Road Sign Detection With Depth Validation for Reflection Filtering
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Solution Overview
Problem
Existing autonomous vehicle systems struggle to accurately distinguish real road signs from their image reflections during runtime operations, leading to potential safety hazards.
Innovation Solution
A system that combines camera images with depth information from sensors like lidar, radar, or stereo images to classify signs as real or reflections, using machine-learning models for initial classification and spatial validation to determine the nature of the signs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicle systems use camera images to detect road signs, then sign detection capability is improved, but the system cannot distinguish real signs from image reflections, leading to safety hazards
Solution Approach 1:
The patent introduces depth information from lidar or stereo cameras as an intermediary element to mediate between the camera image detection and the final sign identification. This additional sensing layer acts as a mediator that provides spatial context to distinguish real signs from reflections, resolving the contradiction between detection capability and safety reliability
Solution Approach 2:
The patent transitions from two-dimensional camera image analysis to three-dimensional spatial validation by incorporating depth information. This dimensional expansion allows the system to validate sign locations in 3D space, distinguishing real signs from reflections based on their spatial relationships and depth consistency
2Reliability
If the system performs spatial validation to distinguish real signs from reflections, then safety is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary classification using machine learning models to identify candidate signs as either real or reflections before conducting full spatial validation. This preliminary sorting action reduces the number of candidates requiring computationally intensive spatial validation, thereby reducing overall processing time while maintaining safety
Solution Approach 2:
The patent divides the sign validation process into segmented stages: initial camera-based detection, machine learning classification, and selective spatial validation. This segmentation allows the system to apply different processing intensities to different candidate signs, optimizing the balance between safety and processing time
Data Source
AI summary
The described aspects and implementations enable efficient identification of real and image signs in autonomous vehicle (AV) applications. In one implementation, disclosed is a method and a system to perform the method that includes obtaining, using a sensing system of the AV, a combined image that includes a camera image and a depth information for a region of an environment of the AV, classifying a first sign in the combined image as an image-true sign, performing a spatial validation of the first sign, which includes evaluation of a spatial relationship of the first sign and one or more objects in the region of the environment of the AV, and identifying, based on the performed spatial validation, the first sign as a real sign.