crowded road conditions like intersections and merging sections, leading to potential lane determination errors and accidents.
Innovation Solution
A driving lane determination apparatus and method that utilizes precise map information and sensor fusion to calculate matching points for each lane, deciding the driving lane based on these calculations, and evaluating positioning reliability, incorporating a system with units for information acquisition, matching point calculation, tracking lane prediction, and final lane decision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional positioning systems are used in crowded road sections, then the system complexity remains low, but the driving lane determination accuracy deteriorates
Solution Approach 1:
The system segments the lane determination process into multiple independent modules: map information acquisition unit, sensor fusion information acquisition unit, matching point calculation unit, and lane determination unit. Each module processes specific data types independently, then integrates results to achieve high accuracy in crowded road sections without overwhelming system complexity.
Solution Approach 2:
The patent introduces matching points as intermediary elements that bridge map information and sensor fusion information. These matching points serve as reference markers calculated by comparing lane line positions from maps with detected lane lines from sensors, enabling accurate lane determination even in complex crowded road environments.
2Reliability
If simple positioning methods are used, then the system remains easy to operate, but positioning reliability deteriorates in crowded roads
Solution Approach 1:
The system merges multiple information sources (map data, GPS coordinates, sensor fusion data) into a unified lane determination process. By combining these diverse data streams and calculating matching points across all sources, the system achieves high positioning reliability in crowded roads while automating the complex integration process to maintain ease of operation.
Solution Approach 2:
The system implements feedback mechanisms where the calculated matching points and determined lanes are continuously validated against incoming sensor data. This feedback loop ensures positioning reliability by constantly comparing expected lane positions with actual detected positions, automatically adjusting for discrepancies without requiring manual intervention.
3Measurement precision
If multiple information sources are integrated for lane determination, then positioning accuracy improves, but information processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing map information in structured formats before real-time operation. Map lane lines, coordinates, and geometric data are prepared in advance, enabling rapid matching with sensor fusion data during actual driving without requiring complex real-time computation of all processing steps.
Solution Approach 2:
The patent applies local quality optimization by focusing computational resources on critical matching operations. Instead of uniformly processing all information sources with equal computational intensity, the system concentrates processing power on calculating matching points where map information and sensor data intersect, achieving high positioning accuracy while minimizing overall processing time.
Data Source
AI summary
A driving lane determination method includes acquiring map information and driving environment information, deciding whether to perform driving lane determination entry based on the map information and the driving environment information, matching the map information and the driving environment information to calculate a matching point of each lane upon deciding the driving lane determination entry, deciding a matching lane based on the calculated matching point, deciding a tracking lane based on a prediction lane predicted from a previous driving lane and lane change determination upon deciding the matching lane, and deciding a final driving lane based on the decided matching lane and the decided tracking lane.