Lane Positioning Using Motion-Image Fusion on Changing Lane Counts
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Solution Overview
Problem
Existing vehicle positioning technologies face challenges in achieving accurate lane-level positioning without high costs, especially in scenarios with changing lane quantities, where visual image-based methods are unreliable due to lane line disappearance and congestion.
Innovation Solution
A method that combines vehicle motion data and road condition image data to determine the lane position, using multi-dimensional data to enhance accuracy and reduce costs, by integrating vehicle motion data, which is less affected by the lane environment, with road condition image data, which provides intuitive lane change information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If visual image-based methods are used for lane positioning, then the system cost is reduced, but the positioning reliability deteriorates in scenarios with changing lane quantities due to lane line disappearance and congestion
Solution Approach 1:
The patent combines vehicle motion data (from sensors like accelerometers and gyroscopes) with road condition image data (from cameras) to create a hybrid positioning system. This merging allows the system to maintain reliability in complex scenarios by using sensor data when visual data is insufficient, while keeping costs lower than pure sensor-based solutions by utilizing available camera data.
Solution Approach 2:
The patent introduces a lane change prediction module as an intermediary that uses vehicle motion data to predict upcoming lane changes. This intermediary component bridges the gap between visual data limitations and accurate positioning by anticipating lane changes before they occur, allowing the system to maintain reliability without requiring expensive high-precision maps or laser radars.
2Measurement precision
If high-precision maps and laser radars are used for accurate lane positioning, then the positioning precision is improved, but the device complexity and implementation difficulty increase
Solution Approach 1:
The patent replaces expensive, complex devices like laser radars and high-precision maps with cheaper, more readily available components: standard vehicle sensors (accelerometers, gyroscopes) and conventional cameras. These components are already present in most modern vehicles, eliminating the need for additional expensive hardware while maintaining acceptable positioning precision through intelligent data fusion.
Solution Approach 2:
The patent substitutes mechanical/optical measurement systems (laser radars, precision maps) with an information-processing-based system that uses vehicle motion data and image analysis. This substitution reduces device complexity by relying on computational methods rather than complex physical measurement instruments, while still achieving accurate lane positioning through data fusion and lane change prediction.
3Ease of manufacture
If visual image-based positioning is used in areas with lane line disappearance, then the implementation cost is reduced, but the positioning accuracy deteriorates
Solution Approach 1:
The patent uses vehicle motion data to predict lane changes before they occur. By analyzing acceleration, deceleration, and steering patterns, the system anticipates upcoming lane changes and prepares positioning calculations in advance. This preliminary action ensures accurate positioning even when lane lines disappear during the actual lane change maneuver, maintaining precision without increasing implementation cost.
Solution Approach 2:
The patent transitions from relying solely on two-dimensional visual image data to incorporating temporal and motion-dimensional data from vehicle sensors. By adding the dimension of vehicle motion dynamics (acceleration, deceleration, steering angle over time), the system achieves accurate positioning in scenarios where traditional visual methods fail, without requiring expensive additional hardware.
Data Source
AI summary
A positioning method and apparatus, a computer device, and a non-transitory computer-readable storage medium. The method includes: determining a first target lane where a target vehicle is located based on the target vehicle reaching a first position in a target area on a road, the target area comprising the first position and a second position, a first quantity of lanes included at the first position being different from a second quantity of lanes included at the second position, the first position being a start position in the target area, and the second position being an end position in the target area, acquiring vehicle motion data of the target vehicle in the target area and road condition image data, and determining a second target lane where the target vehicle is located before leaving the second position according to the vehicle motion data, the road condition image data, and the first target.