17949450. SYSTEMS AND METHODS FOR PREDICTIVE CONTROL AT HANDLING LIMITS WITH AN AUTOMATED VEHICLE simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)

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SYSTEMS AND METHODS FOR PREDICTIVE CONTROL AT HANDLING LIMITS WITH AN AUTOMATED VEHICLE

Organization Name

TOYOTA JIDOSHA KABUSHIKI KAISHA

Inventor(s)

James Andrew Dallas of Mountain View CA (US)

Michael Thompson of San Juan Capistrano CA (US)

Yan Ming Jonathan Goh of Palo Alto CA (US)

Avinash Balachandran of Sunnyvale CA (US)

SYSTEMS AND METHODS FOR PREDICTIVE CONTROL AT HANDLING LIMITS WITH AN AUTOMATED VEHICLE - A simplified explanation of the abstract

This abstract first appeared for US patent application 17949450 titled 'SYSTEMS AND METHODS FOR PREDICTIVE CONTROL AT HANDLING LIMITS WITH AN AUTOMATED VEHICLE

Simplified Explanation

The patent application describes a system and method for adjusting a prediction model to control a vehicle during automated driving when it reaches its handling limits.

  • The method involves adjusting the parameters of a prediction model based on friction estimates and sideslip costs associated with the projected trajectory of the vehicle. The friction estimates are derived from Kalman filtering.
  • The prediction model scales the handling limits of the vehicle for the projected trajectory according to a friction circle.
  • The prediction model generates vehicle dynamics using load transfer and brake distribution, taking into account estimated road conditions and the handling limits.
  • The prediction model outputs a driving command to the vehicle for the projected trajectory based on the vehicle dynamics.

Potential applications of this technology:

  • Automated driving systems: This technology can be used in automated driving systems to improve the control and handling of vehicles when they approach their limits.
  • Vehicle safety systems: The adjusted prediction model can enhance the safety of vehicles by providing more accurate and precise control commands in challenging driving conditions.

Problems solved by this technology:

  • Handling limits at projected trajectories: This technology addresses the problem of controlling a vehicle when it reaches its handling limits during automated driving. By adjusting the prediction model and scaling the handling limits, the system can optimize the control commands to keep the vehicle within safe limits.
  • Uncertainty in road conditions: The use of friction estimates derived from Kalman filtering helps in estimating the road conditions and adjusting the handling limits accordingly. This addresses the problem of uncertainty in road conditions and improves the vehicle's ability to adapt to different driving surfaces.

Benefits of this technology:

  • Improved vehicle control: By adjusting the prediction model and scaling the handling limits, this technology enables better control of the vehicle, especially in challenging driving conditions.
  • Enhanced safety: The adjusted prediction model and accurate control commands help in keeping the vehicle within safe limits, reducing the risk of accidents and improving overall safety.
  • Adaptability to road conditions: The use of friction estimates and estimated road conditions allows the system to adapt to different driving surfaces, ensuring optimal control and handling of the vehicle.


Original Abstract Submitted

System, methods, and other embodiments described herein relate to adjusting a prediction model for control at handling limits associated with a projected trajectory during automated driving. In one embodiment, a method includes adjusting parameters of a prediction model using friction estimates and sideslip costs associated with a projected trajectory of a vehicle, the friction estimates being derived from Kalman filtering. The method also includes scaling, using the prediction model, handling limits of the vehicle for the projected trajectory according to a friction circle. The method also includes generating, by the prediction model, vehicle dynamics using a load transfer and a brake distribution, the vehicle dynamics being associated with estimated road conditions and the handling limits. The method also includes outputting, by the prediction model using the vehicle dynamics, a driving command to the vehicle for the projected trajectory.

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