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公开(公告)号：US20230050192A1
公开(公告)日：2023-02-16
申请号：US17389722
申请日：2021-07-30
IPC分类号： B60W60/00 , G05B13/02 , G05D1/00 , G05D1/02 , G08G1/0967
摘要： Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).
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公开(公告)号：US20220137961A1
公开(公告)日：2022-05-05
申请号：US17089763
申请日：2020-11-05
发明人： Rien Quirynen , Jiaming Liang , Stefano Di Cairano
摘要： A system is controlled by solving a mixed-integer optimal control optimization problem using branch-and-bound (B&B) optimization that searches for a global optimal solution within a search space. The B&B optimization iteratively partitions the search space into a nested tree of regions, and prunes at least one region from the nested tree of regions before finding a local optimal solution for each region when a dual objective value of a projection of a sub-optimal dual solution estimate for each region into a dual feasible space is greater than an upper bound or lesser than a lower bound of the global optimal solution maintained by the B&B optimization.
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3.发明申请公开(公告)号：US20200293009A1
公开(公告)日：2020-09-17
申请号：US16297870
申请日：2019-03-11
摘要： A controller for controlling a system with continuous and discrete elements of operation accepts measurements of a current state of the system, solves a mixed-integer model predictive control (MI-MPC) problem subject to state constraints on the state of the system to produce control inputs to the system, and submits the control inputs to the system thereby changing the state of the system. To solve the MI-MPC, the controller transforms the state constraints into state-invariant control constraints on the control inputs to the system, such that any combination of values for the control inputs, resulting in a sequence of values for the state variables that satisfy the state constraints, also satisfy the state-invariant control constraints, and solve the MI-MPC problem subject to the state constraints and the state-invariant control constraints.
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公开(公告)号：US20200290625A1
公开(公告)日：2020-09-17
申请号：US16299285
申请日：2019-03-12
发明人： Karl Berntorp , Rien Quirynen , Stefano Di Cairano
IPC分类号： B60W40/068 , B60W30/18 , B60W30/02
摘要： A system control a vehicle using a friction function describing a friction between a type of surface of the road and a tire of the vehicle as a function of a slip of a wheel of the vehicle. The parameters of each friction function include an initial slope of the friction function defining a stiffness of the tire and one or combination of a peak friction, a shape factor and a curvature factor of the friction function. Upon estimating a slip and a stiffness of the tire, the system selects from the memory parameters of the friction function corresponding to the current stiffness of the tire, determines a control command using a value of the friction corresponding to the slip of the tire according to the friction function defined by the selected parameters, and submits the control command to an actuator of the vehicle.
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公开(公告)号：US11932262B2
公开(公告)日：2024-03-19
申请号：US17365338
申请日：2021-07-01
发明人： Rien Quirynen , Karl Berntorp
CPC分类号： B60W50/0097 , B60W50/06 , B60W60/001 , G05B13/048 , B60W2050/0052
摘要： Stochastic nonlinear model predictive control (SNMPC) allows to directly take uncertainty of the dynamics and/or of the system's environment into account, e.g., by including probabilistic chance constraints. However, SNMPC requires the approximate computation of the probability distributions for the state variables that are propagated through the nonlinear system dynamics. This invention proposes the use of Gaussian-assumed density filters (ADF) to perform high-accuracy propagation of mean and covariance information of the state variables through the nonlinear system dynamics, resulting in a tractable SNMPC approach with improved control performance. In addition, the use of a matrix factorization for the covariance matrix variables in the constrained optimal control problem (OCP) formulation guarantees positive definiteness of the full trajectory of covariance matrices in each iteration of any optimization algorithm. Finally, a tailored adjoint-based sequential quadratic programming (SQP) algorithm is described that considerably reduces the computational cost and allows a real-time feasible implementation of the proposed ADF-based SNMPC method to control nonlinear dynamical systems under uncertainty.
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公开(公告)号：US20240059317A1
公开(公告)日：2024-02-22
申请号：US17933210
申请日：2022-09-19
发明人： Rien Quirynen , Yebin Wang , Stefano Di Cairano , Ahmad Ahmad , Zejiang Wang , Akshay Bhagat , Eyad Zeino
IPC分类号： B60W60/00 , B60W10/18 , B60W30/09 , B60W30/095
CPC分类号： B60W60/0015 , B60W10/18 , B60W30/09 , B60W30/0956 , B60W30/0953 , B60W10/06
摘要： A control system for controlling movement of a vehicle, is disclosed. The control system is configured to construct a graph having multiple nodes defining states of the vehicle. The multiple nodes comprise an initial node defining initial state and a target node defining target state. Each pair of nodes is connected by an edge defined by collision-free motion primitives where the nodes comprise motion cusps. Multiple nodes connected through edges form a first path. A first number of motion cusps in the first path is determined and the graph is expanded to add new nodes until a termination condition is met on determining that the first number of motion cusps is above a threshold. The expansion of the graph is subjected to a constraint associated with a total number of motion cusps. Further a second path is determined having lesser motion cusps than the first path.
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公开(公告)号：US11840256B2
公开(公告)日：2023-12-12
申请号：US17389722
申请日：2021-07-30
IPC分类号： B60W60/00 , G05B13/02 , G05D1/00 , G05D1/02 , G08G1/0967
CPC分类号： B60W60/0015 , B60W60/00276 , G05B13/028 , G05D1/0088 , G05D1/0214 , G05D1/0274 , G08G1/096708 , G08G1/096783 , B60W2300/12 , B60W2420/42 , B60W2420/52 , B60W2520/06 , B60W2520/10 , B60W2520/12 , B60W2552/05 , B60W2552/10 , B60W2554/4026 , B60W2554/4029 , B60W2554/4041 , B60W2554/4042 , B60W2554/4043 , B60W2554/4044 , B60W2556/45
摘要： A global multi-vehicle decision making system is disclosed for providing real-time motion planning and coordination of one or multiple connected and automated and/or semi-automated vehicles (CAVs) in an interconnected traffic network that includes one or multiple non-controlled vehicles (NCVs), one or multiple conflict zones and one or multiple conflict-free road segments. The system includes a receiver configured to acquire infrastructure sensing signals, at least one memory configured to store map and programs, and at least one processor configured to perform steps of formulating a global mixed-integer programming (MIP) problem using the infrastructure sensing signals, computing a motion plan for each CAV and each NCV in the traffic network by solving the global MIP problem, computing an optimal sequence of entering/exiting times and a sequence of average velocities for each CAV and each NCV, and computing a velocity profile and/or one or multiple planned stops for each CAV.
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公开(公告)号：US20230096384A1
公开(公告)日：2023-03-30
申请号：US17489263
申请日：2021-09-29
发明人： Yuko OMAGARI , Junya Hattori , Tomoki Uno , Stefano Di Cairano , Rien Quirynen
摘要： A processor of a computing device comprises: a rearrangement unit to rearrange a plurality of elements included in each of a Hessian matrix of an evaluation function and a coefficient matrix of the linear constraint; a generation unit to generate a simultaneous linear equation for finding the optimal solution, based on the evaluation function including the rearranged Hessian matrix and the linear constraint including the rearranged coefficient matrix; and a search unit to find the optimal solution using the simultaneous linear equation. The rearrangement unit rearranges the plurality of elements so as to gather a sparse element of the plurality of elements included in the Hessian matrix, and rearranges the plurality of elements so as to gather a sparse element of the plurality of elements included in the coefficient matrix.
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公开(公告)号：US20220187793A1
公开(公告)日：2022-06-16
申请号：US17117159
申请日：2020-12-10
发明人： Karl Berntorp , Rien Quirynen , Sean Vaskov
IPC分类号： G05B19/4155 , G05D1/00
摘要： A stochastic model predictive controller (SMPC) estimates a current state of the system and a probability distribution of uncertainty of a parameter of dynamics of the system based on measurements of outputs of the system, and updates a control model of the system including a function of dynamics of the system modeling the uncertainty of the parameter with first and second order moments of the estimated probability distribution of uncertainty of the parameter. The SMPC determines a control input to control the system by optimizing the updated control model of the system at the current state over a prediction horizon and controls the system based on the control input to change the state of the system.
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公开(公告)号：US20210302974A1
公开(公告)日：2021-09-30
申请号：US16830601
申请日：2020-03-26
摘要： A control system for controlling a motion of a vehicle to a target driving goal uses a decision-maker configured to determine a sequence of intermediate goals leading to the next target goal by optimizing the motion of the vehicle subject to a first model and tightened driving constraints formed by tightening driving constraints by a safety margin, and uses a motion planner configured to determine a motion trajectory of the vehicle tracking the sequence of intermediate goals by optimizing the motion of the vehicle subject to the second model. The driving constraints include mixed logical inequalities of temporal logic formulae specified by traffic rules to define an area where the temporal logic formulae are satisfied, while the tightened driving constraints shrink the area by the safety margin, which is a function of a difference between the second model and the first model approximating the second model.
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