Systems and methods for safety-guaranteed driving control of automated vehicles via integrated CLFs and CDBFs

Patent metadata

Field	Value
Publication number	US-12515703-B2
Application number	US-202318242240-A
Country	US
Kind code	B2
Filing date	Sep 5, 2023
Priority date	Sep 2, 2022
Publication date	Jan 6, 2026
Grant date	Jan 6, 2026

Abstract

A safety-guaranteed control system for automated vehicles (AVs) considers both vehicle and tire stabilities on varying road conditions. Conventional AV control systems may not be sufficient to adequately handle control-dependent and time-varying safety constraints. The system integrates control-dependent barrier functions (CDBF) and time-varying CBFs (TCBFs) with control Lyapunov functions (CLFs) in a quadratic programming problem.

First claim

The invention claimed is:

1 . A system, comprising:
a processor in communication with a memory, the memory including instructions executable by the processor to:
access operating data descriptive of operation of a vehicle, including a rate of change of a set of control inputs being applied to the vehicle at a current time step;
evaluate, at the processor and based on the operating data:
a control Lyapunov function (CLF)-based constraint;
a time-varying control barrier function (TCBF)-based constraint; and
a control-dependent barrier function (CDBF)-based constraint;

construct, during operation of the vehicle and based on the operating data, a joint set of constraints that jointly reformulate the CLF-based constraint, the TCBF-based constraint, and the CDBF-based constraint as functions of a rate of change of the set of control inputs being applied to the vehicle;
determine updated values of the set of control inputs for application to the vehicle at a future time step based on the joint set of constraints; and
apply the updated values of the set of control inputs as input to a mobility system of the vehicle.

2 . The system of claim 1 , the memory further including instructions executable by the processor to:
determine the updated values of the set of control inputs by application of a quadratic programming method that evaluates the joint set of constraints in view of the operating data of the vehicle.

3 . The system of claim 1 , the operating data including perception data descriptive of a surrounding environment of the vehicle, the perception data including a set of lane boundaries with respect to a global lateral displacement value of the vehicle and a set of heading angle boundaries with respect to a heading angle of the vehicle.

4 . The system of claim 3 , the TCBF-based constraint being dynamically evaluated based on an updated global lateral displacement value computed from a rate of change of the updated values of the set of control inputs, the TCBF-based constraint bounding the updated global lateral displacement value to comply with the set of lane boundaries.

5 . The system of claim 3 , the TCBF-based constraint being dynamically evaluated based on an updated heading angle computed from a rate of change of the updated values of the set of control inputs, the TCBF-based constraint bounding the updated heading angle to comply with the set of heading angle boundaries.

6 . The system of claim 1 , the operating data including a lateral velocity and a yaw rate of the vehicle, the lateral velocity and yaw rate being associated with a set of stability region boundary functions descriptive of a stability region of the vehicle.

7 . The system of claim 6 , the CDBF-based constraint being dynamically evaluated based on an updated yaw rate and an updated lateral velocity of the vehicle computed from a rate of change of the updated values of the set of control inputs, the CDBF-based constraint bounding the updated yaw rate and the updated lateral velocity of the vehicle to comply with the stability region of the vehicle.

8 . The system of claim 1 , the set of control inputs including a front wheel steering angle value and a yaw moment value.

9 . The system of claim 1 , the memory further including instructions executable by the processor to:
determine the updated values of the set of control inputs for application to the vehicle at the future time step by integrating the rate of change of the updated values of the set of control inputs with respect to the set of control inputs associated with the current time step.

10 . A method, comprising:
accessing, at a processor in communication with a memory, operating data descriptive of operation of a vehicle, including a rate of change of a set of control inputs being applied to the vehicle at a current time step; evaluating, at the processor and based on the operating data:
a control Lyapunov function (CLF)-based constraint;
a time-varying control barrier function (TCBF)-based constraint; and
a control-dependent barrier function (CDBF)-based constraint;
constructing, during operation of the vehicle and based on the operating data, a joint set of constraints that jointly reformulate the CLF-based constraint, the TCBF-based constraint, and the CDBF-based constraint as functions of a rate of change of the set of control inputs being applied to the vehicle; determining updated values of the set of control inputs for application to the vehicle at a future time step based on the joint set of constraints; and applying the updated values of the set of control inputs as input to a mobility system of the vehicle.

11 . The method of claim 10 , further comprising:
determine the updated values of the set of control inputs by application of a quadratic programming method that evaluates the joint set of constraints in view of the operating data of the vehicle.

12 . The method of claim 10 , further comprising:
determining the updated values of the set of control inputs for application to the vehicle at the future time step by integrating the rate of change of the updated values of the set of control inputs with respect to the set of control inputs associated with the current time step.

13 . The method of claim 10 , the operating data including perception data descriptive of a surrounding environment of the vehicle, the perception data including a set of lane boundaries with respect to a global lateral displacement value of the vehicle and a set of heading angle boundaries with respect to a heading angle of the vehicle.

14 . The method of claim 13 , the TCBF-based constraint being dynamically evaluated based on an updated global lateral displacement value computed from a rate of change of the updated values of the set of control inputs, the TCBF-based constraint bounding the updated global lateral displacement value to comply with the set of lane boundaries, and the TCBF-based constraint being dynamically evaluated based on an updated heading angle computed from a rate of change of the updated values of the set of control inputs, the TCBF-based constraint bounding the updated heading angle to comply with the set of heading angle boundaries.

15 . The method of claim 10 , the operating data including a lateral velocity and a yaw rate of the vehicle, the lateral velocity and yaw rate being associated with a set of stability region boundary functions descriptive of a stability region of the vehicle, and the set of control inputs including a front wheel steering angle value and a yaw moment value.

16 . The method of claim 15 , the CDBF-based constraint being dynamically evaluated based on an updated yaw rate and an updated lateral velocity of the vehicle computed from a rate of change of the updated values of the set of control inputs, the CDBF-based constraint bounding the updated yaw rate and the updated lateral velocity of the vehicle to comply with the stability region of the vehicle.

Assignees

• Chen Yan
• Huang Yiwen
• Univ Arizona State

Inventors

• Chen Yan
• Huang Yiwen

Classifications

• B60W2520/14

  Yaw · CPC title

• B60W2520/06

  Direction of travel · CPC title

• B60W2552/53

  Road markings, e.g. lane marker or crosswalk · CPC title

• B60W30/09

  Taking automatic action to avoid collision, e.g. braking and steering · CPC title

• B60W2710/207

  Steering angle of wheels · CPC title