ML Model Training for Autonomous Vehicle Motion Planning

The patent applies local quality by creating vehicle-type-specific motion planning and control parameters. Instead of using uniform parameters for all vehicles, the system establishes different parameter sets tailored to each vehicle type (e.g., passenger cars, trucks, buses), thereby improving motion planning accuracy and control smoothness for each specific vehicle category while maintaining manageable system complexity through structured parameterization.

The patent implements dynamics by introducing adaptive motion planning parameters that dynamically adjust based on vehicle type characteristics. The system transitions from static, one-size-fits-all parameters to dynamic, vehicle-specific parameters that can be automatically selected and adjusted according to the actual vehicle being operated, enhancing planning accuracy without permanently increasing system complexity.

The patent applies segmentation by dividing the evaluation system into multiple scenario-specific grading modules. Instead of using a single predefined grading system, the patent creates specialized evaluation modules for different driving scenarios (e.g., cornering, straight-line driving, intersection navigation), allowing accurate and effective evaluation in each specific scenario while maintaining overall system manageability through modular architecture.

The patent implements universality by designing an evaluation system that can adapt to multiple driving scenarios through a unified framework. The system uses a common evaluation architecture that can be configured with different grading criteria and parameters depending on the specific scenario being evaluated, thereby achieving both evaluation effectiveness across diverse scenarios and system simplicity through reuse of core evaluation mechanisms.