Autonomous Driving Test Approximation with Trained Neural Networks

The patent creates virtual copies of real-world driving scenarios through simulated environments. Instead of physically testing every possible driving situation, the system generates virtual test cases that replicate real-world conditions, allowing comprehensive verification without the time and resource costs of actual road testing.

The patent performs preliminary analysis to identify critical driving situations and parameters before conducting full-scale testing. By pre-determining which scenarios are most relevant for safety verification, the system can focus testing resources on high-impact cases rather than exhaustively testing all possible situations.

The patent replaces expensive real-world testing with cost-effective virtual simulations. By copying driving scenarios in a simulated environment, the system achieves the same verification goals without the substantial costs associated with physical test drives, sensor calibration, and human safety personnel.

The patent uses computationally inexpensive virtual test cases that can be generated and discarded quickly. Instead of investing in long-term, expensive physical testing infrastructure, the system creates numerous low-cost simulated scenarios that can be rapidly executed and replaced as needed.

The patent divides the complex testing problem into manageable segments by categorizing driving situations into distinct scenarios with specific parameters. Instead of treating all possible driving situations as a single complex problem, the system segments them into structured test cases that can be independently generated, executed, and analyzed.

The patent systematically varies key parameters such as environmental conditions, vehicle states, and traffic scenarios to generate diverse test cases. By changing parameters in a controlled manner rather than randomly, the system achieves comprehensive coverage while maintaining organizational structure and reducing overall system complexity.