Monocular Vehicle Topography Mapping With AI Height Estimation
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Solution Overview
Problem
Existing methods for determining the topography of a vehicle environment, such as those used in autonomous driving, require intensive computational power and rely on detecting candidate objects before calculating their size and height, which can be inefficient and resource-intensive.
Innovation Solution
A detection device equipped with a camera unit and an evaluation unit that utilizes trained artificial neural networks to determine height and depth values directly from a single image, adjusting resolution based on importance and relevance, allowing for efficient computation by focusing resources on critical areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical camera-based methods (structure-from-motion or stereo camera) are used to determine three-dimensional structure, then measurement precision of depth and height is improved, but device complexity and computational power requirements increase significantly
Solution Approach 1:
The patent replaces complex mechanical/computational 3D reconstruction systems (stereo cameras, structure-from-motion algorithms) with a monocular camera system combined with machine learning. Instead of using multiple cameras or intensive computational geometry, a single camera captures images that are processed by trained neural networks to directly estimate depth and height, substituting mechanical complexity with intelligent processing.
Solution Approach 2:
The patent introduces machine learning models (neural networks) as an intermediary between the monocular camera input and the depth/height output. This intermediary layer learns to map 2D image features to 3D spatial information, bridging the gap without requiring complex direct computational geometry or multiple sensors.
2Device complexity
If machine learning-based monocular depth methods are used, then device complexity is reduced, but measurement precision of depth and height may deteriorate
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance with large datasets containing ground truth depth and height information. During operation, the pre-trained models directly predict 3D parameters from 2D images without requiring complex runtime computations. This shifts computational burden from runtime to training time, improving real-time performance while maintaining accuracy.
Solution Approach 2:
The patent changes the approach from direct computational geometry to statistical learning by transforming the problem parameters. Instead of solving geometric equations, the system learns parameter mappings (pixel coordinates to depth/height values) through training, allowing efficient inference with acceptable precision for autonomous driving applications.
3Measurement precision
If candidate objects are detected before calculating depth values, then object detection accuracy is improved, but productivity and processing speed deteriorate due to sequential processing
Solution Approach 1:
The patent merges object detection and depth estimation into a unified machine learning framework. Instead of sequentially detecting objects first and then calculating their depth, the neural network simultaneously performs both tasks by learning joint representations, enabling parallel processing and improving throughput without sacrificing detection accuracy.
Solution Approach 2:
The patent uses preliminary action by pre-training models to recognize both object identities and spatial parameters together. During inference, the model directly outputs both detection results and depth/height values in a single pass, eliminating the need for sequential processing steps and improving real-time performance.
Data Source
AI summary
A detection device for determining a topography of a vehicle environment comprises a camera unit which is configured to detect at least one image of the surroundings of the vehicle environment, wherein the respective image of the surroundings comprises pixels arranged at respective image coordinates of the image of the surroundings. It is provided that the detection device comprises an evaluation unit which is configured to process the respective image of the surroundings according to a predetermined height determination method, wherein the image of the surroundings in the predetermined height determination method is fed to a first artificial neural network which is trained to assign respective height values to the respective pixels of the respective image of the surroundings in a world coordinate system of the vehicle environment with respect to a predetermined horizontal plane.