Neural Controller Variable Estimation From 2D Object Features
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Solution Overview
Problem
Existing control systems for semiautomated mobile platforms, such as self-driving vehicles, rely on complex and expensive software for manual parameterization, which is difficult to generalize across different vehicle applications and is not suitable for end-to-end machine learning methods due to their lack of transparency and manageability.
Innovation Solution
A method using a trained neural network to determine controller variables for mobile platforms based on two-dimensional sensor data, eliminating the need for non-linear projections into three-dimensional space and allowing for robust estimation of controller values independent of the sensor type used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual parameterization by application engineers is used, then control behavior can be optimized for specific vehicle types, but the process is difficult to quantify and cannot be generalized across various vehicle applications
Solution Approach 1:
The patent transforms the manual parameterization process into an automated learning process where the system learns control parameters from training data. Instead of engineers manually adjusting parameters for each vehicle type, the neural network automatically adapts to different vehicle characteristics through training, enabling generalization across applications while maintaining optimized control behavior.
Solution Approach 2:
The system performs self-parameterization through automated training processes. The neural network learns optimal control parameters independently without requiring manual intervention from application engineers for each new vehicle application, making the system self-adaptable and eliminating the generalization bottleneck.
2Extent of automation
If end-to-end machine learning methods are used, then control can be learned directly from sensor data, but the mapping is difficult to understand and debug for safety-relevant components
Solution Approach 1:
The patent segments the control system into interpretable components: object detection, feature extraction, and control parameter estimation. Each component processes and transforms data in a understandable way, allowing engineers to debug and verify individual stages while maintaining the benefits of automated learning. This modular approach preserves interpretability while achieving end-to-end automation.
Solution Approach 2:
The patent introduces intermediate representations (object features, state variables) that bridge the gap between raw sensor data and control parameters. These intermediates provide interpretable checkpoints in the data flow, enabling debugging and verification while maintaining the automated learning pipeline. The intermediates act as mediators that preserve information interpretability throughout the end-to-end process.
3Manufacturing precision
If classic controllers with manual parameterization are used, then control behavior can be optimized for specific applications, but the process is expensive and complex requiring specialized software packages
Solution Approach 1:
The patent replaces complex manual controller design processes with automated neural network training. Instead of using expensive specialized software packages and manual tuning procedures, the system uses automated machine learning that can be implemented with standard tools, significantly reducing complexity and cost while maintaining or improving control optimization quality.
4Measurement precision
If transformation from two-dimensional image space to three-dimensional space is performed, then control variables can be derived for classic controllers, but the transformation is ambiguous, highly non-linear and noisy
Solution Approach 1:
Instead of transforming 2D image data into 3D space coordinates and then processing, the patent inverts the approach by directly estimating control parameters from 2D image features. This avoids the ambiguous and noisy 3D transformation step entirely, working directly in the 2D image space where the data naturally exists, thereby improving accuracy and reducing complexity.
Data Source
AI summary
A method for determining a value of at least one controller variable for guiding a mobile platform in at least semiautomated fashion is proposed. Images of surroundings of the mobile platform are determined using a plurality of two-dimensionally representing sensor systems. A multiplicity of objects are identified in the images. At least two object features for each of the multiplicity of objects are determined in order to determine the at least one controller variable. An input tensor is generated using the in each case at least two determined object features of the multiplicity of objects for a trained neural network. The value of the controller variable is estimated using the input tensor and the trained neural network.