Autonomous Driving Obstacle Detection Using 3D ROI Depth Mapping
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Solution Overview
Problem
Autonomous vehicles struggle to accurately determine the distance of obstacles, leading to unsafe driving conditions and poor user experience due to inaccurate activation of safety measures.
Innovation Solution
Implement a method using a depth estimation model and semantic segmentation to convert RGB images into 3D point cloud maps, determine 3D regions of interest based on vehicle size, and convert these into 2D regions for obstacle detection, enabling precise identification of obstacles and appropriate safety responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional obstacle detection methods are used, then the system is simple, but the measurement precision of obstacle distance is poor
Solution Approach 1:
The patent transforms 2D image data into 3D spatial information by constructing point cloud maps and calculating depth values. This dimensional transformation enables accurate distance measurement of obstacles by converting planar image coordinates into three-dimensional spatial coordinates with depth information.
Solution Approach 2:
The patent introduces depth estimation models and point cloud maps as intermediary representations between the camera and obstacle detection. These intermediaries process and enhance the raw image data, providing accurate depth information that improves obstacle distance measurement precision.
2Reliability
If safety measures are activated frequently, then driving safety is improved, but false alarms increase
Solution Approach 1:
The patent applies semantic segmentation to identify specific regions of interest in the image, focusing detection resources on areas with potential obstacles. By analyzing only relevant local regions rather than the entire image, the system improves detection accuracy and reduces false alarms caused by irrelevant background elements.
Solution Approach 2:
The patent replaces traditional rule-based safety activation with an AI-driven depth estimation and obstacle recognition system. This substitution enables more accurate distinction between real obstacles and false targets, reducing unnecessary safety activations while maintaining reliable safety responses.
3Measurement precision
If comprehensive obstacle detection is performed, then detection accuracy is improved, but the time required for detection increases
Solution Approach 1:
The patent segments the image processing task into distinct stages: initial obstacle candidate identification, depth estimation for candidates only, and detailed analysis of high-priority regions. This segmentation allows comprehensive detection accuracy while reducing overall processing time by avoiding exhaustive analysis of all image regions.
Solution Approach 2:
The patent performs preliminary processing to identify potential obstacle regions before conducting detailed depth estimation and analysis. By pre-filtering and prioritizing regions of interest, the system prepares data in advance, enabling faster and more accurate obstacle detection when time-critical decisions are required.
Data Source
AI summary
A method for managing driving applied in an electronic device which assesses distances to objects in a path of autonomous driving obtains RGB images of a scene in front of a vehicle, processes the RGB images based on a trained depth estimation model, and obtain depth images corresponding to the RGB images. The depth images are converted to 3D point cloud maps, 3D regions of interest from the 3D point cloud maps are determined according to a size of the vehicle, and the 3D regions of interest are converted into 2D regions of interest according to internal parameters of a camera. The 2D regions of interest are analyzed for obstacles. Driving continues when the 2D regions of interest have no obstacles, the vehicle is controlled to issue an alarm when obstacles are discovered.