Vehicle Lane Estimation via Transverse Distance Variation
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Solution Overview
Problem
Existing advanced driver assistance systems (ADAS) and vehicle-to-vehicle (V2V) communication face challenges in accurately recognizing a vehicle's driving lane due to errors in GPS signals, leading to unreliable services and driver inconvenience, especially when recognizing road surface markings is difficult or when other vehicles are nearby.
Innovation Solution
A system and method that recognizes the most adjacent road surface markings and estimates the driving lane by calculating variations in transverse distance between the vehicle and these markings at preset time intervals, comparing them with preset values to determine lane changes and provide accurate driving lane information to navigation devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based lane recognition is used, then location information can be obtained, but measurement precision deteriorates due to GPS signal errors
Solution Approach 1:
The patent introduces road surface markings as an intermediary reference object between the vehicle and the lane. Instead of directly using GPS coordinates to determine lane position, the system uses camera-captured images of road surface markings (lane dividers, edge lines) as a mediator to accurately identify the vehicle's lane. This intermediary approach eliminates GPS error accumulation and provides precise lane recognition through visual feature matching.
2Measurement precision
If all road surface markings are recognized to estimate driving lane, then measurement precision may improve, but device complexity increases and processing time increases
Solution Approach 1:
The patent extracts only the essential road surface marking features needed for lane estimation - specifically focusing on the closest visible marking to the vehicle. Rather than processing all detected markings, the system identifies and utilizes the most relevant marking (typically the nearest lane divider or edge line) to determine lane position. This extraction approach maintains high measurement precision while significantly reducing computational complexity and processing requirements.
3Measurement precision
If road surface markings far from vehicle are used for lane estimation, then measurement precision may improve, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent applies local quality by focusing measurement and recognition efforts on the local area closest to the vehicle where road surface markings are most visible and clearly defined. The system prioritizes detecting and measuring markings in the immediate vicinity (near field) rather than attempting to measure distant markings. This local-focused approach ensures high measurement precision because nearby markings appear larger, clearer, and less susceptible to occlusion or degradation, thereby reducing detection and measurement difficulty.
Data Source
AI summary
A system for estimating a driving lane of a vehicle includes a road surface marking recognizing unit configured to recognize a road surface marking of a lane in which the vehicle is driving, a lane change determining unit configured to calculate a variation in a distance between the vehicle and the recognized road surface marking at a pre-set time interval, and compare the calculated variation with a preset value to generate lane change information. The system also includes a driving lane estimating unit configured to estimate the driving lane of the vehicle on the basis of information of the lane in which the vehicle is driving and the lane change information.