Obstacle Avoidance Reminding for Unmanned Navigation
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Solution Overview
Problem
Existing obstacle avoidance reminding technologies primarily focus on passability detection in the front region, failing to provide specific information about obstacles, which limits effective navigation and safety in blind guiding and unmanned driving applications.
Innovation Solution
The method involves ground detection using image data to acquire road information, followed by passability and obstacle detection, generating detailed obstacle avoidance reminding information by analyzing ground conditions and obstacle presence, enabling users to navigate safely around obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If only passability detection is performed to determine whether the road is passable, then the detection process is simple and fast, but specific information about obstacles is not provided
Solution Approach 1:
The detection process is segmented into multiple stages: first performing passability detection to determine if the road is passable, and only if impassable, then performing additional road condition detection and obstacle detection. This segmentation ensures obstacle information is obtained when needed while avoiding unnecessary complexity in passable scenarios.
Solution Approach 2:
Passability detection is performed as a preliminary action before detailed obstacle detection. This preliminary check filters out passable roads that don't require further analysis, preventing loss of obstacle information only when relevant while minimizing overall detection complexity.
2Reliability
If road condition detection and obstacle detection are always performed, then comprehensive obstacle avoidance information is provided, but the detection time and processing load increase
Solution Approach 1:
The detection workflow is segmented into conditional stages where comprehensive road condition detection and obstacle detection are performed only when passability detection determines the road is impassable. This ensures reliable obstacle avoidance information is obtained when needed while reducing detection time for passable roads.
Solution Approach 2:
Instead of always performing full detection, the system performs partial detection (only passability check) when sufficient, and excessive detection (full road condition and obstacle analysis) only when necessary. This balanced approach optimizes both reliability and time efficiency.
3Measurement precision
If detailed ground detection and obstacle detection are performed, then accurate obstacle avoidance information is obtained, but the processing complexity increases
Solution Approach 1:
Processing is segmented into hierarchical levels: ground detection for basic road information, passability detection for quick assessment, and detailed obstacle detection only when required. This segmentation achieves high measurement precision for obstacles when needed while managing processing complexity through conditional execution.
Solution Approach 2:
Ground detection and passability detection serve as preliminary actions that prepare data and determine whether detailed obstacle detection is necessary. This preliminary processing reduces overall complexity by filtering out cases that don't require intensive analysis while ensuring accuracy when obstacles are present.
Data Source
AI summary
An obstacle avoidance reminding method includes: performing ground detection based on acquired image data to acquire ground information of a road; performing passability detection based on the acquired ground information, and determining a traffic state of the road; if it is determined that the road is impassable, performing road condition detection for the road to acquire a first detection result, and performing obstacle detection for the road to acquire a second detection result; and determining obstacle avoidance reminding information based on the first detection result and the second detection result.