Techniques for kinematic and dynamic behavior estimation in autonomous vehicles

This invention describes a system for autonomous heavy vehicles, like diggers, to better understand and predict their own movement and behavior at a worksite. It uses sensor data from the vehicle to feed into a specific type of AI model called a Gaussian process, which then estimates how the vehicle will move. These estimates help control the vehicle's operations, and the model continuously learns and improves by incorporating actual data from the vehicle's real-world actions.

Why it matters: Filed before significant advancements in real-time machine learning inference and edge computing for autonomous systems. The computational demands for real-time Gaussian process models are now more manageable on vehicle-embedded hardware.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, sensors, autonomous vehicle integration

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