CN113104048B - Vehicle control system and method considering motion sickness of passengers in vehicle - Google Patents
Vehicle control system and method considering motion sickness of passengers in vehicle Download PDFInfo
- Publication number
- CN113104048B CN113104048B CN202110320366.7A CN202110320366A CN113104048B CN 113104048 B CN113104048 B CN 113104048B CN 202110320366 A CN202110320366 A CN 202110320366A CN 113104048 B CN113104048 B CN 113104048B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- motion
- state
- occupant
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0013—Planning or execution of driving tasks specially adapted for occupant comfort
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W2040/0872—Driver physiology
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Human Computer Interaction (AREA)
- Vehicle Body Suspensions (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The invention discloses a vehicle control system and method considering in-vehicle passenger motion, which comprises a vehicle motion state sensing module, an in-vehicle passenger state sensing module, a vehicle state estimation module, a passenger motion state estimation module, a constraint generation module, an optimization solution module and a vehicle action control module. The occupant motion sickness state is calculated. According to the original data acquired by each vehicle-mounted sensor, a state estimation algorithm is applied to estimate the motion state of the vehicle and the motion sickness state of passengers in the vehicle, and the state of the whole system is calculated by combining the vehicle position output by the upper sensing module. By means of system state constraint and system input constraint obtained by the planning generation module, optimal control is obtained by an optimal control method, and vehicle motion is controlled to achieve the aim of relieving the motion of passengers in the vehicle. The system has low cost and reliable performance, can be used for designing an automatic driving control algorithm considering the motion sickness of passengers in the vehicle, and improves the driving safety, the riding comfort and the user experience.
Description
Technical Field
The invention relates to an automatic driving vehicle technology, in particular to a vehicle control system and a method considering motion sickness of passengers in a vehicle.
Background
Carsickness is a manifestation of motion sickness, and symptoms of carsickness include sweating, dizziness, nausea, vomiting, and significantly affect the riding experience of occupants in a vehicle. A plurality of research studies at home and abroad show that most people have slight or severe carsickness when taking vehicles, and the carsickness can be considered as a common phenomenon.
With the development of an automatic driving technology, the automation level of a vehicle is gradually improved, the motion of the vehicle including acceleration, deceleration and steering can be controlled by an automatic driving system, in order to improve competitiveness and meet the expectation of a user on an advanced concept of an intelligent vehicle, travel services such as an unmanned taxi, an unmanned bus and the like should provide travel service experience provided by a human driver at present. Meanwhile, on the one hand, as a passenger, in addition to the basic requirement of "whether to reach the destination safely", the basic element for judging the quality of the service is the riding comfort; on the other hand, in a vehicle with advanced automatic driving, starting from the aspect of motion control of the vehicle, the possibility of reducing the incidence and severity of carsickness of passengers in the vehicle is provided.
Therefore, in order to improve the level of the automatic driving travel service, it is imperative to develop an automatic driving algorithm for improving the riding comfort of passengers in the vehicle. However, in the design of the existing measures for resisting carsickness, carsickness is relieved from the perspective of optimization of the ergonomics of the vehicle interior, including interior displays, seat arrangement, adjustment of the climate environment in the vehicle interior, or the design and control of the vehicle chassis suspension are initiated to improve vibrations, thereby improving the riding comfort. Starting from the aspect of motion control of vehicles, a motion control algorithm is designed by utilizing an occupant carsickness mechanism model, so that a carsickness relieving solution is not disclosed at present.
Disclosure of Invention
The invention aims to provide a vehicle control system and a method considering motion sickness of an occupant in a vehicle, aiming at the defects of the prior art.
The purpose of the invention is realized by the following technical scheme: a vehicle control system considering in-vehicle occupant motion comprises a vehicle motion state sensing module, a vehicle state estimation module, an in-vehicle occupant state sensing module, an occupant motion state estimation module, a constraint generation module, an optimization solution module, a vehicle action control module and the like.
The vehicle motion state sensing module measures and estimates the current t of the vehicle through a vehicle-mounted sensor 0 Temporal motion state information x v (t 0 ) Including the vehicle acceleration vector a (t) 0 ) Angular velocity vector ω (t) 0 ) And vehicle speed v (t) 0 )。
The vehicle state estimation module comprises a vehicle model MV and receives the current t 0 Temporal vehicle motion state information x v (t 0 ) And the current position information x of the vehicle obtained by the upper sensor module p (t 0 ) Combining a control output sequence u (t) output in the iterative process of an optimization solving module, and t e [ t ∈ [ [ t ] 0 ，t 0 +T p ]For predicting future T p Vehicle motion state x over a period of time v (t) and position x p (t)。
The in-vehicle passenger state sensing module acquires the current t through an in-vehicle non-contact sensor comprising an in-vehicle camera and a seat pressure distribution sensor 0 The relative attitude G (t) between the occupant and the vehicle at the time 0 ) Extracting the physiological information index b (t) which can be used for representing the motion sickness of the passengers at the current t0 moment through a contact type physiological information sensor in the vehicle, including electrocardio, skin electricity and body temperature sensors 0 ) And estimating a motion sickness state x of an occupant in the vehicle m (t 0 )。
The passenger motion sickness state estimation module comprises a passenger motion sickness model MP for representing the motion sickness state of passengers in the vehicle, and receives the motion sickness state x of the passengers in the vehicle m (t 0 ) Combined with predicted future T of the vehicle p Vehicle motion state x over a period of time v (t) and the current relative posture G (t) between the occupant and the vehicle 0 ) Predicting future T p In-vehicle occupant motion sickness state x within a time period m (t)；
The constraint generation module receives t 0 Current position x of vehicle at time p (t 0 ) And a vehicle passable area, obtaining a vehicle position constraint g (x) p ) Vehicle motion state constraint h (x) in combination with vehicle motion limits v ) And a constraint k (u) of the vehicle motion control amount;
the optimization solving module is used for designing and optimizing an objective function J according to an objective for weakening the motion sickness of passengers, receiving the outputs of a vehicle model MV and a passenger motion sickness model MP and restraining g (x) p )、h(x v ) And k (u), continuously and iteratively calculating future T by using an optimization control algorithm p Controlling quantity sequence u (t) in time interval until obtaining optimal control sequence u * (t) and outputting the optimal control u at the current time * (t 0 ) Optimum target longitudinal acceleration
And optimum target steering wheel angle delta * (t 0 )。
The vehicle action control module receives the optimal control quantity u output by the optimization solving module * (t 0 ) The motion execution system for controlling the vehicle comprises a driving subsystem, a braking subsystem and a steering subsystem, and corresponding control instructions comprising a driving pedal opening degree signal, a braking pedal opening degree signal and a steering driving motor target torque are output according to response characteristics of the subsystems to realize the aim of controlling the u * (t 0 ) Accurate tracking of.
Further, in the occupant motion sickness state estimation module, the occupant motion sickness model MP is a mathematical model based on a motion sickness generation mechanism or an empirical mathematical model.
Further, in the vehicle state estimation module, the vehicle model MV is expressed by using a discrete nonlinear differential equation system for x at any time t v (t) and x p (t) all satisfy:
where Δ t is the calculation time step.
Further, in the occupant motion sickness state estimation module, t is assumed 0 The relative attitude between the occupant and the vehicle after time t is unchanged for 0 At any time t after time, x m All satisfy:
x m (t+△t)＝f m (x v (t)，x m (t)，G(t 0 ))。
further, in the optimization solving module, the optimization control algorithm specifically includes the following sub-steps:
a) Simultaneous state x p 、x v 、x m Obtaining a system state x, a simultaneous equation set f v 、f m Obtaining a system state differential equation set f at the current t 0 After the moment, any time t has
x(t+△t)＝f(x(t)，u(t)，G(t 0 ))
b) Optimization target J considering track tracking effect simultaneously v Optimization target J considering control energy u And an optimization objective J that takes into account occupant motion sickness m To which a weight w is added 1 ，w 2 ，w 3 And obtaining an optimized objective function J:
J＝w 1 J v +w 2 J u and w 3 J m
c) The optimal control problem is described as follows:
x(t+△t)＝f(x(t)，u(t)，G(t 0 ))
s.t.g(x p )≤0，h(x v )≤0，k(u)≤0。
further, in step b), J v 、J u 、J m The method specifically comprises the following steps:
wherein x is p，ref Is a reference position, v, on the target trajectory P ref Is a reference speed, Q, on the target trajectory P p ，Q v ，Q u ，Q m Is a weight matrix; the occupant motion sickness model MP is a subjective vertical collision model based on a motion sickness generation mechanism, and delta V is a subjective vertical collision vector.
Further, in the occupant motion sickness state estimation module, when the vehicle does not have a seat pressure distribution sensor, the relative posture between the occupant in the vehicle and the vehicle is calculated and obtained only by data collected by the camera in the vehicle. When the passenger physiological information index b (t) can not be obtained 0 ) Time, motion sickness state x of passengers in the vehicle at any time t m (t) passing only the system of differential equations f m The result of the iteration is that,iterative initial value x m (0)＝0。
A vehicle control method considering the motion sickness of passengers in a vehicle based on the system comprises the following steps:
(1) The vehicle motion state sensing module and the in-vehicle passenger state sensing module are used for estimating t by using a state estimation algorithm according to the original data acquired by each vehicle-mounted sensor 0 Vehicle movement state x of time v (t 0 ) And a state x of motion sickness of the occupant in the vehicle m (t 0 ) And combined with t 0 Vehicle position x of time p (t 0 ) Obtaining t 0 Time of day overall system state quantity
And the relative posture G (t) between the occupant and the vehicle 0 )；
(2) Simultaneously establishing equations of a vehicle model MV and an occupant motion model MP to obtain a system differential equation set and dispersing
x(t+△t)＝f(x(t)，u(t)，G(t 0 ))；
(3) Obtaining a system state constraint g (x) by a constraint generating module p )、h(x v ) And a system input constraint k (u);
(4) In the optimization solving module, an optimization control method is used and predicted x is combined v (t)、x p (t) and x m (T), solving for T p In the time domain, the optimal control sequence u satisfying the set constraint of the step (3) * (t) minimizing the optimization objective function J and outputting t 0 Optimal control of time u * (t 0 )；
(5) In the vehicle action control module, according to the response characteristics of each execution subsystem of the vehicle, a proper control command is calculated, and the optimal control u is tracked * (t 0 )；
(6) And (5) re-running the steps (1) to (5) every time delta t is carried out until the control system is closed.
A computer-readable storage medium storing a computer program which, when executed, implements the steps of the above-described vehicle control method.
A vehicle includes the above vehicle control system.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention can realize the control of the vehicle motion through an optimized control algorithm. The motion state of the vehicle is optimized through an optimal control means, and the stimulation of the weakened motion to passengers in the vehicle can reduce the probability of motion sickness of the passengers, weaken the motion sickness feeling of the passengers and ensure the riding comfort of the passengers while finishing the driving task;
2. compared with the prior art, the prior art usually optimizes the vehicle motion control algorithm by utilizing the motion impact degree (namely acceleration derivative, jerk), and cannot ensure the improvement of the passenger carsickness phenomenon;
3. when the device is used by passengers with importance on privacy and freedom degree, the camera can be turned off, the wearing of physiological information acquisition equipment is stopped, the relative postures G (t) of the passengers and the vehicle are obtained only through the seat pressure distribution sensor, and the motion sickness state x of the passengers in the vehicle at any time t is obtained through the passenger motion sickness state estimation algorithm m And (t) the privacy right and the riding freedom degree of passengers are better ensured.
Drawings
FIG. 1 is a schematic diagram of a vehicle trajectory tracking condition;
FIG. 2 is a schematic diagram of a control system;
FIG. 3 is a schematic diagram of an occupant state sensing module;
in the figure: steering wheel 1, camera 2, seat pressure sensor 4, physiological information sensor 3.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The invention relates to a vehicle control system and a method considering motion sickness of passengers in a vehicle, which take general track tracking as a sample scene and explain the specific implementation mode of the invention content. Trajectory tracking scenarios as shown in fig. 1, a controlled vehicle C travels on a road plane, and the vehicle motion attitude is controlled by a control system. A passable area A exists on the road plane, a vehicle target running track P exists in the area A, and the track P comprises position information and speed information. The control object of the present invention is to control the vehicle to travel along the trajectory P, that is, to control the vehicle to travel along the target position at the target vehicle speed. The controlled vehicle C is provided with a drive-by-wire chassis system, can receive command output of a control system, comprises a driving pedal opening signal, a braking pedal opening signal and a steering motor torque signal, and realizes control of a power driving system, a braking system and a steering system. When the control system is deployed on different vehicles, the signals output by the control system to the vehicles are correspondingly adjusted according to specific conditions of the vehicles.
As shown in fig. 2, the present embodiment provides a vehicle including a vehicle control system considering motion sickness of an occupant in the vehicle, and the system includes a vehicle motion state sensing module, a vehicle state estimation module, an occupant in the vehicle state sensing module, an occupant motion sickness state estimation module, a constraint generation module, an optimization solution module, and a vehicle motion control module. Wherein, the system also receives the current t output by the upper sensing module of the automatic driving system 0 Time-of-day vehicle position information x p (t 0 ) A vehicle passable area A and a target running track P. Wherein the vehicle position x p ＝[XYθ] T Including the coordinates (X, Y) of the vehicle currently in the geodetic coordinate system and the heading angle theta.
(1) The vehicle motion state sensing module measures the current t of the vehicle through a vehicle-mounted sensor comprising an IMU, a wheel speed meter and a steering wheel steering angle sensor 0 Temporal motion state information x v (t 0 ) Including the vehicle acceleration vector a (t) 0 ) Angular velocity vector ω (t) 0 ) And vehicle speed v (t) 0 )：
x v ＝[a x a y a z ω x ω y ω z v] T
Wherein v is the vehicle speed, a x ，a y ，a z Longitudinal acceleration, lateral acceleration and vertical acceleration, omega, of the vehicle x ，ω y ，ω z The superscript "T" refers to the matrix transpose for vehicle roll angular velocity, pitch angular velocity, and yaw angular velocity.
(2) The vehicle state estimation module is internally provided with a vehicle model MV; receiving the current t output by the vehicle motion state sensing module 0 Temporal vehicle motion state information x v (t 0 ) And the current position information x of the vehicle obtained by the upper sensing module p (t 0 ) Combining the control quantity sequence u (t) output in the iterative process of the optimization solving module of the control system, and t E [ t ∈ [ [ t ] 0 ，t 0 +T p ]For predicting future T p Vehicle motion state x over a period of time v (t) and position x p (t)，t∈[t 0 ，t 0 +T p ]. Wherein the vehicle model MV is expressed by a discrete nonlinear differential equation system for x at any time t v (t) and x p (t) all satisfy:
where Δ t is the calculation time step.
(3) As shown in fig. 3, the in-vehicle occupant state sensing module is mounted in an actual vehicle, and is provided with a proximity sensor and a contact sensor in the vehicle, taking the detection of motion sickness of a front passenger as an example. The non-contact sensor comprises an in-vehicle passenger detection camera 2 and a seat pressure sensor 4; the visible range of the camera 2 includes the upper body and the head of the passenger; the seat pressure sensor 4 is an array pressure sensor for detecting the pressure distribution of the seat surface when a passenger is seated. The physiological information sensor 3 is a contact sensor, and the passenger needs to wear a corresponding physiological signal acquisition device, such as an electrocardio electrode, a skin electrode and a body temperature contact.
With the camera 2 as a main sensor, a visual-based face direction detection algorithm is deployedAnd a joint detection algorithm that detects a relative pose between the occupant and the camera. The seat pressure sensor 4 is used as an auxiliary sensor, and the center of gravity position of the occupant is estimated from the seat pressure distribution. Combining the above information, estimating the current t 0 Relative posture G (t) between occupant and vehicle in vehicle at time 0 ). On the other hand, according to the existing research results, the current t detected by the physiological information sensor 3 is combined 0 Time-of-day occupant physiological information index b (t) 0 ) Estimating a motion sickness state x of an occupant in the vehicle m (t 0 )。
(4) An occupant motion sickness state estimation module for receiving the motion sickness state x of the occupant in the vehicle output by the occupant state sensing module in the vehicle m (t 0 ) And the future T of the vehicle predicted by the vehicle state estimation module p Vehicle motion state x over a period of time v (t)，t∈[t 0 ，t 0 +T p ]And the current relative posture G (t) between the occupant and the vehicle in the vehicle, combined with the output of the non-contact sensor 0 ) Predicting the in-vehicle occupant motion sickness state x in the future T period by using the built-in occupant motion sickness model MP m (t)，t∈[t 0 ，t 0 +T p ]。
The mathematical models currently used to describe occupant motion sickness include two broad classes, one based on the mechanism by which motion is generated and the other based on experience. Any mathematical model for describing motion sickness can be used as an occupant motion sickness model MP, and different mathematical models are used for representing different parameters of the occupant motion sickness state, namely the motion sickness state x of the occupant in the vehicle m The specific parameters in (a) are associated with the selected model MP. In the embodiment, a subjective vertical collision model based on a motion sickness generation mechanism is selected as the model MP. The model MP is written as a discrete nonlinear system of differential equations using mathematical means.
T cannot be predicted because the body movement of the passenger in the vehicle is not controlled by the vehicle 0 After the time, the relative attitude between the occupant and the vehicle, so t is assumed 0 The relative attitude between the occupant and the vehicle after time t is unchanged for 0 At any time t after time, x m All satisfy:
x m (t+△t)＝f m (x v (t)，x m (t)，G(t 0 ))
particularly, when the vehicle is not provided with a seat pressure distribution sensor, the relative posture of passengers in the vehicle between the vehicles can be obtained by calculating only data collected by a camera in the vehicle; the in-vehicle passenger state perception module can not obtain the passenger physiological information index b (t) 0 ) Time, motion sickness state x of passengers in the vehicle at any time t m (t) can be obtained by iteration of only the above-mentioned system of differential equations, the initial value x of which is iterated m (0)＝0。
(5) A constraint generation module for receiving the current position x of the vehicle calculated by the upper layer perception module p (t 0 ) And a vehicle passable area A, obtaining a vehicle position constraint g (x) p ) Including vehicle lateral position limit Y m And a longitudinal position limit X m . Generating a vehicle motion state constraint h (x) from vehicle motion limits v ) Maximum longitudinal acceleration axm, maximum longitudinal acceleration rate of change
Maximum lateral acceleration aym, maximum rate of change of lateral acceleration
And the maximum yaw rate ω m And the maximum yaw rate change rate
Generating a controlled quantity constraint k (u) including a maximum target longitudinal acceleration based on the drive-by-wire chassis characteristics
And maximum target steered wheel steering angle
(6) An optimization solution module for designing an optimization objective function J based on an optimization objective for reducing occupant motion sickness and receiving x output by a vehicle model MV (vehicle state estimation module) p (t)、x v (t)，X output by passenger motion sickness model MP (passenger motion state estimation module) m (t), and a constraint g (x) output by the constraint generating module p )、h(x v ) And k (u); continuously iteratively calculating future T using an optimal control algorithm p Controlling quantity sequence u (t) in time interval until obtaining optimal control sequence u * (t) and outputs the optimum control u at the present time * (t 0 ) Including the optimum target longitudinal acceleration
And optimum target steering wheel angle delta * (t 0 )。
The vehicle state estimation module, the occupant motion sickness state estimation module, the constraint generation module and the optimization solution module jointly realize an optimization control algorithm. And in the iterative process of the optimization solution, continuously transmitting data until an optimal control sequence is obtained by calculation. The specific flow of calculation of the optimization control algorithm is as follows.
6.1 ) simultaneous state x p 、x v 、x m Obtaining a system state x, a simultaneous equation set f v 、f m Obtaining a system state differential equation set f at the current t 0 After the moment, any time t has
x(t+△t)＝f(x(t)，u(t)，G(t 0 ))
6.2 Design optimization objective function J. The optimization objective function is divided into three parts, namely an optimization objective J considering the track tracking effect v Optimization target J considering control energy u And an optimization objective J that takes into account occupant motion sickness m 。
When the actual train-linked motion track is close to the target track, the track tracking effect is better, so that
Wherein x is p，ref Is a reference position, v, on the target trajectory P ref Is a reference speed, Q, on the target trajectory P p And Q v Is a weight matrix.
To ensure that the energy required for control is minimal, then
Wherein Q u Is a weight matrix.
The MP model selected by the embodiment uses the acceleration vector a of the head of the passenger h And angular velocity vector ω h As an input, a subjective vertical collision vector Δ V is calculated and the cumulative Δ V over time is mapped to the motion sickness incidence MSI, characterizing the level of motion sickness of the occupant. Since MSI is only related to Δ V, the time that the occupant is in the vehicle, Δ V is considered a specific indication of the level of motion sickness of the occupant in the vehicle. Thus, the optimization objective function J for the motion sickness state of the passenger in the vehicle is considered m The definition is as follows:
wherein Q is m Is a weight matrix.
Simultaneous J v 、J u 、J m Weight w to the three parts 1 ，w 2 ，w 3 And obtaining a total objective function J of system optimization control:
J＝w 1 J v +w 2 J u +w 3 J m
6.3 The optimal control problem is described as follows:
x(t+△t)＝f(x(t)，u(t)，G(t 0 ))
s.t.g(x p )≤0，h(x v )≤0，k(u)≤0
6.4 Using an optimization method, iteratively calculating T p Control quantity sequence u (t) in time domain until obtaining optimal control sequence u * (t) and outputting the first step u * (t 0 ) As the current t 0 And outputting the control quantity of the moment to a vehicle action control module.
(7) A vehicle motion control module for receiving the current t output by the optimization solving module 0 Optimum control amount u of time * (t 0 ) Combining the characteristics of the drive-by-wire chassis to optimize the target longitudinal acceleration
And optimum target steering wheel angle delta * And (t 0) converting the signal into a driving pedal opening signal, a brake pedal opening signal and a steering motor torque signal which can be received by the drive-by-wire chassis, and finally finishing control.
The embodiment provides a vehicle control method considering motion sickness of an occupant in a vehicle, which comprises the following steps:
(1) The vehicle motion state sensing module and the in-vehicle passenger state sensing module are used for estimating t by using a state estimation algorithm according to the original data acquired by each vehicle-mounted sensor 0 Vehicle movement state x of time v (t 0 ) And a state x of motion sickness of the occupant in the vehicle m (t 0 ) And combining t output by the upper sensing module 0 Vehicle position x of time p (t 0 ) Obtaining t 0 Time of day, overall system state quantity
And the relative posture G (t) between the occupant and the vehicle 0 )；
(2) Simultaneously establishing equations of a vehicle model MV and an occupant motion model MP to obtain a system differential equation set and dispersing
x(t+△t)＝f(x(t)，u(t)，G(t 0 ))；
(3) Obtaining a system state constraint g (x) by a constraint generating module p )、h(x v ) And a system input constraint k (u);
(4) In the optimization solving module, an optimization control method is used, preferably a prediction control method is used, and x predicted by the vehicle state estimation module and the occupant motion sickness state estimation module is combined v (t)、x p (t) and x m (T), solving for T p In the time domain, the optimal open-loop control sequence u meeting the set constraint of the step (3) * (t) minimizing the optimization objective function J and outputting t 0 Optimal control of time u * (t 0 )；
(5) In the vehicle action control module, according to the response characteristics of each execution subsystem of the vehicle, a proper control command is calculated, and the optimal control u is tracked * (t 0 )；
(6) And (5) re-running the steps (1) to (5) every time delta t is carried out until the control system is closed.
The present invention also provides a computer-readable storage medium storing a computer program which, when executed, performs the above-described operational steps.
Claims (10)
1. A vehicle control system considering in-vehicle passenger motion is characterized by comprising a vehicle motion state sensing module, a vehicle state estimation module, an in-vehicle passenger state sensing module, a passenger motion state estimation module, a constraint generation module, an optimization solution module and a vehicle action control module;
the vehicle motion state sensing module measures and estimates the current t of the vehicle through a vehicle-mounted sensor 0 Temporal motion state information x v (t 0 ) Including the vehicle acceleration vector a (t) 0 ) Angular velocity vector ω (t) 0 ) And a vehicle speed u (t) 0 )；
The vehicle state estimation module comprises a vehicle model MV and receives the current t 0 Temporal vehicle motion state information x v (t 0 ) And the current position information x of the vehicle obtained by the upper sensor module p (t 0 ) Combining a control quantity sequence u (t) output in the iterative process of the optimization solving module, and t e [ t ∈ [ [ t ] 0 ，t 0 +T p ]For predicting future T p Vehicle motion state x over a period of time v (t) and position x p (t)；
The in-vehicle passenger state sensing module acquires through in-vehicle non-contact sensors including in-vehicle cameras and seat pressure distribution sensorsCurrent t 0 The relative attitude G (t) between the occupant and the vehicle at the time 0 ) Extracting the current t through a contact type physiological information sensor in the vehicle, including electrocardio, skin electricity and body temperature sensors 0 The time of day can be used as a physiological information index b (t) for representing the motion sickness of the passenger 0 ) And estimating a motion sickness state x of an occupant in the vehicle m (t 0 )；
The passenger motion-sickness state estimation module comprises a passenger motion-sickness model MP for representing the motion-sickness state of passengers in the vehicle, and receives the motion-sickness state x of the passengers in the vehicle m (t 0 ) In combination with predicted future T of the vehicle p Vehicle motion state x over a period of time v (t) and the current relative posture G (t) between the occupant and the vehicle 0 ) Predicting future T p In-vehicle occupant motion sickness state x within a time period m (t)；
The constraint generation module receives t 0 Current position x of vehicle at time p (t 0 ) And a vehicle passable area, obtaining a vehicle position constraint g (x) p ) Vehicle motion state constraint h (x) in combination with vehicle motion limits v ) And a constraint k (u) of the vehicle motion control amount;
the optimization solution module, which designs an optimization objective function J according to an objective for reducing occupant motion sickness, receives outputs of a vehicle model MV and an occupant motion model MP, and constrains g (x) p )、h(x v ) And k (u), using an optimization control algorithm to continuously calculate the future T in an iterative manner p Controlling quantity sequence u (t) in time interval until obtaining optimal control sequence u * (t) and outputs the optimum control u at the present time * (t 0 ) Optimum target longitudinal acceleration
And optimum target steering wheel angle delta * (t 0 )；
The vehicle action control module receives the optimal control u output by the optimization solving module * (t 0 ) Controlling a motion actuation system of a vehicle, the motion actuation system comprising a drive subsystem, a brake subsystem and a steering subsystem, in dependence on the respective subsystemResponding to the characteristic, outputting corresponding control instructions including a driving pedal opening degree signal, a braking pedal opening degree signal and a steering driving motor target torque, and realizing the aim of u * (t 0 ) Accurate tracking of.
2. The vehicle control system that takes into account occupant motion sickness as set forth in claim 1, wherein the occupant motion sickness state estimation module is characterized in that the occupant motion sickness model MP is a mathematical model based on a motion sickness generation mechanism or an empirically based mathematical model.
3. The vehicle control system that accounts for occupant motion sickness of claim 1, wherein the vehicle state estimation module wherein the vehicle model MV is represented using a discrete nonlinear system of differential equations for x at any time t v (t) and x p (t) all satisfy:
where Δ t is the calculation time step.
4. The vehicle control system that accounts for occupant motion sickness of claim 3, wherein in the occupant motion sickness state estimation module, t is assumed 0 The relative attitude between the occupant and the vehicle after time t is unchanged for 0 At any time t after time, x m All satisfy:
x m (t+Δt)＝f m (x v (t)，x m (t)，G(t 0 ))。
5. the vehicle control system that accounts for occupant motion sickness as set forth in claim 4, wherein the optimization solution module wherein the optimization control algorithm specifically includes the sub-steps of:
a) Simultaneous state x p 、x v 、x m Obtaining the system state x, simultaneous equations set f v 、f m Obtaining a system stateSystem of differential equations f at the current t 0 After the moment, any time t has
x(t+Δt)＝f(x(t)，u(t)，G(t 0 ))
b) Optimization target J considering trajectory tracking effect simultaneously v Optimization target J considering control energy u And an optimization objective J that takes into account occupant motion sickness m Weighted by a weight w 1 ，w 2 ，w 3 And obtaining an optimized objective function J:
J＝w 1 J v +w 2 J u +w 3 J m
c) The optimal control problem is described as follows:
x(t+Δt)＝f(x(t)，u(t)，G(t 0 ))
s.t.g(x p )≤0，h(x v )≤0，k(u)≤0。
6. the vehicle control system that accounts for motion sickness in an occupant of claim 5, wherein in step b), J v 、J u 、J m The method specifically comprises the following steps:
wherein x is p，ref Is a reference position, v, on the target trajectory P ref Is a reference speed, Q, on the target trajectory P p ，Q v ，Q u ，Q m Is a weight matrix; the occupant motion sickness model MP is a subjective vertical collision model based on a motion sickness generation mechanism, and the delta V is a subjective vertical collision vector.
7. The vehicle control system considering the motion sickness of the occupant in the vehicle as set forth in claim 4, wherein in the occupant motion sickness state estimation module, when the vehicle is not provided with the seat pressure distribution sensor, the relative attitude between the occupant in the vehicle and the vehicle is calculated only by data collected by the camera in the vehicle; when the passenger physiological information index b (t) can not be obtained 0 ) Time, motion sickness state x of passengers in the vehicle at any time t m (t) passing only the system of differential equations f m Obtaining by iteration, iteration initial value x m (0)＝0。
8. A vehicle control method considering motion sickness of an occupant in a vehicle based on the system according to any one of claims 1 to 7, characterized by the steps of:
(1) The vehicle motion state sensing module and the in-vehicle passenger state sensing module are used for estimating t by using a state estimation algorithm according to the original data acquired by each vehicle-mounted sensor 0 Vehicle movement state x of time v (t 0 ) And a state x of motion sickness of the occupant in the vehicle m (t 0 ) In combination with t 0 Vehicle position x of time p (t 0 ) Obtaining t 0 Time of day overall system state quantity
And the relative posture G (t) between the occupant and the vehicle 0 )；
(2) Simultaneously establishing equations of a vehicle model MV and an occupant motion model MP to obtain a system differential equation set and dispersing
x(t+Δt)＝f(x(t)，u(t)，G(t 0 ))；
(3) Obtaining a system state constraint g (x) by a constraint generating module p )、h(x v ) And a system input constraint k (u);
(4) In the optimization solving module, an optimization control method is used and combinedPredicted x v (t)、x p (t) and x m (T), solving for T p In the time domain, the optimal control sequence u satisfying the set constraint of the step (3) * (t) minimizing the optimization objective function J and outputting t 0 Optimal control of time u * (t 0 )；
(5) In the vehicle action control module, according to the response characteristics of each execution subsystem of the vehicle, a proper control command is calculated, and the optimal control u is tracked * (t 0 )；
(6) And (5) re-running the steps (1) to (5) every time delta t is carried out until the control system is closed.
9. A computer-readable storage medium storing a computer program which, when executed, performs the steps of the method of claim 8.
10. A vehicle comprising a system according to any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110320366.7A CN113104048B (en) | 2021-03-25 | 2021-03-25 | Vehicle control system and method considering motion sickness of passengers in vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110320366.7A CN113104048B (en) | 2021-03-25 | 2021-03-25 | Vehicle control system and method considering motion sickness of passengers in vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113104048A CN113104048A (en) | 2021-07-13 |
| CN113104048B true CN113104048B (en) | 2022-11-08 |
Family
ID=76712123
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110320366.7A Active CN113104048B (en) | 2021-03-25 | 2021-03-25 | Vehicle control system and method considering motion sickness of passengers in vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113104048B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113960329B (en) * | 2021-09-30 | 2023-11-07 | 瞬联软件科技（北京）有限公司 | Indication device and indication method for riding comfort |
| CN113741201B (en) * | 2021-10-18 | 2023-12-05 | 吉林大学 | Intelligent control method for actively relieving motion sickness applied to chassis control layer |
| CN115991186B (en) * | 2023-03-06 | 2023-08-11 | 郑州轻工业大学 | Longitudinal and transverse control method for anti-carsickness automatic driving vehicle |
| CN116130086B (en) * | 2023-04-20 | 2024-05-17 | 湖南大学 | Motion sickness prediction system |
| CN116726388B (en) * | 2023-06-15 | 2024-03-26 | 湖南大学 | Anthropomorphic feedback active space-time self-balancing vestibular stimulation system |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102016204635A1 (en) * | 2016-03-21 | 2017-09-21 | Ford Global Technologies, Llc | Method and device for avoiding or mitigating travel sickness in a vehicle |
| CN108639061B (en) * | 2018-04-16 | 2020-06-02 | 浙江工业大学 | Active anti-carsickness auxiliary driving control method for automatic driving vehicle |
| US10926773B2 (en) * | 2019-05-10 | 2021-02-23 | Denso International America, Inc. | Systems and methods for mitigating motion sickness in a vehicle |
| CN110155072B (en) * | 2019-06-06 | 2020-08-18 | 中国联合网络通信集团有限公司 | Carsickness prevention method and carsickness prevention device |
| CN110320916A (en) * | 2019-07-24 | 2019-10-11 | 上海交通大学 | Consider the autonomous driving vehicle method for planning track and system of occupant's impression |
| CN112109735A (en) * | 2020-10-22 | 2020-12-22 | 苏州智行众维智能科技有限公司 | Intelligent driving control system and method considering vehicle comfort |
| CN112362356B (en) * | 2020-11-02 | 2021-08-10 | 吉林大学 | Intelligent vehicle braking and parking capacity testing method considering passenger comfort |
-
2021
- 2021-03-25 CN CN202110320366.7A patent/CN113104048B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113104048A (en) | 2021-07-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113104048B (en) | Vehicle control system and method considering motion sickness of passengers in vehicle | |
| US9796421B1 (en) | Autonomous vehicle lateral control for path tracking and stability | |
| WO2018072394A1 (en) | Intelligent vehicle safety driving envelope reconstruction method based on integrated spatial and dynamic characteristics | |
| CN111208813A (en) | Dynamics prediction control system and method for automatic driving vehicle | |
| EP3674156A1 (en) | Method and device for generating target path for autonomous vehicle | |
| Hang et al. | Cooperative control framework for human driver and active rear steering system to advance active safety | |
| JP2007022117A (en) | Vehicle stabilization control system | |
| Ali et al. | Predictive prevention of loss of vehicle control for roadway departure avoidance | |
| CN111439261B (en) | Traffic flow calculation system for intelligent vehicle group active lane change function | |
| US20230271470A1 (en) | Vehicle active suspension control system and method | |
| US20220242449A1 (en) | Vehicle Control System | |
| CN113635893A (en) | Urban intelligent traffic-based unmanned vehicle steering control method | |
| EP3904186A1 (en) | Vehicle travel control device | |
| JP7135978B2 (en) | Arithmetic unit for automobile | |
| US20230271469A1 (en) | Vehicle active suspension control system and method | |
| CN112937551B (en) | Vehicle control method and system considering input characteristics of driver | |
| CN116476588A (en) | Control method and system for active suspension of vehicle | |
| CN114555406B (en) | Model-based predictive regulation of an electric machine of a motor vehicle powertrain | |
| US20230331056A1 (en) | Vehicle active suspension control system and method | |
| CN113741201A (en) | Intelligent control method applied to chassis control layer and used for actively relieving motion sickness | |
| WO2023145170A1 (en) | Vehicle integrated control device and vehicle integrated control method | |
| CN110888419A (en) | Driving control method and system suitable for scooter | |
| WO2023166777A1 (en) | Vehicle control device and vehicle control method | |
| JP7461756B2 (en) | Vehicle control device and vehicle control method | |
| US20230294472A1 (en) | Vehicle active suspension control system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |