Obstacle avoidance control method and system of vehicle and vehicle

Abstract

The invention provides an obstacle avoidance control method and system of a vehicle and the vehicle, and relates to the technical field of vehicle active safety, and the obstacle avoidance control method comprises the steps that motion data of the vehicle at the current moment is acquired, real-time motion data is obtained, the real-time motion data comprises real-time distance information and real-time vehicle speed information, and the distance information is the distance between an obstacle and the vehicle; historical motion data of the vehicle are obtained, historical dynamic motion data are obtained, and the historical dynamic motion data comprise dynamic distance information and dynamic vehicle speed information of the vehicle within a preset time period before the current moment; based on the historical dynamic motion data, the expected vehicle speed at the current moment is determined; determining a target driving state corresponding to the current moment according to the real-time motion data and the expected vehicle speed; and controlling the vehicle to enter the target driving state so as to perform obstacle avoidance control on the vehicle. The problem that in the prior art, an automatic obstacle avoidance technology is still low in safety is solved.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle active safety technologies, and particularly to an obstacle avoidance control method, system and vehicle for a vehicle. Background Art

[0002] In recent years, with the rapid development of the economy, the number of private cars has been increasing day by day, causing congestion on the roads and bringing many traffic accidents, resulting in huge losses to people's lives and property. There are many reasons for traffic accidents, and the main factor is still the driver himself. Specifically, it is caused by the driver's inability to make a correct prediction of the occurrence of the accident. If some measures can be taken to guide the driver's operation, the incidence of traffic accidents will be reduced.

[0003] With the rapid development of the automotive industry and the continuous improvement of people's living standards, cars have replaced traditional means of transportation. Since there are more and more vehicles on the road, if the obstacle avoidance of vehicles is not effectively controlled, traffic safety accidents may be caused. As one of the key technologies of the vehicle active safety system, the vehicle obstacle avoidance method has received extensive attention.

[0004] The current emergency braking for automatic obstacle avoidance of vehicles mainly relies on millimeter-wave radar and visual perception for obstacle detection. When a collision risk is detected, the system directly triggers the maximum braking torque for emergency braking.

[0005] However, in a congested environment, if the vehicle in front starts and stops frequently, the traditional AEB may cause a risk of rear-end collision of the vehicle behind due to excessive braking, and the safety is relatively low. Summary of the Invention

[0006] Aiming at the deficiencies in the prior art, the present disclosure provides an obstacle avoidance control method, system and vehicle for a vehicle, which solves the problem that the existing automatic obstacle avoidance technology is still relatively low in safety.

[0007] At least one embodiment of the present disclosure provides an obstacle avoidance control method for a vehicle, including:

[0008] Obtaining the motion data of the vehicle at the current moment to obtain real-time motion data, where the real-time motion data includes real-time distance information and real-time vehicle speed information, and the distance information is the distance between the obstacle and the vehicle;

[0009] Obtaining the historical motion data of the vehicle to obtain historical dynamic motion data, where the historical dynamic motion data includes the dynamic distance information and dynamic vehicle speed information of the vehicle within a preset time period before the current moment;

[0010] Determining the expected vehicle speed at the current moment based on the historical dynamic motion data;

[0011] Determine the target driving state corresponding to the current moment according to the real-time motion data and the desired vehicle speed;

[0012] Control the vehicle to enter the target driving state to perform obstacle avoidance control on the vehicle.

[0013] The technical solution provided by the present disclosure has at least the following beneficial effects:

[0014] By determining the desired vehicle speed based on historical dynamic motion data and controlling the vehicle to enter the target driving state, it is possible to avoid having only emergency braking as a control method when obstacle avoidance is required, improving the safety of vehicle driving.

[0015] In an obstacle avoidance control method for a vehicle provided in an embodiment of the present disclosure, the step of obtaining the distance information includes:

[0016] Obtain the point cloud data within a preset area in front of the vehicle;

[0017] Combine the point cloud data and the nearest neighbor search algorithm to determine the distances from each point in front of the vehicle to the vehicle, and use the smallest of these distances as the distance information.

[0018] The technical solution provided by the present disclosure has at least the following beneficial effects:

[0019] By using the smallest distance in the front point cloud data as the distance information, it is possible to effectively avoid collisions between the vehicle and obstacles in front.

[0020] In an obstacle avoidance control method for a vehicle provided in an embodiment of the present disclosure, determining the desired vehicle speed at the current moment based on the historical dynamic motion data includes:

[0021] According to the dynamic distance information and the dynamic vehicle speed information, calculate the dynamic collision time between the vehicle and the obstacle in real time;

[0022] Obtain the path curvature information in front of the vehicle at the current moment;

[0023] Based on the path curvature information, the dynamic collision time, the real-time vehicle speed, and a preset curvature coupling factor, determine the desired vehicle speed at the current moment, where the curvature coupling factor is the coupling relationship between the path curvature and the vehicle speed change.

[0024] The technical solution provided by the present disclosure has at least the following beneficial effects:

[0025] By setting the path curvature and the preset curvature coupling factor, it is possible to effectively cope with various road conditions and avoid large errors in the calculated desired vehicle speed caused by changes in vehicle speed due to the curvature of the road ahead.

[0026] In an obstacle avoidance control method for a vehicle provided by one embodiment of the present disclosure, determining the expected vehicle speed at the current moment based on the historical dynamic motion data includes:

[0027] According to the dynamic distance information and the dynamic vehicle speed information, calculate the dynamic collision time between the vehicle and the obstacle in real time;

[0028] Obtain the path curvature information in front of the vehicle at the current moment;

[0029] Based on the path curvature information, the dynamic collision time, the real-time vehicle speed, and a preset curvature coupling factor, determine the initial expected vehicle speed corresponding to the current moment, where the curvature coupling factor is the coupling relationship between the path curvature and the vehicle speed change;

[0030] Obtain the historical initial expected vehicle speeds corresponding to each moment within the preset time period,

[0031] Based on the Kalman filter, determine the dynamic expected vehicle speed according to each historical initial expected vehicle speed and the initial expected vehicle speed corresponding to the current moment;

[0032] Determine the expected vehicle speed at the current moment according to the dynamic expected vehicle speed.

[0033] The technical solution provided by the present disclosure has at least the following beneficial effects:

[0034] By performing Kalman filtering on the dynamic expected vehicle speed, the generation accuracy of the dynamic expected vehicle speed can be improved.

[0035] In an obstacle avoidance control method for a vehicle provided by one embodiment of the present disclosure, determining the expected vehicle speed at the current moment according to the dynamic expected vehicle speed includes:

[0036] According to the dynamic distance information and the dynamic vehicle speed information, determine whether the obstacle is a moving object;

[0037] When the obstacle is a moving object, based on the PID algorithm, adjust the expected vehicle speed at the current moment in real time according to the real-time vehicle speed and the dynamic expected vehicle speed;

[0038] Based on the dynamic expected vehicle speed, use a first-order low-pass filter to smooth the adjusted expected vehicle speed at the current moment to obtain the final expected vehicle speed at the current moment.

[0039] The technical solution provided by the present disclosure has at least the following beneficial effects:

[0040] Adjust the desired vehicle speed through the PID algorithm, and use a first-order low-pass filter to smooth the desired vehicle speed at the current moment after adjustment, which can avoid control oscillation caused by instruction mutation.

[0041] In an obstacle avoidance control method for a vehicle provided in one embodiment of the present disclosure, the determining the target driving state corresponding to the current moment according to the real-time motion data and the desired vehicle speed includes:

[0042] Determine whether the real-time distance information meets a preset obstacle avoidance distance condition;

[0043] When the real-time distance information meets the obstacle avoidance distance condition, determine the over-speed ratio or under-speed ratio of the real-time vehicle speed to the desired vehicle speed;

[0044] When the over-speed ratio is greater than a preset first ratio threshold, determine the target driving state corresponding to the current moment based on the emergency braking trigger condition of the vehicle;

[0045] When the over-speed ratio or under-speed ratio is less than or equal to a preset second ratio threshold, determine the target driving state based on the real-time motion data corresponding to the vehicle and a preset smooth driving condition;

[0046] When the under-speed ratio is less than a preset third ratio threshold, determine the target driving state based on the safe acceleration condition of the vehicle.

[0047] The technical solution provided by the present disclosure has at least the following beneficial effects:

[0048] By adopting the above various different situations to respectively control the vehicle to determine different driving states, it ensures that the vehicle can flexibly handle various emergencies, reduces the risk of rear-end collision of the following vehicle caused by excessive braking, and improves the driving safety of the vehicle.

[0049] In an obstacle avoidance control method for a vehicle provided in one embodiment of the present disclosure:

[0050] The obstacle avoidance distance condition is less than or equal to 1.5 meters;

[0051] The first ratio threshold is 50%, and the first ratio threshold is determined based on the emergency braking trigger condition of the vehicle;

[0052] The second ratio threshold is 5%, and the second ratio threshold is determined based on a preset smooth driving condition corresponding to the vehicle;

[0053] The third ratio threshold is 30%, and the third ratio threshold is determined based on the safe acceleration condition corresponding to the vehicle.

[0054] In an obstacle avoidance control method for a vehicle provided in one embodiment of the present disclosure, the method further includes:

[0055] When the real-time distance information meets the preset obstacle avoidance distance condition, and the overspeed ratio is greater than a preset first ratio threshold, or the overspeed ratio or underspeed ratio is less than or equal to a preset second ratio threshold, if the real-time vehicle speed changes, the transmission of a preset signal is interrupted, and the preset signal is a signal that does not affect the driving state of the vehicle.

[0056] The technical solution provided by the present disclosure at least has the following beneficial effects:

[0057] By interrupting the transmission of other preset signals, it is possible to ensure the priority transmission of the desired vehicle speed signal, enabling the vehicle to enter the preset braking state in a timely manner and ensuring that the transmission delay of the control unit is less than 50 ms.

[0058] At least one embodiment of the present disclosure provides an obstacle avoidance control system for a vehicle, including:

[0059] A real-time data acquisition module that acquires the motion data of the vehicle at the current moment to obtain real-time motion data, where the real-time motion data includes real-time distance information and real-time vehicle speed information, and the distance information is the distance between the obstacle and the vehicle;

[0060] A historical data acquisition module that acquires the historical motion data of the vehicle to obtain historical dynamic motion data, where the historical dynamic motion data includes the dynamic distance information and dynamic vehicle speed information of the vehicle within a preset time period before the current moment;

[0061] A desired vehicle speed generation module that determines the desired vehicle speed at the current moment based on the historical dynamic motion data;

[0062] A target achievement state determination module that determines the target driving state corresponding to the current moment according to the real-time motion data and the desired vehicle speed

[0063] An obstacle avoidance control module that controls the vehicle to enter the target driving state to perform obstacle avoidance control on the vehicle.

[0064] The present disclosure also provides a vehicle, including a vehicle body and an obstacle avoidance control system for a vehicle as described above configured on the body.

[0065] The present disclosure also provides an electronic device, including a memory, a processor, and a program stored on the memory and running on the processor, where the processor implements an obstacle avoidance control method for a vehicle as described above when executing the program.

[0066] The present disclosure also provides a computer-readable storage medium storing instructions that, when run on a terminal device, cause the terminal device to execute an obstacle avoidance control method for a vehicle as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a schematic flow chart of an obstacle avoidance control method for a vehicle according to the present disclosure;

[0068] Figure 2 is a schematic structural relationship diagram of an obstacle avoidance control system for a vehicle according to the present disclosure;

[0069] Figure 3 is a control logic diagram of an obstacle avoidance control system for a vehicle according to the present disclosure;

[0070] Figure 4 is a schematic structural diagram of an electronic device provided by the present disclosure.

[0071] In the drawings, the list of components represented by each reference numeral is as follows:

[0072] 100, obstacle avoidance control system, 101, real-time data acquisition module, 102, historical data acquisition module, 103, expected vehicle speed generation module, 104, target achievement status determination module, 105, obstacle avoidance control module;

[0073] 10, electronic device, 11, processor, 12, read-only memory (ROM), 13, random access memory (RAM), 14, bus, 15, input / output (I / O) interface, 16, input unit, 17, output unit, 18, storage unit, 19, communication unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0074] The principles and features of the present disclosure are described below. The examples given are only for explaining the present disclosure and are not intended to limit the scope of the present disclosure.

[0075] An embodiment of the present disclosure provides an obstacle avoidance control method for a vehicle. Please refer here Figure 1 as shown, including:

[0076] S1. Obtain the motion data of the vehicle at the current moment to obtain real-time motion data, where the real-time motion data includes real-time distance information and real-time vehicle speed information, and the distance information is the distance between the obstacle and the vehicle;

[0077] S2. Obtain the historical motion data of the vehicle to obtain historical dynamic motion data, where the historical dynamic motion data includes the dynamic distance information and dynamic vehicle speed information of the vehicle within a preset time period before the current moment;

[0078] S3. Determine the expected vehicle speed at the current moment based on historical dynamic motion data;

[0079] S4. Determine the target driving state corresponding to the current moment according to the real-time motion data and the expected vehicle speed;

[0080] S5. Control the vehicle to enter the target driving state to perform obstacle avoidance control on the vehicle.

[0081] By setting the expected vehicle speed and controlling the vehicle to enter various target driving states accordingly, it is possible to avoid having only one control method of emergency braking when obstacle avoidance is required, thus improving the safety of vehicle driving.

[0082] It should be understood that the dynamic distance information is characterized as a set of historical distance information within a preset time period. Similarly, the dynamic vehicle speed information is characterized as a set of historical vehicle speed information within a preset time period.

[0083] In an exemplary embodiment, the step of obtaining the distance information includes:

[0084] Obtain the point cloud data within a preset area in front of the vehicle. In this embodiment, in order to identify a wider area to improve the vehicle's monitoring ability of obstacles, in this embodiment, a lidar sensor deployed in front of the vehicle and cameras deployed on both sides of the vehicle are used to obtain the point cloud data about the obstacles. Among them, the lidar sensor can scan the obstacles in front in real time to obtain the distance from the vehicle to the obstacles, and the cameras detect the obstacles closer to both sides of the vehicle through image recognition and compensate for the vertical pitch angle limitation of the lidar scanning beam to supplement the blind area information of the lidar;

[0085] Combine the point cloud data and the nearest neighbor search algorithm. Specifically, continuously monitor the area of 120° in front of the vehicle at a preset scanning frequency to collect the point cloud data, perform spherical coordinate transformation on the original point cloud data, calculate the radial distance of each point from the vehicle, use the nearest neighbor search algorithm to search for the minimum distance among them, and take the minimum distance as the distance information.

[0086] In an exemplary embodiment, to determine the expected vehicle speed at the current moment based on historical dynamic motion data, the specific steps can be:

[0087] According to the dynamic distance information and the dynamic vehicle speed information, calculate the dynamic collision time between the vehicle and the obstacle in real time;

[0088] Obtain the path curvature information in front of the vehicle at the current moment;

[0089] Based on the path curvature information, the dynamic collision time, the real-time vehicle speed, and a preset curvature coupling factor, determine the expected vehicle speed at the current moment. The curvature coupling factor is the coupling relationship between the path curvature and the vehicle speed change.

[0090] By setting the path curvature and a preset curvature coupling factor, various road conditions can be effectively dealt with, and a large error in the calculated expected vehicle speed caused by a change in the vehicle speed due to the curvature of the road ahead can be avoided.

[0091] In another exemplary embodiment, to improve the generation accuracy of the dynamic expected vehicle speed, the above method of determining the expected vehicle speed at the current moment based on historical dynamic motion data may specifically further include the following steps:

[0092] According to the dynamic distance information and the dynamic vehicle speed information, calculate the dynamic collision time between the vehicle and the obstacle in real time;

[0093] Obtain the path curvature information in front of the vehicle at the current moment, and based on the path curvature information, the dynamic collision time, the real-time vehicle speed, and a preset curvature coupling factor, determine the initial expected vehicle speed corresponding to the current moment, where the curvature coupling factor is the coupling relationship between the path curvature and the change in the vehicle speed;

[0094] Obtain the historical initial expected vehicle speeds at each moment within a preset time period,

[0095] Based on the historical initial expected vehicle speeds and the initial expected vehicle speed at the current moment, generate the dynamic expected vehicle speed through Kalman filtering;

[0096] Determine the expected vehicle speed at the current moment according to the dynamic expected vehicle speed.

[0097] Optionally, to improve the user driving experience, determining the expected vehicle speed at the current moment according to the dynamic expected vehicle speed may specifically further include:

[0098] According to the dynamic distance information and the dynamic vehicle speed information, determine whether the obstacle is a moving object;

[0099] If the obstacle is a moving object, then combine the current actual vehicle speed and the dynamic expected vehicle speed, and use the PID algorithm to adjust the expected vehicle speed at the current moment in real time;

[0100] Based on the dynamic expected vehicle speed, use a first-order low-pass filter to smooth the adjusted expected vehicle speed at the current moment to obtain the final expected vehicle speed at the current moment.

[0101] Adjusting the expected vehicle speed through the PID algorithm and using a first-order low-pass filter to smooth the adjusted expected vehicle speed at the current moment can avoid control oscillation caused by sudden command changes.

[0102] In an exemplary embodiment provided by the present disclosure, determining the target driving state corresponding to the current moment according to the real-time motion data and the expected vehicle speed specifically includes:

[0103] Determine whether the real-time distance information meets the preset obstacle avoidance distance condition;

[0104] When the real-time distance information meets the obstacle avoidance distance condition, determine the over-speed ratio or under-speed ratio of the real-time vehicle speed and the desired vehicle speed, where the obstacle avoidance distance condition is less than or equal to 1.5 meters;

[0105] When the over-speed ratio is greater than the preset first ratio threshold, determine the target driving state corresponding to the current moment based on the emergency braking trigger condition of the vehicle, where the first ratio threshold is 50%, and the first ratio threshold is determined based on the emergency braking trigger condition of the vehicle. In this case, the vehicle enters the emergency braking state;

[0106] When the over-speed ratio or under-speed ratio is less than or equal to the preset second ratio threshold, determine the target driving state based on the real-time motion data of the vehicle and the preset smooth driving condition, where the second ratio threshold is 5%, and the second ratio threshold is determined based on the preset smooth driving condition of the vehicle. In this case, the vehicle enters the smooth driving state, and then, according to the current vehicle speed and the moving speed of the obstacle, control the vehicle to follow the obstacle;

[0107] When the under-speed ratio is less than the preset third ratio threshold, determine the target driving state based on the safe acceleration condition of the vehicle, where the third ratio threshold is 30%, and the third ratio threshold is determined based on the safe acceleration condition of the vehicle. In this case, the vehicle driver is allowed to accelerate manually. And through the visual perception system, monitor the objects around the vehicle. When the surrounding pedestrians or vehicles approach the vehicle, the visual perception system sends the recognized data to the decision-making unit, and the decision-making unit adjusts the control strategy according to the actual vehicle speed and the distance of the object relative to the vehicle to reduce the desired vehicle speed, so that the vehicle can quickly stop or slow down to give way.

[0108] Based on the trigger conditions in various different situations, determine that the vehicle enters the corresponding target driving state to make targeted adjustments according to the actual requirements of the current environment of the vehicle. At the same time, combined with the smoothing processing of the first-order low-pass filter, it can significantly avoid the strong sense of jerk when the vehicle controller controls braking, ensure that the vehicle can flexibly handle various emergencies, reduce the risk of rear-end collision caused by excessive braking, and improve the driving safety of the vehicle.

[0109] Optionally, the method further includes:

[0110] When the distance between the obstacle and the vehicle is within the first preset range, and the current vehicle speed is higher than the first preset ratio of the final desired speed, or the current vehicle speed and the final desired speed are within the second preset ratio range, if the current vehicle speed changes, the transmission of the preset signal is interrupted. The other preset signal represents information that does not affect the normal driving of the vehicle, so as to preferentially transmit the desired speed signal. The above preset signal is, for example, a signal related to the basic information of the vehicle uploaded to the network platform.

[0111] By interrupting the transmission of other preset signals, it can ensure the preferential transmission of the desired speed signal, enable the vehicle to enter the preset braking state in a timely manner, and ensure that the transmission delay of the control unit is less than 50 ms.

[0112] The present disclosure also provides an obstacle avoidance control system 100 for a vehicle. Please refer here Figure 2 as shown, including:

[0113] A real-time data acquisition module 101, which acquires the motion data of the vehicle at the current moment to obtain real-time motion data. The real-time motion data includes real-time distance information and real-time vehicle speed information. Among them, the distance information is the distance between the obstacle and the vehicle;

[0114] A historical data acquisition module 102, which acquires the historical motion data of the vehicle to obtain historical dynamic motion data. The historical dynamic motion data includes the dynamic distance information and dynamic vehicle speed information of the vehicle within a preset time period before the current moment;

[0115] A desired vehicle speed generation module 103, which determines the desired vehicle speed at the current moment based on the historical dynamic motion data;

[0116] A target achievement state determination module 104, which determines the target driving state corresponding to the current moment according to the real-time motion data and the desired vehicle speed

[0117] An obstacle avoidance control module 105, which controls the vehicle to enter the target driving state to perform obstacle avoidance control on the vehicle.

[0118] Furthermore, the real-time data acquisition module 101 includes:

[0119] Acquire the point cloud data within the preset area in front of the vehicle;

[0120] Combined with the point cloud data and the nearest neighbor search algorithm, determine the distance from each point in front of the vehicle to the vehicle, and use the smallest distance among them as the distance information.

[0121] Furthermore, the desired vehicle speed generation module 103 specifically includes:

[0122] According to the dynamic distance information and dynamic vehicle speed information, calculate the dynamic collision time between the vehicle and the obstacle in real time;

[0123] Obtain the path curvature information in front of the vehicle at the current moment;

[0124] Based on the path curvature information, dynamic collision time, real-time vehicle speed, and a preset curvature coupling factor, determine the desired vehicle speed at the current moment, where the curvature coupling factor is the coupling relationship between the path curvature and the vehicle speed change.

[0125] Furthermore, the desired vehicle speed generation module 103 specifically further includes:

[0126] According to the dynamic distance information and dynamic vehicle speed information, calculate the dynamic collision time between the vehicle and the obstacle in real time;

[0127] Obtain the path curvature information in front of the vehicle at the current moment;

[0128] Based on the path curvature information, dynamic collision time, real-time vehicle speed, and a preset curvature coupling factor, determine the initial desired vehicle speed corresponding to the current moment, where the curvature coupling factor is the coupling relationship between the path curvature and the vehicle speed change;

[0129] Obtain the historical initial desired vehicle speeds corresponding to each moment within a preset time period,

[0130] Based on the Kalman filter, according to each historical initial desired vehicle speed and the initial desired vehicle speed corresponding to the current moment, determine the dynamic desired vehicle speed;

[0131] Determine the desired vehicle speed at the current moment according to the dynamic desired vehicle speed.

[0132] Furthermore, the desired vehicle speed generation module 103 specifically further includes:

[0133] According to the dynamic distance information and dynamic vehicle speed information, determine whether the obstacle is a moving object;

[0134] In the case where the obstacle is a moving object, based on the PID algorithm, adjust the desired vehicle speed at the current moment in real time according to the real-time vehicle speed and the dynamic desired vehicle speed;

[0135] Based on the dynamic desired vehicle speed, use a first-order low-pass filter to smooth the adjusted desired vehicle speed at the current moment to obtain the final desired vehicle speed at the current moment.

[0136] Furthermore, the target achievement status determination module 104 specifically includes:

[0137] Determine whether the real-time distance information meets the preset obstacle avoidance distance condition;

[0138] In the case where the real-time distance information meets the obstacle avoidance distance condition, determine the over-speed ratio or under-speed ratio of the real-time vehicle speed to the desired vehicle speed;

[0139] When the overspeed ratio is greater than a preset first ratio threshold, based on the emergency braking trigger condition of the vehicle, determine the target driving state corresponding to the current moment;

[0140] When the overspeed ratio or underspeed ratio is less than or equal to a preset second ratio threshold, based on the real-time motion data of the vehicle and the preset smooth driving condition, determine the target driving state;

[0141] When the underspeed ratio is less than a preset third ratio threshold, based on the safe acceleration condition of the vehicle, determine the target driving state.

[0142] Furthermore, the target achievement state determination module 104 specifically further includes:

[0143] The obstacle avoidance distance condition is less than or equal to 1.5 meters;

[0144] The first ratio threshold is 50%, and the first ratio threshold is determined based on the emergency braking trigger condition of the vehicle;

[0145] The second ratio threshold is 5%, and the second ratio threshold is determined based on the preset smooth driving condition of the vehicle;

[0146] The third ratio threshold is 30%, and the third ratio threshold is determined based on the safe acceleration condition of the vehicle.

[0147] Furthermore, the obstacle avoidance control module 105 further includes:

[0148] When the real-time distance information meets the preset obstacle avoidance distance condition, and the overspeed ratio is greater than the preset first ratio threshold, or the overspeed ratio or underspeed ratio is less than or equal to the preset second ratio threshold, if the real-time vehicle speed changes, then interrupt the transmission of the preset signal, and the preset signal is a signal that does not affect the driving state of the vehicle.

[0149] In summary, please refer to Figure 3 as shown, when this system executes:

[0150] First, use the lidar in the real-time data acquisition module 101 to detect obstacles, obtain the distance information and vehicle speed information, and summarize the historical dynamic motion data through the historical data acquisition module 102;

[0151] Transmit the above historical dynamic motion data to the desired vehicle speed generation module 103, and then the desired vehicle speed generation module 103 determines the desired vehicle speed at the current moment based on the dynamic distance information and dynamic vehicle speed information;

[0152] The desired vehicle speed generation module 103 sends the calculated desired vehicle speed to the data forwarding module;

[0153] The data forwarding module transmits data to the obstacle avoidance control module 105;

[0154] Subsequently, the obstacle avoidance control module 105 controls the vehicle to enter the following target driving states according to the over-speed or under-speed ratio of the current actual vehicle speed and the distance information from the obstacle ahead:

[0155] If the distance between the obstacle and the vehicle is greater than 1.5 m, the vehicle is controlled to drive according to the final desired vehicle speed at the current moment;

[0156] If the distance between the obstacle and the vehicle is less than 1.5 m and the current actual vehicle speed is 50% higher than the final desired vehicle speed, the vehicle is controlled to perform an emergency brake;

[0157] If the distance between the obstacle and the vehicle is less than 1.5 m and the difference ratio between the current actual vehicle speed and the final desired vehicle speed is within ±5%, the vehicle is controlled to follow the obstacle;

[0158] If the distance between the obstacle and the vehicle is less than 1.5 m and the current actual vehicle speed is 30% lower than the final desired vehicle speed, the vehicle is controlled to drive by combining the final desired vehicle speed at the current moment and the throttle opening information, that is, the driver is allowed to manually control the vehicle to accelerate and approach the obstacle.

[0159] And calculate the desired vehicle speed according to the distance between the current vehicle and the obstacle

[0160] The embodiment of the present disclosure also provides a vehicle, including a vehicle body and an obstacle avoidance control system of a vehicle as described above configured on the body.

[0161] It should be noted that in the present disclosure, all actions of obtaining signals, information or data are carried out on the basis of strictly following the relevant data protection regulations and policies of the country where the location is located and with the authorization of the corresponding device owner. The owners mainly include:

[0162] (1) Automobile manufacturers: As developers of vehicle hardware and systems, they control the vehicle's underlying hardware and software platforms and have management and control rights over the data generated by vehicle operation, such as driving and fault data.

[0163] (2) Component suppliers: Provide key components for automobiles and have certain ownership of the data collected and processed by the components for product optimization and after-sales, such as the data generated by sensors and chips.

[0164] (3) Vehicle owners or users: The actual users of the vehicle, who have the right to decide on the usage mode and scope of vehicle data, such as whether to share driving trajectories, driving habits and other data, and have the need and right to protect the privacy of their own relevant data.

[0165] (4) Service provider: Provides services such as software and data analysis, and has the right to use and manage the acquired and processed data under the framework of the agreement, but the ownership usually belongs to other entities.

[0166] An embodiment of the present disclosure also provides a computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a terminal device, the terminal device is caused to execute an obstacle avoidance control method for a vehicle as described above.

[0167] An embodiment of the present disclosure also provides an electronic device, including a memory, a processor, and a program stored on the memory and running on the processor. When the processor executes the program, an obstacle avoidance control method for a vehicle as described above is implemented.

[0168] Figure 4 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0169] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0170] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0171] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a vehicle automatic obstacle avoidance method.

[0172] In some embodiments, a vehicle automatic obstacle avoidance method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the vehicle automatic obstacle avoidance method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute a vehicle automatic obstacle avoidance method in any other suitable manner (e.g., by means of firmware).

[0173] The various embodiments of the systems and technologies described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a special or general programmable processor, can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0174] Computer programs for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0175] In the context of the present disclosure, a computer-readable storage medium may be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0176] In order to provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or an LCD (liquid crystal display)); and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0177] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0178] A computing system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0179] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" can explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0180] In the description of this specification, descriptions with reference to terms such as "an embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0181] Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limitations on the embodiments of the present disclosure. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the embodiments of the present disclosure.

Claims

1. An obstacle avoidance control method for a vehicle, characterized in that, Including: Obtain the motion data of the vehicle at the current moment to obtain real-time motion data, where the real-time motion data includes real-time distance information and real-time vehicle speed information. Among them, the distance information is the distance between the obstacle and the vehicle; Obtain the historical motion data of the vehicle to obtain historical dynamic motion data, where the historical dynamic motion data includes the dynamic distance information and dynamic vehicle speed information of the vehicle within a preset time period before the current moment; Based on the historical dynamic motion data, determine the expected vehicle speed at the current moment; According to the real-time motion data and the expected vehicle speed, determine the target driving state corresponding to the current moment; Control the vehicle to enter the target driving state to perform obstacle avoidance control on the vehicle.

2. The obstacle avoidance control method for a vehicle according to claim 1, characterized in that The step of obtaining the distance information includes: Obtain the point cloud data within a preset area in front of the vehicle; Combine the point cloud data and the nearest neighbor search algorithm to determine the distances from each point in front of the vehicle to the vehicle, and use the smallest distance among them as the distance information.

3. The obstacle avoidance control method for a vehicle according to claim 1, wherein The determining the expected vehicle speed at the current moment based on the historical dynamic motion data includes: According to the dynamic distance information and the dynamic vehicle speed information, calculate the dynamic collision time between the vehicle and the obstacle in real time; Obtain the path curvature information in front of the vehicle at the current moment; Based on the path curvature information, the dynamic collision time, the real-time vehicle speed, and a preset curvature coupling factor, determine the expected vehicle speed at the current moment, where the curvature coupling factor is the coupling relationship between the path curvature and the vehicle speed change.

4. A vehicle obstacle avoidance control method according to claim 1, characterized in that, The determining the expected vehicle speed at the current moment based on the historical dynamic motion data includes: According to the dynamic distance information and the dynamic vehicle speed information, calculate the dynamic collision time between the vehicle and the obstacle in real time; Obtain the path curvature information in front of the vehicle at the current moment; Based on the path curvature information, the dynamic collision time, the real-time vehicle speed, and a preset curvature coupling factor, determine the initial expected vehicle speed corresponding to the current moment, where the curvature coupling factor is the coupling relationship between the path curvature and the vehicle speed change; Obtain the historical initial expected vehicle speeds corresponding to each moment within the preset time period, Based on the Kalman filter, according to each historical initial expected vehicle speed and the initial expected vehicle speed corresponding to the current moment, determine the dynamic expected vehicle speed; Determine the expected vehicle speed at the current moment according to the dynamic expected vehicle speed.

5. The obstacle avoidance control method for a vehicle according to claim 4, wherein The determining the expected vehicle speed at the current moment according to the dynamic expected vehicle speed includes: According to the dynamic distance information and the dynamic vehicle speed information, determine whether the obstacle is a moving object; When the obstacle is a moving object, based on the PID algorithm, adjust the expected vehicle speed at the current moment in real time according to the real-time vehicle speed and the dynamic expected vehicle speed; Based on the dynamic expected vehicle speed, use a first-order low-pass filter to smooth the adjusted expected vehicle speed at the current moment to obtain the final expected vehicle speed at the current moment.

6. The obstacle avoidance control method for a vehicle according to claim 1, characterized in that, Determining the target driving state corresponding to the current moment according to the real-time motion data and the desired vehicle speed includes: Determining whether the real-time distance information meets a preset obstacle avoidance distance condition; When the real-time distance information meets the obstacle avoidance distance condition, determining the over-speed ratio or under-speed ratio of the real-time vehicle speed and the desired vehicle speed; When the over-speed ratio is greater than a preset first ratio threshold, determining the target driving state corresponding to the current moment based on the emergency braking trigger condition of the vehicle; When the over-speed ratio or under-speed ratio is less than or equal to a preset second ratio threshold, determining the target driving state based on the real-time motion data corresponding to the vehicle and a preset smooth driving condition; When the under-speed ratio is less than a preset third ratio threshold, determining the target driving state based on the safe acceleration condition of the vehicle.

7. The obstacle avoidance control method for a vehicle according to claim 6, wherein: The obstacle avoidance distance condition is less than or equal to 1.5 meters; The first ratio threshold is 50%, and the first ratio threshold is determined based on the emergency braking trigger condition of the vehicle; The second ratio threshold is 5%, and the second ratio threshold is determined based on a preset smooth driving condition corresponding to the vehicle; The third ratio threshold is 30%, and the third ratio threshold is determined based on the safe acceleration condition corresponding to the vehicle.

8. A vehicle obstacle avoidance control method according to claim 5, characterized in that, It further includes: When the real-time distance information meets the preset obstacle avoidance distance condition, and the over-speed ratio is greater than the preset first ratio threshold, or the over-speed ratio or under-speed ratio is less than or equal to the preset second ratio threshold, if the real-time vehicle speed changes, the sending of a preset signal is interrupted, and the preset signal is a signal that does not affect the driving state of the vehicle.

9. An obstacle avoidance control system for a vehicle, characterized in that, It includes: A real-time data acquisition module that acquires the motion data of the vehicle at the current moment to obtain real-time motion data, and the real-time motion data includes real-time distance information and real-time vehicle speed information, wherein the distance information is the distance between the obstacle and the vehicle; A historical data acquisition module that acquires the historical motion data of the vehicle to obtain historical dynamic motion data, and the historical dynamic motion data includes the dynamic distance information and dynamic vehicle speed information of the vehicle within a preset time period before the current moment; A desired vehicle speed generation module that determines the desired vehicle speed at the current moment based on the historical dynamic motion data; A target achievement state determination module that determines the target driving state corresponding to the current moment according to the real-time motion data and the desired vehicle speed An obstacle avoidance control module that controls the vehicle to enter the target driving state to perform obstacle avoidance control on the vehicle.

10. A vehicle, characterized in that, It includes a vehicle body and an obstacle avoidance control system for a vehicle as described in claim 9 configured on the body.

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