Autonomous Vehicle Camera System for Low-Height Obstacle Detection
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Solution Overview
Problem
Current autonomous vehicle navigation systems face challenges in accurately detecting lane endings and responding to traffic lights and obstacles without relying on map data, and in distinguishing relevant traffic lights, especially when obstacles have a height less than 10 cm.
Innovation Solution
The system employs cameras to capture images of the environment, processes them to detect lane endings, traffic lights, and obstacles, and adjusts navigation accordingly, using visual information and road signs to anticipate lane endings and recognizing traffic lights' status to trigger responses such as lane changes or braking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses cameras to detect lane endings and traffic lights without map data, then the system's reliability is improved, but the device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical sensor systems (LIDAR, radar) with optical camera-based detection systems. The camera system captures images that are processed through image recognition algorithms to detect lane endings, traffic lights, and obstacles, substituting complex mechanical sensing with optical sensing and computational processing.
Solution Approach 2:
The patent introduces image processing algorithms and neural networks as intermediaries between the camera sensor and the navigation decision-making system. These intermediary processing layers extract meaningful information from raw images, enabling reliable detection without direct complex sensor-to-action connections.
2Measurement precision
If the system detects low-height obstacles less than 10 cm, then the measurement precision is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent transitions from single-camera 2D image analysis to multi-camera stereoscopic vision, adding a depth dimension to the detection system. By capturing images from multiple camera positions and processing them through stereo matching algorithms, the system can accurately measure obstacle heights including very low obstacles less than 10 cm that would be indistinguishable in single-plane images.
Solution Approach 2:
The patent performs preliminary image processing and feature extraction to identify potential obstacle regions before detailed height measurement. The system pre-processes images to enhance contrast, detect edges, and segment potential obstacles, making subsequent precise height measurement of low-height objects more feasible.
Data Source
AI summary
Systems and methods use cameras to provide autonomous navigation features. In one implementation, a driver-assist object detection system is provided for a vehicle. One or more processing devices associated with the system receive at least two images from a plurality of captured images via a data interface. The device(s) analyze the first image and at least a second image to determine a reference plane corresponding to the roadway the vehicle is traveling on. The processing device(s) locate a target object in the first two images, and determine a difference in a size of at least one dimension of the target object between the two images. The system may use the difference in size to determine a height of the object. Further, the system may cause a change in at least a directional course of the vehicle if the determined height exceeds a predetermined threshold.