CN-116691675-B - Automobile efficient braking system based on road surface adhesion condition hybrid identification

Abstract

The invention discloses an automobile high-efficiency braking system based on mixed recognition of road surface attachment conditions, which comprises a road surface recognition module, an execution mechanism subsystem, a sensor subsystem and an entire automobile integrated planning controller, wherein the road surface recognition module primarily recognizes current road surface attachment information to obtain an estimated initial value of the road surface attachment coefficient, the entire automobile integrated planning controller acquires vehicle state information through the sensor subsystem, performs iterative optimization through an extended Kalman filter in the road surface recognition module by using different recognition strategies, and outputs estimated road surface attachment information and vehicle running states, and the execution mechanism control line control braking system is used for realizing high-efficiency braking. The invention can realize the advance cognition of the road surface adhesion information in front, thereby realizing the efficient braking of the intelligent automobile, and further solving the expected functional safety problem caused by the cognition lag of the road surface adhesion information.

Inventors

• HU HUACONG
• BAI XIANXU
• Duan Shunchang
• LI WEIHAN
• LI JIACHENG
• SUN JUN
• SHI QIN

Assignees

• 合肥工业大学

Dates

Publication Date

20260505

Application Date

20230421

Claims (2)

1. 1. The automobile high-efficiency braking system based on the mixed recognition of the road surface attachment condition is characterized by comprising a road surface recognition module, an execution mechanism subsystem, a sensor subsystem and a whole automobile integrated planning controller; the road surface identification module comprises a vehicle-mounted sensing camera module and a road surface type identification system; the sensing camera module is used for collecting road surface environment information in real time and transmitting the road surface environment information to the road surface type identification system; The road surface type identification system compares the acquired road surface environment information with various typical original road surfaces to determine the current road surface type and transmits the current road surface type to the machine learning training test system, wherein each road surface type represents an estimated interval of different road surface adhesion coefficients; the machine learning training test system calculates a road surface adhesion coefficient estimation interval corresponding to the current road surface type, obtains an estimated initial value of the road surface adhesion coefficient and sends the estimated initial value to the whole vehicle integrated planning controller; the actuating mechanism subsystem comprises a driving motor and a vehicle brake actuator; The sensor subsystem is connected with the whole vehicle integrated programming controller and comprises a steering angle sensor, a steering wheel moment sensor, an electromagnetic wheel speed sensor, a yaw rate sensor, a braking pressure sensor, an acceleration sensor and a wheel speed sensor; the steering angle sensor is used for acquiring the steering state and the steering angle of the steering wheel in real time; the steering wheel moment sensor is used for detecting the total resistance moment/aligning moment of the steering system in real time; the electromagnetic wheel speed sensor is used for acquiring a pulse signal sent out when the wheel rotates in real time to acquire the rotation speed of the driving wheel; the GPS sensor is used for collecting the running longitudinal speed of the vehicle in real time; The yaw rate sensor is used for collecting the yaw rate at the centroid of the vehicle in real time; The brake pressure sensor is used for acquiring a vehicle braking state in real time and acquiring the pressure of a brake cylinder in the brake-by-wire mechanism, wherein the vehicle braking state is a working state of a brake when a vehicle encounters an emergency, and is divided into a half braking state and a full braking state according to the pressure change of a master cylinder; The acceleration sensor is used for acquiring the lateral acceleration and the longitudinal deceleration of the vehicle in real time; the wheel speed sensor is used for obtaining tire angular speed information in real time; The decision layer of the whole vehicle integrated planning controller monitors the steering and braking states of the vehicle in real time according to the information states fed back by the steering angle sensor and the braking pressure sensor, adopts self-adaptive extended Kalman filtering to estimate the running state parameters of the vehicle at the next moment so as to iteratively update the running state parameters of the vehicle at the current moment, and inputs the running state parameters updated in real time into an internal road surface adhesion coefficient estimator so as to identify and update the road surface adhesion coefficient according to the estimated initial value; the decision layer of the whole vehicle integrated planning controller calculates the control quantity at the current moment according to the vehicle running state at the current moment and the road adhesion coefficient; and the execution layer of the whole vehicle integrated planning controller gives corresponding control instructions to the execution mechanism subsystem according to the control quantity at the current moment, so that the real-time braking of the vehicle is realized.
2. 2. The vehicle high-efficiency braking system based on road surface adhesion mixed recognition of claim 1, wherein the vehicle integrated planning controller realizes real-time braking of the vehicle according to the following steps; Step 1, a decision layer of the whole vehicle integrated planning controller judges whether the vehicle has steering operation currently according to the acquired steering angle of the steering wheel, if so, the decision layer executes a vehicle state and road surface adhesion coefficient identification strategy based on steering correction moment and then executes the step 2; Step 2, a decision layer of the whole vehicle integrated planning controller converts a steering angle of a steering wheel measured by a steering angle sensor into a front wheel steering angle, the front wheel steering angle is used as a control input quantity, the measured yaw rate and the lateral acceleration are used as observables, and the observables are input into an adaptive extended Kalman filter observer together for processing, so that running state parameters of a vehicle at the current moment after filtering are obtained; step3, taking the estimated initial value of the road surface adhesion coefficient as an iteration initial value, taking the vehicle state parameter at the current moment after filtering as an observation value, inputting the observation value into a road surface adhesion coefficient estimator for processing, and executing step 9 after obtaining the estimated value of the road surface adhesion coefficient at the next moment; Step 4, the decision layer of the whole vehicle integrated planning controller judges whether the current vehicle has braking operation according to the acquired vehicle braking state, if yes, the step 5 is executed after the vehicle state based on the longitudinal dynamics model and the road surface adhesion coefficient recognition strategy are executed, otherwise, the step 8 is executed; Step 5, the decision layer of the whole vehicle integrated planning controller calculates the slip rate according to the longitudinal running speed of the vehicle obtained by the GPS sensor and the rotating speed of the driving wheel obtained by the electromagnetic wheel speed sensor, and judges whether the slip rate in the current running state is lower than a set threshold value, if so, the step 6 is executed, otherwise, the step 7 is executed; Step 6, the decision layer of the whole vehicle integrated planning controller takes the slip rate in the current running state as a small slip rate, adopts a linear tire model to estimate the road surface adhesion coefficient in real time, and executes the step 9 after obtaining the estimated value of the road surface adhesion coefficient at the next moment; Step 7, the decision layer of the whole vehicle integrated planning controller adopts a steady-state tire model to estimate the road surface adhesion coefficient in real time, so that the estimated value of the road surface adhesion coefficient at the next moment is obtained, and then the step 9 is executed; Step 8, the executing layer of the whole vehicle integrated planning controller controls a brake pedal according to the initial value of the road adhesion coefficient and the operation instruction of a driver so as to realize the braking of the vehicle; And 9, calculating the target braking deceleration of the vehicle in the current running state by a decision layer of the whole vehicle integrated planning controller according to the real-time updated road surface adhesion coefficient and the running state of the vehicle, and calculating the total braking force required by the actuating mechanism subsystem by an actuating layer of the whole vehicle integrated planning controller according to the target braking deceleration of the vehicle, the longitudinal deceleration measured by an acceleration sensor and the wheel cylinder pressure measured by the braking pressure sensor, so as to dynamically allocate the output torque of a driving motor of the vehicle and the wheel cylinder pressures of four braking wheel cylinders of a vehicle braking actuator, thereby realizing the real-time braking of the vehicle.

Description

Automobile efficient braking system based on road surface adhesion condition hybrid identification Technical Field The invention belongs to the technical field of automobile electronic control, and mainly relates to real-time acquisition and accuracy of road surface information, and efficient braking of a vehicle is realized according to the accurate road surface information. Background With the continuous development and perfection of intelligent automobile technology, modern automobiles gradually have the capability of acquiring information of sensing surrounding environment. The intelligent automobile acquires information of surrounding environment such as road conditions, weather conditions, traffic conditions, pedestrians, obstacles and the like through various sensors and devices, so that more accurate and safer decisions can be made to improve driving safety and driving experience. In addition, the intelligent automobile can provide the optimal driving route and the optimal vehicle speed for a driver by utilizing a high-precision map and a navigation system, so that congestion and dangerous driving conditions are avoided. Meanwhile, the intelligent automobile can monitor surrounding vehicles, pedestrians and obstacles in real time through sensors such as millimeter wave radar, laser radar, cameras and the like, and make timely response such as deceleration, lane changing or parking, and the like, so that traffic accidents are timely avoided. Although intelligent automobiles can realize automatic driving and accident avoidance through various sensors and technologies, in practical application, there are still a few potential safety hazards. For example, when a roadblock or a building suddenly appears on a sidewalk, a smart car may not respond timely and cannot brake efficiently, thereby causing an accident. Secondly, the intelligent vehicle extremely depends on a vehicle-mounted computer and a software system, if the vehicle-mounted computer encounters a too complex road condition, the operation time of the vehicle-mounted computer is prolonged, a control instruction is delayed, and driving accidents are extremely easy to cause. It is possible that the vehicle may not function properly upon malfunction or software problems, thus placing the driver and passengers in dangerous situations. In addition, sensors such as radars, cameras and the like carried by smart vehicles are very expensive to manufacture and sell, and the cost of technology and convenience provided by the experience of smart driving by ordinary consumers is overly burdensome, making smart vehicles still a luxury for most consumers. Therefore, on the premise of not affecting the intelligent driving experience and driving safety of consumers, the use of sensors is reduced as much as possible, and the related problems of the intelligent automobile with respect to cost, safety and reliability are made up by using an algorithm, so that the intelligent automobile is still a research direction for a long time in the future. The development of intelligent vehicles also places higher demands on the safety of the intended function of braking, firstly the braking system of the intelligent vehicle must be provided with a high degree of reliability in order to ensure that the vehicle can be safely parked in any situation. Secondly, the braking system of the intelligent automobile needs to have fault diagnosis and early warning functions, and faults of the braking system can be found and processed in time. When the braking system fails, the intelligent automobile should be able to alert the driver in time and provide relevant fault diagnostic information. Again, the braking system of the intelligent automobile should be able to adaptively adjust the braking effort according to different driving scenarios and road conditions to ensure stability and safety when the vehicle is braked. In case of rain or wet road, the braking system should be able to adaptively adjust the braking effort to avoid slipping or runaway of the vehicle. Finally, the braking system of the smart car should work cooperatively with other smart systems such as an anti-lock braking system (ABS), a body stability control system (ESP), etc. to ensure stability and safety of the vehicle at the time of braking. The current intelligent vehicle-mounted vehicle sensing system mainly focuses on the acquisition of vehicle driving environment information, is not perfect in acquisition and utilization of road surface adhesion information, is insufficient in knowledge in advance of the road surface adhesion coefficient and other information of a driving road surface, and cannot effectively adjust the running state of an automobile in advance. When the condition of the running road surface changes, the running and braking states of the automobile cannot be quickly and effectively dynamically adjusted according to the change of the road surface parameters. In addition, the road surface informatio