Road Abnormality Detection for Autonomous Vehicle Navigation
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Solution Overview
Problem
Autonomous driving vehicles face inaccuracies in positioning due to discrepancies between electronic maps and actual road environments, potentially leading to accidents.
Innovation Solution
A method and apparatus for generating road abnormality information by comparing acquired driving environment data with pre-stored data, identifying differences, and updating electronic maps in real-time to ensure accurate navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous driving vehicle uses the original electronic map for navigation, then the navigation system can operate with pre-stored data, but the positioning accuracy deteriorates when the driving environment changes
Solution Approach 1:
The system compares real-time driving environment data with pre-stored electronic map data to detect differences, providing feedback on environmental changes. This feedback mechanism enables the system to identify when the actual environment deviates from the mapped data, allowing for real-time adjustments to maintain positioning accuracy while adapting to changing road conditions.
Solution Approach 2:
The system performs preliminary comparison between current driving environment data and pre-stored map data before navigation decisions are made. By proactively detecting differences in advance, the system can prepare appropriate responses ahead of time, ensuring that positioning accuracy is maintained even when environmental changes occur during vehicle operation.
2Adaptability or versatility
If the electronic map is updated in real-time, then the adaptability to environment changes improves, but the data processing complexity increases
Solution Approach 1:
The system extracts and compares only the essential driving environment data elements (such as road markings, obstacles, and environmental features) between current and pre-stored maps, rather than processing the entire map dataset. This selective extraction approach enables real-time updates and adaptability while reducing the computational complexity associated with full map regeneration and comparison.
Solution Approach 2:
The data processing is segmented into distinct modules: data acquisition, data comparison, difference detection, and map update. This segmentation allows each component to process information independently and efficiently, reducing overall system complexity while maintaining real-time adaptability. The modular structure enables optimized processing of specific data types without requiring complete system reprocessing.
Data Source
AI summary
Embodiments of the present disclosure relate to a method and apparatus for generating information. The method can include: acquiring first driving environment data of a target road segment; comparing the first driving environment data with pre-stored second driving environment data of the target road segment, and determining a difference between the first driving environment data and the second driving environment data; and generating, in response to determining the difference satisfying a preset condition, road abnormality information.