20240011791. EDGE ENHANCED INCREMENTAL LEARNING FOR AUTONOMOUS DRIVING VEHICLES simplified abstract (GM GLOBAL TECHNOLOGY OPERATIONS LLC)

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EDGE ENHANCED INCREMENTAL LEARNING FOR AUTONOMOUS DRIVING VEHICLES

Organization Name

GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor(s)

Wenyuan Qi of Shanghai (CN)

EDGE ENHANCED INCREMENTAL LEARNING FOR AUTONOMOUS DRIVING VEHICLES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240011791 titled 'EDGE ENHANCED INCREMENTAL LEARNING FOR AUTONOMOUS DRIVING VEHICLES

Simplified Explanation

The abstract of this patent application describes a system that includes a computer with a processor and memory. The memory contains instructions that allow the processor to determine a route for a vehicle based on sensor data, using a trained neural network model. The system also updates the neural network model based on data received from an edge computing device or an infrastructure device.

  • The system uses a trained neural network model to determine a route for a vehicle based on sensor data.
  • The neural network model is updated using data received from an edge computing device or an infrastructure device.
  • The system includes a computer with a processor and memory.
  • The memory contains instructions that program the processor to perform the route determination and model updating tasks.

Potential applications of this technology:

  • Autonomous vehicles: The system can be used in autonomous vehicles to determine the best route based on sensor data and update the neural network model for improved performance.
  • Traffic management: The system can be used in traffic management systems to optimize routes for vehicles and improve overall traffic flow.
  • Logistics and delivery: The system can be used in logistics and delivery operations to optimize routes for vehicles and improve efficiency.

Problems solved by this technology:

  • Route optimization: The system solves the problem of determining the best route for a vehicle based on sensor data, improving efficiency and reducing travel time.
  • Model updating: The system solves the problem of updating the neural network model based on data received from edge computing or infrastructure devices, ensuring the model remains accurate and up-to-date.

Benefits of this technology:

  • Improved efficiency: The system helps optimize routes for vehicles, leading to improved efficiency and reduced travel time.
  • Enhanced safety: By using sensor data and a trained neural network model, the system can help identify and avoid potential hazards, enhancing safety for the vehicle and its occupants.
  • Adaptability: The system can update the neural network model based on new data, allowing it to adapt to changing conditions and improve performance over time.


Original Abstract Submitted

a system comprises a computer including a processor and a memory. the memory includes instructions such that the processor is programmed to: determine, via a trained neural network model, a route for a vehicle to traverse based on vehicle sensor data, and update the trained neural network model based on data received from at least one of an edge computing device or an infrastructure (v2i) device.

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