Lane-Level Map Matching for Real-Time Lane Closure Detection
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Solution Overview
Problem
Existing systems struggle to accurately detect and verify lane closures on roads using conventional digital maps, particularly in real-time, which can impact traffic flow and vehicle navigation.
Innovation Solution
A method and apparatus utilizing lane-level map-matching technology to analyze probe data from vehicles, determining lane closures by comparing current traffic volumes with historical data, and providing route guidance or semi-autonomous vehicle control based on lane closure indications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional digital maps and crowd-sourced traffic data are used, then basic route navigation is provided, but accurate detection and verification of lane closures in real-time cannot be achieved
Solution Approach 1:
The patent segments the road network into individual road segments and further segments traffic flow into lane-specific trajectories. By matching probe data points to specific lanes rather than treating roads as undifferentiated segments, the system achieves lane-level precision in detecting closures while maintaining reliability through segment-by-segment verification.
Solution Approach 2:
The system implements feedback by continuously comparing current lane traffic volumes against historical baseline volumes for the same time periods. When deviations exceed thresholds, the system verifies through multiple data points and provides feedback alerts for lane closures, improving both detection accuracy and real-time reliability.
2Quantity of substance
If probe data from multiple vehicles is aggregated, then collective traffic patterns are obtained, but lane-specific traffic volume and closure detection become difficult
Solution Approach 1:
The patent adds a lane dimension to the traditional road segment model. By introducing lane identifiers and matching probe data points to specific lanes within road segments, the system transforms aggregated traffic data into lane-specific measurements, enabling precise closure detection while utilizing the full volume of probe data.
3Measurement precision
If historical traffic data is used for comparison, then expected traffic volumes can be determined, but real-time or near-real-time detection capability is reduced
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing baseline traffic volumes for each lane across different time periods (hourly, daily, weekly patterns). When probe data arrives, the system immediately compares against pre-computed baselines, achieving both high measurement precision and rapid real-time detection without computation delays.
Data Source
AI summary
A method is provided for determining and verifying lane closures using lane-level map-matching to monitor all lanes of a road. Methods may include: receiving a plurality of probe data points, where the probe data points; map-matching the probe data points to one or more road segments; determining, for the probe data points, lanes of travel of the one or more road segments; determining a volume of traffic along the lanes of travel of the one or more road segments based on the lanes of travel of the one or more road segments determined for the probe data; determining an expected volume of traffic along the lanes of travel; and generating an indication of a lane closure in response to a volume of traffic along a first lane of travel of the one or more road segments being a predetermined amount below an expected volume of traffic.