Camera-Based Lane Position Detection Without GPS Base Stations
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Solution Overview
Problem
The high construction cost associated with laying base stations for precise vehicle positioning using GPS or Beidou systems and the need for re-establishing site models when lane lines change pose challenges in unmanned driving.
Innovation Solution
A method and system that determines the position relation between a vehicle and lane lines using vehicle and lane line images, mapped into a world coordinate system, without relying on positioning systems, by obtaining vehicle models and calibration parameters from camera images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS or Beidou positioning system is used to judge position relation between vehicle and lane line, then positioning precision is improved, but construction cost increases due to large-scale base station deployment
Solution Approach 1:
The patent extracts the lane line detection function from the complex positioning system (GPS/Beidou with base stations). By using only the camera to capture lane line images and process them through image recognition algorithms, the system removes the need for expensive external positioning infrastructure while maintaining the ability to determine vehicle position relative to lane lines.
Solution Approach 2:
The vehicle uses its own onboard camera to capture and process lane line images independently. The system performs self-service by using the vehicle's自身的 imaging device to complete the positioning task without relying on external base stations or positioning satellites, thereby eliminating the need for large-scale infrastructure construction.
2Measurement precision
If site model is re-established when lane lines change, then positioning accuracy is maintained, but construction and maintenance cost increases
Solution Approach 1:
The system dynamically adapts to lane line changes by continuously capturing lane line images and updating the detection model in real-time. Instead of requiring manual re-establishment of site models when lane lines change, the algorithm automatically adjusts to new lane configurations through continuous image processing and recognition.
Solution Approach 2:
The system changes its detection parameters automatically by processing new lane line images and updating the recognition model. When lane lines change, the system captures new images and adjusts its detection parameters (such as lane line position, angle, and curvature) to match the new configuration, eliminating the need for manual model re-establishment.
3Measurement precision
If positioning system with base stations is deployed, then vehicle position can be determined, but system complexity and infrastructure requirement increase
Solution Approach 1:
The patent replaces the mechanical positioning infrastructure (base stations, satellites, receivers) with an optical system. Instead of using electromagnetic signals from GPS/Beidou satellites and ground-based base stations, the system uses optical imaging through a camera to capture lane line patterns and determine vehicle position through image processing and geometric calculations.
Data Source
AI summary
A method for detecting the position relation between a vehicle and a lane line includes: obtaining a vehicle model, the vehicle model being represented by multiple first coordinates in a world coordinate system; obtaining a lane line image, the lane line image being captured by a camera disposed on a vehicle; obtaining a calibration parameter of the camera; determining, according to the lane line image and the calibration parameter, a first line segment of a lane line mapped into the world coordinate system; and determining the position relation between the lane line and the vehicle according to the position relation between the first line segment and multiple first coordinates in the world coordinate system. The position relation between the lane line and the vehicle can be determined without using a positioning system, so that the construction cost of intelligent driving is reduced.