External Trigger Simulation for Human-Like Peripheral Vehicle Behavior

The patent applies dynamics by transitioning from static rule-based algorithms to dynamic machine learning models that can adapt and learn irregular human driving behaviors. The neural network-based peripheral object control algorithm continuously learns from training data to reproduce unpredictable human actions such as sudden lane changes, erratic stopping, and non-compliant driving patterns, thereby improving behavioral reproduction accuracy while managing complexity through automated learning.

The patent changes the fundamental parameters of the control algorithm from fixed rules to learned parameters through machine training. By using training datasets containing real human driving behaviors and adjusting neural network parameters through backpropagation, the system can accurately reproduce irregular behaviors. This parameter transformation allows the system to capture the complexity of human driving without requiring explicit programming of every possible scenario.

The patent uses copying by creating virtual copies of real human driving behaviors through machine learning models. Instead of physically testing every scenario, the system copies and reproduces human driving patterns, including irregular behaviors, in a virtual simulation environment. This allows comprehensive preparation for real-world scenarios without the costs and risks associated with physical testing.

The patent applies preliminary action by training the peripheral object control algorithm extensively before actual autonomous driving deployment. The system performs preliminary simulations with various irregular human behaviors, allowing the autonomous vehicle's perception and decision-making systems to learn appropriate responses in advance. This preliminary training reduces the need for extensive real-world testing and improves safety from the outset.

The patent replaces physical test drives with virtual copies in a simulation environment. The peripheral object control algorithm generates realistic human driving behaviors that can be repeatedly tested against the autonomous vehicle system without consuming physical resources or exposing real vehicles to risk. This copying approach maintains safety validation accuracy while eliminating the time and cost penalties of physical testing.

The patent performs preliminary safety validation through extensive simulation testing before any real-world deployment. By预先 training the system with diverse human behaviors including irregular patterns, the autonomous vehicle learns safe response strategies in advance. This preliminary action in simulation reduces the need for numerous costly and time-consuming real-world test drives while maintaining high safety standards.