Driver Assistance Object Detection via Pre-Training and Simulation

The patent applies preliminary action by first pre-training the classification algorithm with real sensor data to learn stable representations of real-world objects and conditions. This preliminary training with authentic data establishes a foundation that accounts for real data characteristics before the algorithm is trained with simulated data, thereby resolving the systematic differences between simulated and real data that would otherwise degrade detection performance.

The patent uses an intermediary approach by introducing a two-stage training process where real sensor data serves as an intermediary bridge. The algorithm first learns from real data, then transitions to simulated data training. This intermediary real-data training phase mediates between the authenticity of real data and the efficiency of simulated data, allowing the system to leverage both advantages while mitigating their respective disadvantages.

The patent applies copying by using simulated data as a substitute copy of real sensor data for training purposes. Instead of manually preparing and labeling extensive real data, the system generates simulated sensor data that replicates real-world scenarios. This copying approach maintains detection accuracy by preserving the essential characteristics of real data while dramatically reducing the time and resources required for data preparation.

The patent employs parameter changes by systematically varying parameters in the simulated data generation process to match real-world conditions. By adjusting simulation parameters to reflect authentic sensor characteristics, environmental conditions, and object properties, the simulated data achieves sufficient fidelity to real data for effective training, thereby maintaining detection accuracy while avoiding the extensive manual data preparation process.