Leading Vehicle Body Movement Detection Using AI Vision

The patent applies preliminary action by pre-training deep learning neural networks with extensive training data representing various vehicle body movements (acceleration, braking, lane changes, etc.). The classification categories and evaluation criteria are predetermined, allowing the system to rapidly process real-time sensor data without performing complex analysis during actual operation. This shifts the computational burden to the offline training phase rather than real-time processing.

The patent replaces traditional mechanical or rule-based sensor data processing systems with artificial intelligence and deep learning algorithms. Instead of using complex if-then rules or mechanical analysis methods to interpret sensor data, the system employs trained neural networks that automatically learn patterns and classifications from training data, significantly reducing the complexity of real-time processing while handling large quantities of sensor data.

The patent implements preliminary action by extensively training deep learning neural networks with diverse training data that represents various driving conditions, vehicle types, and body movements. This pre-training process establishes the algorithm's precision before deployment, allowing the system to achieve high measurement precision in real-time applications without requiring complex runtime adjustments or calculations.

The patent applies parameter changes by using deep learning neural networks with adjustable parameters (weights and biases) that are optimized during training. The system transforms the complex problem of precise movement detection into a parameter optimization problem, where the neural network learns the optimal parameters from training data. Once trained, these parameters enable precise detection without requiring complex real-time computational processes.

The patent applies preliminary action by pre-processing and pre-training the deep learning models offline before deployment. The neural networks are trained on extensive datasets in advance, so that during real-time operation, the system only needs to perform forward propagation through the already-trained network, which is computationally efficient. This allows rapid hazard detection while minimizing real-time energy consumption.

The patent implements partial action by processing only the most critical aspects of sensor data in real-time using the pre-trained model. The system focuses computational resources on detecting and classifying hazardous situations rather than analyzing all aspects of the driving environment in equal detail. This selective real-time processing reduces energy consumption while maintaining high detection speed for safety-critical events.