Neural Network Smart Vehicle Control Model for Steering
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Solution Overview
Problem
Current smart vehicle steering wheel control systems rely heavily on manual parameter regulation and sensor input, requiring significant manpower and being inefficient in various driving environments.
Innovation Solution
A method and apparatus for building a smart vehicle control model using neural networks, which acquires and processes sample data to extract vehicle state and road condition features, trains a model to predict steering wheel turning angles, and optimizes the model through simulated testing, reducing the need for manual parameter regulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PID algorithm is used for steering wheel control, then control precision can be maintained, but manual parameter regulation effort increases significantly
Solution Approach 1:
The system uses neural network models that automatically learn and adjust control parameters from training data, enabling the control system to self-optimize without manual intervention. The neural network extracts features from sensor data and directly outputs control signals, replacing the manual PID parameter tuning process while maintaining control precision.
Solution Approach 2:
The patent replaces the traditional mechanical PID control system with an intelligent neural network-based control system. The neural network processes sensor inputs and generates control outputs through learned patterns, substituting the manual parameter regulation mechanism with an automated intelligent system that adapts to different driving conditions.
2Extent of automation
If traditional control methods are used, then system simplicity is maintained, but automation level and efficiency decrease
Solution Approach 1:
The neural network control system serves multiple functions: it processes various sensor inputs (accelerometer, gyroscope, magnetometer data), adapts to different road conditions (curved roads, slopes), and generates appropriate control signals. This multi-functional intelligent system replaces multiple separate control mechanisms, achieving high automation while managing complexity through integration.
Solution Approach 2:
The system dynamically changes control parameters based on real-time sensor data and learned patterns. The neural network adjusts its internal parameters (weights and biases) during training and operation, enabling adaptive control that responds to varying driving conditions without requiring manual reconfiguration of system architecture.
Data Source
AI summary
The present invention provides a method of building a smart vehicle control model, and a method and apparatus for controlling a smart vehicle, wherein the method of building a smart vehicle control model comprises: acquiring sample data which comprise corresponding steering wheel turning angles under driving environments; extracting vehicle state features and road condition features from the sample data; using the extracted features to train a neural network model to obtain the smart vehicle control model. The method of controlling smart vehicle comprises: extracting vehicle state features and road condition features of a vehicle to be controlled; inputting the extracted features into the smart vehicle control model to obtain a steering wheel turning angle; controlling the vehicle to be controlled using the steering wheel turning angle. The present invention builds the smart vehicle control model in a machine learning manner, does not require manual parameter regulation and reduces man power costs caused by parameter regulation.