Vehicle Lane Positioning Using Reference Vehicles in Obscured Roads
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Solution Overview
Problem
Existing navigation systems fail to automatically determine a vehicle's lane position, especially under varying road conditions such as weather obstructions or construction, which limits operator awareness and navigation accuracy.
Innovation Solution
A navigation system that receives vehicle environment information, identifies lane reference vehicles based on type and position, generates a road lane model with lane delineation estimates, and calculates the user vehicle's lane position through lateral shift analysis, using both static and dynamic road elements when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane marking methods are used for navigation, then the system is simple and easy to operate, but the measurement precision of lane position deteriorates under weather obstructions or construction conditions
Solution Approach 1:
The patent uses proximate vehicles as intermediary objects to infer lane position information. Instead of directly detecting lane markings, the system identifies vehicles in proximity, determines their lane positions, and uses their relative positions to estimate the user vehicle's lane position, especially when lane markings are obscured or unreliable
Solution Approach 2:
The patent replaces the mechanical/optical detection of lane markings with a computational approach using vehicle position data. The system substitutes direct visual detection of road infrastructure with indirect inference through vehicle-to-vehicle relative positioning and lane reference vehicle identification
2Measurement precision
If lane reference vehicles are identified and monitored to generate road lane models, then the measurement precision of lane position improves, but the device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary identification and classification of lane reference vehicles from proximate vehicles before using them for lane position estimation. By pre-selecting suitable reference vehicles based on vehicle type and position criteria, the system prepares reliable reference data in advance, improving subsequent lane position measurement accuracy
Solution Approach 2:
The patent segments the fleet of proximate vehicles into categories, identifying specific vehicles as lane reference vehicles based on their type and position. This segmentation allows the system to selectively use only the most suitable vehicles for lane position estimation, rather than processing all detected vehicles
Data Source
AI summary
A navigation system includes: a communication unit configured to receive vehicle environment information of a user vehicle, the vehicle environment information including proximate vehicle information representing proximately located vehicles relative to the user vehicle; and a control unit, coupled to the communication unit, configured to: determine lane reference vehicles from the proximately located vehicles based on a vehicle type of the proximately located vehicles; monitor the relative location of the lane reference vehicles; generate a road lane model including a lane delineation estimation based on the relative location of the lane reference vehicles; and calculate a lane position of the user vehicle according to the lane delineation estimation based on a lateral position shift of the user vehicle.