Virtual Force Fields for Robust Autonomous Driving Decisions

The patent creates virtual copies of physical road objects (vehicles, pedestrians, infrastructure) as perceptual objects in a simulated environment. These virtual representations capture the essential characteristics and behaviors of real objects, allowing the system to practice and improve detection algorithms without requiring additional physical sensors or infrastructure. The virtual objects are generated through data processing and simulation techniques that replicate real-world scenarios.

The patent replaces physical mechanical sensor systems with computational models and algorithms. Instead of adding more physical sensors to detect road objects, the system uses machine learning models, computer vision algorithms, and data processing pipelines to extract information from existing sensor data. This substitution of mechanical/physical detection methods with computational approaches reduces hardware complexity while maintaining or improving detection precision.

The patent pre-generates large datasets of virtual road objects and scenarios before actual model training. By creating comprehensive virtual environments with diverse objects, conditions, and edge cases in advance, the system prepares training data that covers a wide range of real-world situations. This preliminary data preparation allows models to be trained more efficiently on pre-processed, high-quality data rather than requiring extensive real-world data collection and processing during the training phase.

The patent uses virtual copies of real-world driving scenarios to create training datasets. By simulating diverse road conditions, weather patterns, traffic situations, and edge cases in a virtual environment, the system generates extensive training data without requiring equivalent real-world data collection. This copying approach accelerates data preparation and enables more efficient model training while improving adaptability to unseen situations.