Autonomous Vehicle Path Planning Using Uncertainty Risk Maps

The patent replaces complex mechanical/path optimization algorithms with a neural network-based learning system. The neural network is trained offline to learn safe paths directly from sensor data and environmental models, substituting the need for runtime complex constraint optimization and detailed environmental modeling with a trained intelligent system that generalizes across scenarios.

The patent performs path planning computations in advance by training a neural network offline using extensive environmental models and constraint data. This preliminary training phase captures complex safety requirements and environmental constraints, allowing the system to make rapid path decisions during runtime without重新 performing complex optimizations.

The patent transforms the path safety checking problem from a heuristic qualitative assessment into a quantitative neural network classification task. By changing the approach from rule-based heuristics to a learned model that processes environmental parameters and predicts safety outcomes, the system achieves both ease of implementation and high reliability through data-driven generalization.

The patent replaces heuristic rule-based safety checking with a neural network-based safety assessment system. The neural network learns safety patterns from training data and can evaluate paths in complex situations beyond the scope of predefined heuristics, providing more reliable safety checks while maintaining ease of operation through a unified learning-based framework.

The patent substitutes formal verification methods with a neural network-based safety assessment system. Instead of using complex formal methods to prove path safety, the system uses a trained neural network that has learned safety patterns from extensive training data, achieving reliable safety verification through learning rather than formal proof.

The patent enables the system to learn safety requirements and verification rules automatically from training data without manual specification. The neural network self-learns what constitutes a safe path by analyzing training examples, eliminating the need for manual formal verification rule creation and reducing implementation complexity.

The patent performs extensive data collection and safety analysis in advance during the offline neural network training phase. By pre-processing and learning from extensive training data before deployment, the system accumulates safety knowledge beforehand, allowing rapid runtime decisions without needing to collect and analyze extensive data during actual operation.

The patent creates a trained neural network model that captures safety patterns learned from extensive training data. This model copy contains the distilled safety knowledge and can be deployed to make rapid safety assessments without requiring the original extensive training dataset to be present during runtime operations.