Camera-Based Road Environment Modeling Without Image Rectification
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Solution Overview
Problem
Existing vehicle environment modeling systems face challenges in accurately detecting road surfaces and hazards in varying weather and road conditions, due to noise interference and the need for real-time processing.
Innovation Solution
The system uses a camera-based approach with a deep neural network (DNN) to model the vehicle environment by computing a gamma map, which represents the height of pixels above a road plane and their distance from the sensor, allowing for more stable and efficient road surface modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional road surface detection methods are used, then measurement precision may be maintained, but processing time and power consumption increase significantly
Solution Approach 1:
The patent replaces traditional mechanical/image-processing-based road surface detection methods with a deep neural network system. The DNN processes camera images directly to detect road surfaces, shadows, and lights, substituting complex image processing algorithms with a trained neural network that achieves both high speed and high accuracy simultaneously.
Solution Approach 2:
The deep neural network is pre-trained with large datasets containing various road conditions, lighting scenarios, and shadow patterns. This preliminary training enables the system to rapidly process real-time images without requiring complex runtime computations, thus achieving both fast processing and accurate detection.
2Measurement precision
If image rectification is performed before processing, then measurement precision improves, but processing time and computational burden increase
Solution Approach 1:
The patent extracts and removes the image rectification step from the processing pipeline. The deep neural network is designed to process raw, unrectified images directly, eliminating the time-consuming rectification operation while maintaining detection accuracy through its ability to handle various image perspectives and distortions during training.
Solution Approach 2:
The system changes the input parameters expected by the processing algorithm. Instead of requiring rectified images as input, the DNN is trained to accept and process raw camera images with various distortions, effectively changing the parameter space from rectified coordinates to raw sensor coordinates.
3Reliability
If multiple sensors are used to improve detection reliability, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent makes a single camera sensor perform multiple functions that traditionally required different sensors. The DNN processes the camera output to detect road surfaces, identify shadows, detect lights, and determine road geometry, enabling one sensor to replace what would traditionally require multiple specialized sensors.
Solution Approach 2:
The system merges the functionality of multiple detection tasks into a single integrated deep neural network model. Instead of using separate sensors and processing pipelines for different detection functions, the DNN combines these functions into one unified system that processes a single image stream for multiple purposes.
Data Source
AI summary
System and techniques for vehicle environment modeling with a camera are described herein. A device for modeling an environment comprises: a hardware sensor interface to obtain a sequence of unrectified images representative of a road environment, the sequence of unrectified images including a first unrectified image, a previous unrectified image, and a previous-previous unrectified image; and processing circuitry to: provide the first unrectified image, the previous unrectified image, and the previous-previous unrectified image to an artificial neural network (ANN) to produce a three-dimensional structure of a scene; determine a selected homography; and apply the selected homography to the three-dimensional structure of the scene to create a model of the road environment.