Vehicle Navigation Using Neural Network Lane Data in Uncovered Map Areas
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Solution Overview
Problem
Current vehicle navigation systems face poor navigation effectiveness in uncovered areas of high-precision maps due to limited coverage, leading to map image jumping and inadequate lane-level navigation information, which negatively impacts user experience.
Innovation Solution
The method involves obtaining environment information and lane information, including first lane information from covered areas and second lane information from uncovered areas using a neural network model, to draw a vehicle sign on the map, providing continuous navigation information and reducing map image jumping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lane information is only obtained from covered areas of high-precision maps, then navigation accuracy in covered areas is improved, but navigation effectiveness in uncovered areas deteriorates
Solution Approach 1:
The system makes the map matching module universal by enabling it to function in both covered areas (using high-precision map data) and uncovered areas (using standard map data with neural network generated lane information). This allows the navigation system to adapt to any driving environment regardless of high-precision map coverage, resolving the contradiction between maintaining high accuracy in covered areas and providing effective navigation in uncovered areas.
Solution Approach 2:
A neural network model acts as an intermediary to generate lane information in uncovered areas where high-precision map data is unavailable. This intermediary component bridges the gap between covered and uncovered areas, allowing the system to maintain navigation effectiveness across all regions while preserving the high accuracy benefits of high-precision maps where available.
2Adaptability or versatility
If the system switches between covered and uncovered areas of high-precision map, then navigation coverage is improved, but map image jumping occurs
Solution Approach 1:
The system performs preliminary actions by pre-processing and smoothing the lane information data before rendering it on the map display. This includes generating lane information in advance for uncovered areas using the neural network model and preparing transition data to ensure seamless switching between covered and uncovered areas, thereby preventing map image jumping during transitions.
Solution Approach 2:
The system changes parameters by dynamically adjusting the source and quality of lane information based on the current location's map coverage status. When transitioning between covered and uncovered areas, the system modifies data retrieval parameters, interpolation parameters, and rendering parameters to ensure smooth transitions without visual jumping, thus maintaining map image stability while expanding navigation coverage.
3Area of stationary object
If standard maps are used for navigation, then coverage area is improved, but lane-level navigation information becomes inadequate
Solution Approach 1:
The system replaces the traditional mechanical approach of relying solely on pre-existing map data with an intelligent system using neural networks to generate lane information dynamically. This substitution allows the system to maintain extensive coverage using standard maps while recovering detailed lane-level information through AI-generated data, thus eliminating information loss in uncovered areas.
Solution Approach 2:
The system changes the information quality parameter by using neural network models to generate high-resolution lane-level information from standard map data in uncovered areas. This parameter transformation allows the system to maintain broad geographic coverage using accessible standard maps while restoring detailed lane-level navigation information that would otherwise be lost, achieving both extensive coverage and information completeness.
Data Source
AI summary
Provided are a vehicle navigation method, a vehicle and a storage medium. The vehicle navigation method includes: in response to a vehicle being in a driving state, obtaining environment information corresponding to the vehicle; obtaining lane information corresponding to the vehicle from a lane information set based on the environment information, in which the lane information includes first lane information of covered areas of a high-precision map and second lane information of uncovered areas of the high-precision map; and drawing a vehicle sign corresponding to the vehicle on the map based on the lane information, to provide navigation information for the vehicle.