Vehicle control method and system combining AUTBUS and CAN bus

Abstract

The invention relates to a vehicle control method and system combining an AUTBUS and a CAN bus, belongs to the technical field of vehicle control, and solves the problem that in the prior art, data processing is slow, and a vehicle cannot be controlled in real time. The vehicle control method comprises the steps that a control instruction of a user is received from a vehicle display terminal through a CAN bus, a plurality of preset control points are determined according to the control instruction of the user, the preset control points are ranked, and a vehicle is controlled to move to the preset control points in sequence according to the sequence; determining whether the predetermined control point satisfies a control condition based on the received surrounding image information, radar detection information, pavement defect information and pavement structure information; when the preset control point meets the control condition, the vehicle is controlled to enter a working state through the chassis center machine; otherwise, the vehicle is controlled to move to the next preset control point. Safety control over the vehicle is achieved by rapidly processing the data of the surrounding environment of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control, and in particular to a vehicle control method and system combining AUTBUS and CAN buses. Background Art

[0002] With the development of science and technology, precise and safe control of vehicles is becoming increasingly important. It requires the integration and sharing of massive amounts of data, so the vehicle's control system needs to have powerful data processing capabilities.

[0003] In existing technologies, vehicles typically use CAN (Controller Area Network) for communication control, but this fails to effectively process massive amounts of data. This results in slow data processing and long control delays when vehicles face information technology and big data application scenarios, especially in information fusion and data link sharing, increasing the risks of vehicle operation.

[0004] Therefore, a new technical solution for controlling vehicles is urgently needed. Summary of the Invention

[0005] In view of the above analysis, the embodiments of the present invention aim to provide a vehicle control method and system combining AUTBUS and CAN buses, so as to solve the problem in the prior art of slow data processing and inability to perform real-time control of the vehicle.

[0006] In one aspect, an embodiment of the present invention provides a vehicle control method combining AUTBUS and CAN buses, the vehicle control method comprising:

[0007] Receive user control commands from the vehicle display terminal via the CAN bus, determine multiple predetermined control points according to the user's control commands, sort the multiple predetermined control points, and control the vehicle to move to each predetermined control point in sequence;

[0008] At each predetermined control point, surrounding image information and radar detection information detected by a surrounding environment sensing device are received via the AUTBUS bus, and road surface defect information and road surface structure information detected by a ground-penetrating radar are received via the CAN bus; based on the received information, it is determined whether the predetermined control point meets the control conditions;

[0009] When the predetermined control point meets the control conditions, the vehicle is controlled by the chassis center computer to enter the working state; otherwise, the vehicle is controlled to move to the next predetermined control point.

[0010] Based on the further improvement of the above vehicle control method, the control conditions include surrounding environment conditions and ground control conditions;

[0011] Determine whether the predetermined control point meets the surrounding environmental conditions, including:

[0012] Identifying each target in the surrounding image information based on a deep convolutional neural network to obtain the category of each target, determining the distance between each target and a predetermined control point in combination with the radar detection information, and determining the risk factor of the surrounding environment based on the category of each target and the distance of each target;

[0013] When the risk factor of the surrounding environment is lower than the preset risk threshold, it is determined that the predetermined control point meets the surrounding environment conditions.

[0014] Based on the further improvement of the above vehicle control method, the risk factor of the surrounding environment of the predetermined control point is calculated by the following formula:

[0015]

[0016] Where D represents the risk factor of the surrounding environment of the predetermined control point, W i Indicates the risk level of the i-th target, L i Represents the distance between the i-th target and the predetermined control point, N represents the total number of targets; the danger level of the target is determined based on the category to which the target belongs.

[0017] Based on the further improvement of the above vehicle control method, determining whether the predetermined control point meets the ground control conditions includes:

[0018] determining a ground adaptability coefficient of a predetermined control point according to the pavement defect information and the pavement structure information;

[0019] When the ground adaptation coefficient of the predetermined control point is higher than a preset ground adaptation threshold, it is determined that the predetermined control point meets the ground control condition.

[0020] Based on further improvements to the above vehicle control method, the pavement defect information includes the size of the defect area and the number of defects; the pavement structure information includes the number of pavement layers, the thickness of each pavement layer, and the material composition of each pavement layer.

[0021] Based on the further improvement of the above vehicle control method, the ground adaptation coefficient of the predetermined control point is calculated by the following formula:

[0022]

[0023] Where A represents the ground adaptation coefficient of the predetermined control point, H j represents the thickness of the jth layer of the road surface, X j represents the bearing capacity coefficient of the jth layer of the pavement, S krepresents the defect area of ​​the kth defect, Q represents the number of pavement layers, and P represents the number of defects. The bearing capacity coefficient of each pavement layer is determined based on the material composition of each pavement layer.

[0024] Based on a further improvement of the above vehicle control method, the step of sorting the plurality of predetermined control points includes:

[0025] Calculate the distance between the vehicle's initial point and each predetermined control point, and take the predetermined control point with the shortest distance as the first predetermined control point;

[0026] The distance between each of the remaining predetermined control points and the previous predetermined control point is calculated, and the predetermined control point with the shortest distance is used as the next predetermined control point, and so on, to determine the order of the remaining predetermined control points.

[0027] On the other hand, an embodiment of the present invention provides a vehicle control system combining AUTBUS and CAN buses, the vehicle control system comprising:

[0028] Vehicle display terminal, used to receive user control instructions;

[0029] The CPU control module is connected to the vehicle display terminal via the CAN bus, and is used to receive the user's control instructions from the vehicle display terminal, determine multiple predetermined control points according to the user's control instructions, sort the multiple predetermined control points, and control the vehicle to move to each predetermined control point in sequence;

[0030] The CPU control module is connected to the ground penetrating radar via a CAN bus and is used to obtain road surface defect information and road surface structure information from the ground penetrating radar; the CPU control module is connected to the surrounding environment sensing device via an AUTBUS bus and is used to obtain surrounding environment information and radar detection information from the surrounding environment sensing device and determine whether the predetermined control point meets the control condition;

[0031] The CPU control module is connected to the chassis center computer through the CAN bus. When the predetermined control point meets the control conditions, the chassis center computer controls the vehicle to enter the working state; otherwise, the vehicle is controlled to move to the next predetermined control point.

[0032] Based on the further improvement of the above vehicle control system, the surrounding environment perception device includes one or more of the following:

[0033] Front camera, left camera, rear camera, right camera, rear wide-angle lidar, left wide-angle lidar, right wide-angle lidar, front wide-angle lidar, ultrasonic radar and millimeter-wave radar.

[0034] Based on the further improvement of the above vehicle control system, the CPU control module is further connected to one or more of the following via the AUTBUS bus:

[0035] Battery management system, DCDC converter, body electronic stability system, network power distribution management system, air conditioning management system and human-machine interface.

[0036] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0037] 1. By combining the CAN bus with the AUTBUS bus, the surrounding environment data and ground detection data of the vehicle's predetermined control points can be quickly acquired and processed, which improves the vehicle's ability to process and integrate massive amounts of data and increases the safety of the vehicle during operation.

[0038] 2. By determining whether the predetermined control points meet the surrounding environmental conditions and ground control conditions, the vehicle can respond quickly to unfamiliar and complex application scenarios, thereby improving the vehicle's safety performance during use.

[0039] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like parts throughout the drawings.

[0041] Figure 1 A flow chart of a vehicle control method combining AUTBUS and CAN buses provided in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the structure of a vehicle control system combining AUTBUS and CAN buses provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0044] AUTBUS is a new broadband bus based on time-sensitive technology. It combines the advantages of fieldbus and real-time Ethernet. Compared with the CAN bus, it has higher bandwidth and better real-time performance. It uses a two-wire non-bridge medium and has the advantages of multiple nodes (maximum number of nodes 254), high bandwidth (100Mbps), strong real-time (precision clock synchronization), high security (based on high-performance symmetric and asymmetric encryption and decryption processing technology of national secret algorithms), high reliability and long-distance transmission (500m). AUTBUS uses a bus-based networking method and provides fixed-bandwidth data services and variable-bandwidth data services that support burst data through bus pre-configuration or dynamic application.

[0045] Based on the characteristics of the AUTBUS bus in various aspects, combined with the need for vehicles to process massive amounts of data when facing complex application scenarios, a specific embodiment provided by the present invention discloses a vehicle control method combining the AUTBUS and CAN buses, such as Figure 1 As shown, the vehicle control method includes:

[0046] Step S1: Receive a user's control command from the vehicle display terminal via the CAN bus, determine a plurality of predetermined control points according to the user's control command, sort the plurality of predetermined control points, and control the vehicle to move to each predetermined control point in sequence;

[0047] Step S2: At each predetermined control point, surrounding image information and radar detection information detected by the surrounding environment sensing device are received via the AUTBUS bus, and road surface defect information and road surface structure information detected by the ground-penetrating radar are received via the CAN bus; based on the received information, it is determined whether the predetermined control point meets the control conditions;

[0048] Step S3: When the predetermined control point meets the control condition, the vehicle is controlled to enter the working state through the chassis center; otherwise, the vehicle is controlled to move to the next predetermined control point.

[0049] Specifically, the vehicle display terminal serves as a human-computer interaction platform and can display a map for the user. The user selects different locations on the map as predetermined control points for the vehicle through the vehicle display terminal. The vehicle display terminal encapsulates the content selected by the user as a control instruction and transmits it to the CPU control module through the CAN bus.

[0050] Specifically, in step S1, the CAN bus is used to receive the user's control instructions from the vehicle display terminal, and the user's control instructions are parsed to obtain multiple predetermined control points included in the user's control instructions. For example, the predetermined control points include point A, point B, point C and point D. Each predetermined control point corresponds to a geographic coordinate. The four predetermined control points are sorted, and the vehicle moves in this order.

[0051] When the vehicle moves to any predetermined control point, an evaluation is performed on any predetermined control point to confirm whether the predetermined control point is suitable for control work. If the predetermined control point is evaluated to be suitable, the vehicle enters the working state and starts working; if the evaluation is not suitable, the vehicle is controlled to enter the next predetermined control point. At the same time, the reason why the predetermined control point is not suitable for control work can be fed back to the vehicle display terminal, and the surrounding environment information of the predetermined control point can be displayed.

[0052] Preferably, the sorting of the plurality of predetermined control points comprises:

[0053] Calculate the distance between the vehicle's initial point and each predetermined control point, and take the predetermined control point with the shortest distance as the first predetermined control point;

[0054] The distance between each of the remaining predetermined control points and the previous predetermined control point is calculated, and the predetermined control point with the shortest distance is used as the next predetermined control point, and so on, to determine the order of the remaining predetermined control points.

[0055] Specifically, when sorting multiple predetermined control points, the geographic coordinates of the vehicle at the time it receives the user's control command are used as the vehicle's initial point. Based on the parsed geographic coordinates of each predetermined control point, the distance between each predetermined control point and the vehicle's initial point is calculated. For example, the distances between the vehicle's initial point and points A, B, C, and D are calculated as LA, LB, LC, and LD, respectively. LA, LB, LC, and LD are compared, and the shortest distance is selected. The corresponding predetermined control point is selected as the first predetermined control point. If LC is the shortest, point C is selected as the first predetermined control point.

[0056] Specifically, when there are 4 predetermined control points, namely point A, point B, point C and point D, point A, point B, point C and point D need to be sorted. After calculating the first predetermined control point C, the second predetermined control point, the third predetermined control point and the fourth predetermined control point need to be obtained.

[0057] When calculating the second, third, and fourth predetermined control points, the distance between the previous predetermined control point and the remaining predetermined control points is calculated, and the predetermined control point with the shortest distance is used as the next predetermined control point. For example, when calculating the second predetermined control point, the distances between the previous predetermined control point C and the remaining predetermined control points A, B, and D are calculated as CA, CB, and CD, respectively. CA, CB, and CD are compared to select the shortest distance CB, and the remaining predetermined control point B corresponding to CB is used as the second predetermined control point. When calculating the third predetermined control point, the distances between the previous predetermined control point B and the remaining predetermined control points A and D are calculated as BA and BD, respectively. BA and BD are compared to select the shortest distance BA, and the remaining predetermined control point A corresponding to BA is used as the third predetermined control point. When calculating the fourth predetermined control point, if there is only one remaining predetermined control point, D, it is used as the last predetermined control point.

[0058] After sorting point A, point B, point C, and point D, the order of point C → point B → point A → point D can be obtained, and the vehicle can move in this order.

[0059] It is worth noting that sorting can enable vehicles to reach the predetermined control point as quickly as possible, saving as much time as possible during the movement of the vehicle and improving the working efficiency of the vehicle.

[0060] Specifically, in step S2, when the vehicle reaches a predetermined control point, the predetermined control point is evaluated. During the evaluation, the system receives surrounding image information and radar detection information from the surrounding environment sensing device via the AUTBUS bus, as well as road surface defect information and road surface structure information detected by the ground-penetrating radar via the CAN bus. Based on the received surrounding image information, radar detection information, road surface defect information, and road surface structure information, the system evaluates the predetermined control point to determine whether the control condition is met.

[0061] Specifically, the surrounding environment sensing device includes one or more of the following: a front camera, a left camera, a rear camera, a right camera, a rear wide-angle lidar, a left wide-angle lidar, a right wide-angle lidar, a front wide-angle lidar, an ultrasonic radar, and a millimeter-wave radar. The surrounding environment sensing device is used to obtain image information and radar detection information around the predetermined control point. Specifically, when the ground-penetrating radar detects the predetermined control point, it can obtain road surface defect information and road surface structure information at the predetermined control point.

[0062] It is worth noting that the vehicle's data processing capability can be improved by quickly receiving surrounding image information and radar detection information through the AUTBUS bus.

[0063] Preferably, the control conditions include ambient environment conditions and ground control conditions;

[0064] Determine whether the predetermined control point meets the surrounding environmental conditions, including:

[0065] Identifying each target in the surrounding image information based on a deep convolutional neural network to obtain the category of each target, determining the distance between each target and a predetermined control point in combination with the radar detection information, and determining the risk factor of the surrounding environment based on the category of each target and the distance of each target;

[0066] When the risk factor of the surrounding environment is lower than the preset risk threshold, it is determined that the predetermined control point meets the surrounding environment conditions.

[0067] Specifically, the control conditions include surrounding environment conditions and ground control conditions. Only when the predetermined control point satisfies both the surrounding environment conditions and the ground control conditions, the predetermined control point satisfies the control conditions. For example, Figure 1 As shown, in step S3, the predetermined control point that meets the control conditions is used as the evaluation-suitable predetermined control point, so that the vehicle can enter the working state at the predetermined control point.

[0068] Specifically, when determining whether the predetermined control point meets the surrounding environment conditions, each target in the surrounding image information is identified by a deep convolutional neural network to obtain the category to which each target belongs. The deep convolutional neural network can be any of the following networks:

[0069] Multi-view convolutional neural network MVCNN;

[0070] Grouped view convolutional neural network GVCNN;

[0071] Multi-view long short-term memory network MV-LSTM;

[0072] Multi-view deformation neural network MVTransformer.

[0073] Specifically, the deep convolutional neural network provided in the embodiment of the present invention is MVCNN (Multi-view Convolutional Neural Networks), GVCNN (Group-view Convolutional Neural Networks), MV-LSTM (Long Short-Term Memory) or MVTransformer (Multi-view Transformer Networks).

[0074] It is worth noting that the surrounding image information of the predetermined control point is quickly processed through a deep convolutional neural network to obtain the various targets around the predetermined control point and the category to which each target belongs. For example, the target can be an airplane, a drone, a village or a forest, etc.

[0075] Specifically, the distance between each target and a predetermined control point is determined through radar detection information.

[0076] Preferably, the risk factor of the surrounding environment of the predetermined control point is calculated by the following formula:

[0077]

[0078] Where D represents the risk factor of the surrounding environment of the predetermined control point, W i Indicates the risk level of the i-th target, L i Represents the distance between the i-th target and the predetermined control point, N represents the total number of targets; the danger level of the target is determined based on the category to which the target belongs.

[0079] Specifically, different danger levels are set for possible target categories in advance. For example, the danger level of an airplane is set to 100, the danger level of a drone is set to 30, the danger level of a forest is set to 5, and so on. It can be understood that the danger level of each category of target refers to the amount of damage that may be caused to the vehicle.

[0080] pass The system calculates the risk factor of the surrounding environment at the predetermined control point and compares it with a preset risk threshold. If the risk factor is lower than the threshold, the predetermined control point is considered to meet the surrounding environment conditions; otherwise, it does not meet the surrounding environment conditions. The preset risk threshold is a pre-set risk level based on historical vehicle environmental data and operating status. Below this risk level, the vehicle can operate stably in this environment without being disturbed by the external environment. Above this risk level, the vehicle faces significant safety risks and is not recommended for operation.

[0081] By using the risk factor of the surrounding environment as one of the control conditions, the risk factor of the vehicle's surrounding environment can be evaluated so that the vehicle can operate safely and stably.

[0082] This formula can fully utilize the data of various targets in the surrounding environment, quickly calculate the risk factor of the environment around the predetermined control point, and improve the safety of the vehicle when it is in operation.

[0083] Preferably, determining whether the predetermined control point meets the ground control condition includes:

[0084] determining a ground adaptability coefficient of a predetermined control point according to the pavement defect information and the pavement structure information;

[0085] When the ground adaptation coefficient of the predetermined control point is higher than a preset ground adaptation threshold, it is determined that the predetermined control point meets the ground control condition.

[0086] Specifically, pavement defect information and pavement structure information are obtained through ground penetrating radar detection. The pavement defect information includes the size of the defect area and the number of defects, and the pavement structure information includes the number of pavement layers, the thickness of each pavement layer, and the material composition of each pavement layer.

[0087] Preferably, the ground adaptation coefficient of the predetermined control point is calculated by the following formula:

[0088]

[0089] Where A represents the ground adaptation coefficient of the predetermined control point, H j represents the thickness of the jth layer of the road surface, X j represents the bearing capacity coefficient of the jth layer of the pavement, S k represents the defect area of ​​the kth defect, Q represents the number of pavement layers, and P represents the number of defects. The bearing capacity coefficient of each pavement layer is determined based on the material composition of each pavement layer.

[0090] Specifically, after obtaining the above-mentioned defect area size, defect number, pavement layer number, pavement layer thickness and material composition of each pavement layer through ground penetrating radar, the bearing capacity coefficient of each pavement layer is determined based on the material composition of each pavement layer, and then The ground adaptation coefficient of the predetermined control point can be calculated.

[0091] Specifically, the bearing capacity coefficient of each pavement layer is determined based on its material composition. Different pavement layers have different bearing capacity coefficients. For example, the bearing capacity coefficient of the cement layer is set to 10, while the bearing capacity coefficient of the asphalt layer is set to 5.

[0092] It is worth noting that the ground adaptation coefficient of the predetermined control point is compared with a preset ground adaptation threshold. If the ground adaptation coefficient of the predetermined control point is higher than the preset ground adaptation threshold, the predetermined control point is determined to meet the ground control condition; otherwise, the ground control condition is not met. The preset ground adaptation threshold is determined based on the weight of the vehicle; the heavier the vehicle, the larger the preset ground adaptation threshold. When the ground adaptation coefficient of the predetermined control point is higher than the preset ground adaptation threshold, the vehicle is prevented from tilting, shaking, or other dangerous conditions during operation, ensuring stable vehicle operation.

[0093] By using the ground adaptability coefficient as one of the control conditions, a judgment can be made on the ground bearing capacity of the predetermined control point, so that the vehicle will not be in danger when working at the predetermined control point that meets the ground control conditions, thereby improving the safety of the vehicle during operation.

[0094] This formula can be used to quickly and accurately evaluate the ground adaptability coefficient of a predetermined control point, thereby improving the safety of the vehicle during operation.

[0095] It is worth noting that the vehicle control method combining AUTBUS and CAN bus provided in an embodiment of the present invention obtains surrounding image information, radar detection information, road surface defect information and road surface structure information of a predetermined control point through the combined use of CAN bus and AUTBUS bus in the vehicle, and then calculates the hazard factor of the surrounding environment of the predetermined control point and the ground adaptability coefficient of the predetermined control point, and evaluates whether the predetermined control point meets the surrounding environment conditions and ground control conditions, so that the vehicle can be in a safe and stable environment when working, thereby improving the working efficiency of the vehicle.

[0096] Another specific embodiment provided by the present invention discloses a vehicle control system combining AUTBUS and CAN bus, such as Figure 2 As shown, the vehicle control system includes:

[0097] Vehicle display terminal, used to receive user control instructions;

[0098] The CPU control module is connected to the vehicle display terminal via the CAN bus, and is used to receive the user's control instructions from the vehicle display terminal, determine multiple predetermined control points according to the user's control instructions, sort the multiple predetermined control points, and control the vehicle to move to each predetermined control point in sequence;

[0099] The CPU control module is connected to the ground penetrating radar via a CAN bus and is used to obtain road surface defect information and road surface structure information from the ground penetrating radar; the CPU control module is connected to the surrounding environment sensing device via an AUTBUS bus and is used to obtain surrounding environment information and radar detection information from the surrounding environment sensing device and determine whether the predetermined control point meets the control condition;

[0100] The CPU control module is connected to the chassis center computer through the CAN bus. When the predetermined control point meets the control conditions, the chassis center computer controls the vehicle to enter the working state; otherwise, the vehicle is controlled to move to the next predetermined control point.

[0101] Specifically, such as Figure 2As shown, the vehicle display terminal is connected to the CPU control module via the CAN bus to send the user's control instructions to the CPU control module; the ground penetrating radar is connected to the CPU control module via the CAN bus to transmit the road surface defect information and road surface structure information to the CPU control module; the surrounding environment perception device is connected to the CPU control module via the AUTBUS bus to transmit the surrounding image information and radar detection information to the CPU control module; the chassis center is connected to the CPU control module via the CAN bus to receive the control instructions of the CPU control module, such as the vehicle control instructions.

[0102] Preferably, the surrounding environment sensing device includes one or more of the following:

[0103] Front camera, left camera, rear camera, right camera, rear wide-angle lidar, left wide-angle lidar, right wide-angle lidar, front wide-angle lidar, ultrasonic radar and millimeter-wave radar.

[0104] Specifically, the front camera, left camera, rear camera and right camera can be used to obtain surrounding image information, and then determine the various targets in the surrounding environment; the rear wide-angle lidar, left wide-angle lidar, right wide-angle lidar, front wide-angle lidar, ultrasonic radar and millimeter-wave radar can be used to obtain radar detection information, and then determine the distance between each target and the predetermined control point.

[0105] Preferably, the CPU control module is further connected to one or more of the following via an AUTBUS bus:

[0106] Battery management system, DCDC converter, body electronic stability system, network power distribution management system, air conditioning management system and human-machine interface.

[0107] Specifically, the battery management system is the vehicle's power source, used to provide power to the vehicle; the DCDC converter is a switching power supply chip, used for boosting and lowering voltage; the body electronic stability system monitors the vehicle's driving status and prevents the vehicle from deviating from the ideal trajectory when understeering or oversteering occurs during emergency obstacle avoidance or turning; the network power distribution management system provides power configuration for different components of the vehicle; the air conditioning management system manages the vehicle's air conditioning; and the human-machine interface is the vehicle's human-machine interface.

[0108] Compared with the prior art, the vehicle control method and system combining AUTBUS and CAN bus provided in the embodiment of the present invention quickly obtain and process the surrounding environment data and ground detection data of the vehicle's predetermined control points through the combination of CAN bus and AUTBUS bus, thereby improving the vehicle's processing and integration capabilities for massive data and increasing the safety of the vehicle during operation; at the same time, by judging whether the predetermined control points meet the surrounding environment conditions and ground control conditions, the vehicle can respond quickly when faced with unfamiliar and complex application scenarios, thereby improving the safety performance of the vehicle during use.

[0109] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0110] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A vehicle control method combining AUTBUS and CAN bus, characterized in that: The vehicle control method comprises: Receive user control commands from the vehicle display terminal via the CAN bus, determine multiple predetermined control points according to the user's control commands, sort the multiple predetermined control points, and control the vehicle to move to each predetermined control point in sequence; At each predetermined control point, surrounding image information and radar detection information detected by a surrounding environment sensing device are received via the AUTBUS bus, and road surface defect information and road surface structure information detected by a ground-penetrating radar are received via the CAN bus; based on the received information, it is determined whether the predetermined control point meets the control conditions; When the predetermined control point meets the control conditions, the vehicle is controlled by the chassis center computer to enter the working state; otherwise, the vehicle is controlled to move to the next predetermined control point.

2. The vehicle control method according to claim 1, characterized in that: The control conditions include surrounding environmental conditions and ground control conditions; Determine whether the predetermined control point meets the surrounding environmental conditions, including: Identifying each target in the surrounding image information based on a deep convolutional neural network to obtain the category of each target, determining the distance between each target and a predetermined control point in combination with the radar detection information, and determining the risk factor of the surrounding environment based on the category of each target and the distance of each target; When the risk factor of the surrounding environment is lower than the preset risk threshold, it is determined that the predetermined control point meets the surrounding environment conditions.

3. The vehicle control method according to claim 2, characterized in that: The risk factor of the surrounding environment of the predetermined control point is calculated by the following formula: Where D represents the risk factor of the surrounding environment of the predetermined control point, W i Indicates the risk level of the i-th target, L i Represents the distance between the i-th target and the predetermined control point, N represents the total number of targets; the danger level of the target is determined based on the category to which the target belongs.

4. The vehicle control method according to claim 2, wherein: Determine whether the predetermined control point meets the ground control conditions, including: determining a ground adaptability coefficient of a predetermined control point according to the pavement defect information and the pavement structure information; When the ground adaptation coefficient of the predetermined control point is higher than a preset ground adaptation threshold, it is determined that the predetermined control point meets the ground control condition.

5. The vehicle control method according to claim 4, characterized in that: The pavement defect information includes the size of the defect area and the number of defects; the pavement structure information includes the number of pavement layers, the thickness of each pavement layer and the material composition of each pavement layer.

6. The vehicle control method according to claim 5, characterized in that: The ground adaptation coefficient of the predetermined control point is calculated using the following formula: Where A represents the ground adaptation coefficient of the predetermined control point, H j represents the thickness of the jth layer of the road surface, X j represents the bearing capacity coefficient of the jth layer of the pavement, S k represents the defect area of ​​the kth defect, Q represents the number of pavement layers, and P represents the number of defects. The bearing capacity coefficient of each pavement layer is determined based on the material composition of each pavement layer.

7. The vehicle control method according to claim 1, characterized in that: The step of sorting the plurality of predetermined control points comprises: Calculate the distance between the vehicle's initial point and each predetermined control point, and take the predetermined control point with the shortest distance as the first predetermined control point; The distance between each of the remaining predetermined control points and the previous predetermined control point is calculated, and the predetermined control point with the shortest distance is used as the next predetermined control point, and so on, to determine the order of the remaining predetermined control points.

8. A vehicle control system combining AUTBUS and CAN bus, characterized in that: The vehicle control system includes: Vehicle display terminal, used to receive user control instructions; The CPU control module is connected to the vehicle display terminal via the CAN bus, and is used to receive the user's control instructions from the vehicle display terminal, determine multiple predetermined control points according to the user's control instructions, sort the multiple predetermined control points, and control the vehicle to move to each predetermined control point in sequence; The CPU control module is connected to the ground penetrating radar via a CAN bus and is used to obtain road surface defect information and road surface structure information from the ground penetrating radar; the CPU control module is connected to the surrounding environment sensing device via an AUTBUS bus and is used to obtain surrounding environment information and radar detection information from the surrounding environment sensing device and determine whether the predetermined control point meets the control condition; The CPU control module is connected to the chassis center computer through the CAN bus. When the predetermined control point meets the control conditions, the chassis center computer controls the vehicle to enter the working state; otherwise, the vehicle is controlled to move to the next predetermined control point.

9. The vehicle control system according to claim 8, characterized in that: The surrounding environment sensing device includes one or more of the following: Front camera, left camera, rear camera, right camera, rear wide-angle lidar, left wide-angle lidar, right wide-angle lidar, front wide-angle lidar, ultrasonic radar and millimeter-wave radar.

10. The vehicle control system according to claim 9, characterized in that: The CPU control module is also connected to one or more of the following via the AUTBUS bus: Battery management system, DCDC converter, body electronic stability system, network power distribution management system, air conditioning management system and human-machine interface.

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