Autonomous Driving Simulation Data Generation via Sensor Parameter Adjustment

The patent creates virtual copies of real-world driving scenarios through simulation environments. Instead of using sophisticated sensors in physical vehicles, the system generates synthetic sensor data that replicates realistic driving conditions, thereby achieving accurate measurement data without the complexity of high-end physical sensors.

The patent employs low-cost sensors in physical vehicles to collect raw driving data, which is then used to train simulation models. The expensive sophisticated sensors are replaced by inexpensive simulation-generated data that can be produced in large quantities without physical hardware constraints.

The patent introduces a simulation environment as an intermediary between low-sensitivity physical sensors and the required high-precision measurement data. The simulation system processes and enhances the crude sensor data, generating realistic driving scenarios with accurate measurements without requiring sophisticated physical sensors.

The patent transforms the parameter quality from low-precision physical sensor readings to high-precision simulation-generated data by changing the data source and processing methodology. The simulation adjusts parameters such as position, velocity, and environmental conditions to achieve measurement precision unattainable with low-sensitivity sensors alone.

The patent performs preliminary data collection and analysis from real-world low-sensitivity sensor data to understand actual driving scenario distributions. This preliminary action informs the simulation model to generate diverse scenarios that accurately reflect real-world conditions, preventing information loss by ensuring relevant scenarios are covered.

The patent implements feedback loops where simulation results are continuously refined based on comparisons with real-world data patterns. The system uses feedback from actual driving scenarios to adjust and improve the diversity and accuracy of simulated scenarios, thereby maintaining data completeness while expanding scenario coverage.

The patent transforms incomplete sensor data into reliable simulation data by changing the parameters of the data generation process. The simulation system adjusts parameters such as noise levels, sensor characteristics, and environmental conditions to compensate for the incompleteness of source data, generating reliable synthetic data that maintains statistical properties of real-world scenarios.

The patent creates accurate virtual copies of complete driving scenarios from incomplete sensor data by inferring missing information through simulation models. The system generates comprehensive scenario data including elements not captured by low-sensitivity sensors, thereby maintaining reliability while improving productivity through efficient virtual data generation.