Predictive Semi-Autonomous Vehicle Navigation System

Various types and levels of operator assistance are performed within a unified, configurable framework. A model of the device with a model of the environment and the current state of the device and the environment are used to iteratively generate a sequence of optimal device control inputs that, when applied to a model of the device, generate an optimal device trajectory through a constraint-bounded corridor or region within the state space. This optimal trajectory and the sequence of device control inputs that generates it is used to generate a threat assessment metric. An appropriate type and level of operator assistance is generated based on this threat assessment. Operator assistance modes include warnings, decision support, operator feedback, vehicle stability control, and autonomous or semi-autonomous hazard avoidance. The responses generated by each assistance mode are mutually consistent because they are generated using the same optimal trajectory.

Researchers

Karl Iagnemma / Sterling Anderson / Steven Peters

Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Computer Science: Networking & Signals / Industrial Engineering & Automation: Autonomous Systems
Impact Areas: Advanced Materials

  • integrated framework for vehicle operator assistance based on a trajectory prediction and threat assessment
    United States of America | Granted | 8,437,890
  • methods and apparati for predicting and quantifying threat being experienced by a modeled system
    United States of America | Granted | 8,543,261
  • integrated framework for vehicle operator assistance based on a trajectory prediction and threat assessment
    United States of America | Granted | 8,744,648

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