3D Obstacle Detection From RGB Depth Maps for Turning Vehicles
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Solution Overview
Problem
In autonomous driving, vehicles struggle to accurately distinguish the range of obstacles, leading to unsafe driving and a poor user experience.
Innovation Solution
A method utilizing a vehicle-mounted electronic device that processes RGB images using a trained depth estimation model to generate depth images, which are then converted to 3D point cloud maps. These maps are used to determine 3D regions of interest based on the vehicle's size and driving curvature, enabling accurate obstacle detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional obstacle detection methods are used, then the detection process is simple, but the obstacle range cannot be accurately distinguished
Solution Approach 1:
The patent transforms 2D image data into 3D spatial information by constructing three-dimensional bounding boxes and 3D point cloud maps. This dimensional transformation enables accurate obstacle range distinction by adding depth information to the detection process, resolving the contradiction between simple detection and accurate measurement.
Solution Approach 2:
The patent introduces depth estimation models and 3D point cloud maps as intermediary elements between the camera and obstacle detection. These intermediaries process raw image data to generate structured 3D representations, enabling accurate obstacle range detection without requiring direct complex measurement of each obstacle.
2Measurement precision
If 3D point cloud maps are generated for accurate obstacle detection, then obstacle detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent pre-generates 3D point cloud maps and depth estimation models before actual obstacle detection occurs. By preparing these computational structures in advance, the system reduces real-time processing requirements while maintaining high detection accuracy, thus resolving the time-complexity trade-off.
Solution Approach 2:
The patent segments the processing pipeline into distinct stages: image capture, depth estimation, 3D point cloud generation, and obstacle detection. This segmentation allows each stage to be optimized independently, reducing overall processing time while maintaining accuracy through specialized processing at each step.
Data Source
AI summary
A method for assisting safer riving applied in a vehicle-mounted electronic device obtains RGB images of a scene in front of a vehicle when engine is running, processes the RGB images by a trained depth estimation model, obtains depth images and converts the depth images to three-dimensional (3D) point cloud maps. A curvature of the driving path of the vehicle is calculated, and 3D regions of interest of the vehicle are extracted from the 3D point cloud maps according to a size of the vehicle and the curvature or deviation from straight ahead. The 3D regions of interest are analyzed for presence of obstacles. When no obstacles are present, the vehicle is controlled to continue driving, when presence of at least one obstacle is determined, an alarm is issued.