Lane Marker Identification Using Time-Series Sensor Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional automatic traveling vehicles inaccurately recognize lane boundaries when the number of lanes changes, such as during lane branching or merging, due to relying on distance measurements from a reference lane boundary line.
Innovation Solution
A sensor information processing device that stores past detection results as time-series data and uses a central processing unit to identify lane markers by comparing new detection results with existing data, determining whether they belong to existing or new lane markers based on similarity analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distance measurement from reference lane boundary line is used to determine lane continuity, then the system is simple and fast, but accuracy deteriorates when number of lanes changes
Solution Approach 1:
The patent transitions from single-dimension distance measurement to multi-dimensional time-series data analysis. By incorporating temporal dimension and comparing detection results across multiple time points, the system achieves more accurate lane boundary identification while maintaining reasonable processing complexity through systematic comparison methods.
Solution Approach 2:
The system performs preliminary actions by storing past detection results as time-series data before making identification decisions. This preprocessing allows the system to compare new detection results with historical data, improving accuracy in determining whether detected lines belong to existing lanes or represent new lanes during merging or branching scenarios.
2Measurement precision
If time-series data comparison is used to improve lane marker identification accuracy, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system applies partial action by selectively comparing new detection results with relevant historical data rather than processing all possible combinations. The comparison focuses on key temporal patterns and significant changes, achieving improved accuracy without requiring exhaustive analysis of all time-series data points.
Solution Approach 2:
The system uses feedback mechanisms where identification results from previous time steps inform the processing of current detection results. By continuously updating the time-series data with new measurements and using this accumulated information to guide subsequent identifications, the system improves accuracy while optimizing processing efficiency through iterative refinement.
Data Source
AI summary
Provided is a sensor information processing device that processes the detection results of the plurality of external-environment sensors that recognize the lane marker that divides the lane and can identify the lane marker more accurately than before. A sensor information processing device 100 includes a storage device 102 that stores a past detection result De of an external-environment sensor 200 as time-series data, and a central processing unit 101 that identifies a lane marker on the basis of the time-series data. The central processing unit 101 determines that the new detection result De belongs to the existing lane marker or the new lane marker on the basis of the comparison between the new detection result De not included in the time-series data and the time-series data.