Dynamic safety detection method, device and equipment for transport vehicle and storage medium

Abstract

The invention belongs to the technical field of vehicle safety, and discloses a transport vehicle dynamic safety detection method, device and equipment and a storage medium. The method comprises the following steps: acquiring position and posture information of a transport vehicle through a multi-source positioning module; sensing dynamic information of surrounding vehicles based on fusion of a millimeter wave radar array and binocular vision; acquiring cargo state information by using a distributed pressure sensing and three-dimensional laser scanning device; constructing a safety evaluation model; and determining the safety level of the transport vehicle according to the safety evaluation model based on the transport vehicle pose information, the surrounding vehicle dynamic information and the cargo state information. Through the above mode, multi-dimensional risk monitoring is realized, and at the same time, the risk levels are divided, so that the comprehensiveness and rationality of safety detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle safety, and particularly to a method, device, equipment and storage medium for dynamically detecting the safety of transport vehicles. Background Art

[0002] Due to its own particularity, if a traffic accident or leakage accident occurs during transportation, a transport vehicle will not only cause vehicle damage and human casualties, but also trigger some serious disaster accidents such as combustion, explosion, corrosion, and poisoning. At present, the path anomaly detection of transport vehicles relies on a single GPS signal, and the cargo status monitoring is only based on weight threshold alarms, resulting in a lack of a multi-dimensional risk coupling mechanism for safety detection, and the warning strategy uses a unified threshold without realizing dynamic risk level division.

[0003] The above content is only used to assist in understanding the technical solution of the present invention, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method, device, equipment and storage medium for dynamically detecting the safety of transport vehicles, aiming to solve the technical problems that the safety detection lacks a multi-dimensional risk coupling mechanism, the warning strategy uses a unified threshold, and the dynamic risk level division is not realized.

[0005] To achieve the above object, the present invention provides a method for dynamically detecting the safety of transport vehicles, and the method for dynamically detecting the safety of transport vehicles includes the following steps:

[0006] Obtain the pose information of the transport vehicle through a multi-source positioning module, and the multi-source positioning module integrates dual-frequency GPS, inertial navigation and roadside units;

[0007] Perceive the dynamic information of surrounding vehicles based on the fusion of a millimeter-wave radar array and binocular vision;

[0008] Obtain the cargo status information by using a distributed pressure sensing and three-dimensional laser scanning device;

[0009] Construct a safety assessment model, and the safety assessment model includes path risk, distance risk and cargo risk;

[0010] Determine the safety level of the transport vehicle according to the safety assessment model based on the pose information of the transport vehicle, the dynamic information of the surrounding vehicles and the cargo status information.

[0011] In some embodiments, the distributed pressure sensor is a flexible piezoelectric sensing array arranged on the bottom surface of the cargo box, and the step of obtaining the cargo status information by using a distributed pressure sensing and three-dimensional laser scanning device includes:

[0012] Monitoring the pressure distribution through the flexible piezoelectric sensing array to obtain the pressure information of the goods;

[0013] Constructing a three-dimensional point cloud model of the goods through a rotating laser scanner;

[0014] Fusing the pressure information of the goods with the three-dimensional point cloud model of the goods to obtain the goods status information.

[0015] In some embodiments, the safety assessment model R = α×Rp + β×Rd + γ×Rc, where Rp, Rd, and Rc are the risk parameters corresponding to the path risk, distance risk, and goods risk respectively, and α, β, and γ are the dynamic weight factors of the path risk, distance risk, and goods risk respectively.

[0016] In some embodiments, the determining the safety level of the transport vehicle based on the safety assessment model according to the transport vehicle pose information, the surrounding vehicle dynamic information, and the goods status information includes:

[0017] Determining the risk parameter corresponding to the path risk according to the transport vehicle pose information;

[0018] Determining the risk parameter corresponding to the distance risk according to the surrounding vehicle dynamic information;

[0019] Determining the risk parameter corresponding to the goods risk according to the goods status information;

[0020] Determining the safety level of the transport vehicle based on the safety assessment model according to the risk parameter corresponding to the path risk, the risk parameter corresponding to the distance risk, and the risk parameter corresponding to the goods risk.

[0021] In some embodiments, the determining the risk parameter corresponding to the path risk according to the transport vehicle pose information includes:

[0022] Obtaining the vehicle speed and the road curvature angle from the transport vehicle pose information;

[0023] Determining a dynamic safety threshold according to the vehicle speed and the road curvature angle, and the calculation formula of the dynamic safety threshold is Tp = v×(0.5 + 0.1×sin(θ)), where v represents the vehicle speed and θ represents the road curvature angle;

[0024] Obtaining the lateral offset of the vehicle from the transport vehicle pose information, and determining the risk parameter corresponding to the path risk based on the dynamic safety threshold and the lateral offset, and the calculation formula of the risk parameter is Rp = L - Tp, where L represents the lateral offset.

[0025] In some embodiments, the determining the risk parameter corresponding to the distance risk according to the surrounding vehicle dynamic information includes:

[0026] Obtain the relative speed and the actual vehicle distance between the target vehicle according to the surrounding vehicle dynamic information;

[0027] Calculate the risk parameter corresponding to the distance risk according to the relative speed and the actual vehicle distance, and the calculation formula of the risk parameter is Rd=(Vrel / D)×e a , where Vrel represents the relative speed, D represents the actual vehicle distance, and a represents the deceleration of the transport vehicle itself.

[0028] In some embodiments, the determining the risk parameter corresponding to the cargo risk according to the cargo status information includes:

[0029] Obtain the centroid offset of the cargo according to the cargo status information;

[0030] Calculate the ratio of the centroid offset of the cargo to the inertial momentum according to the centroid offset of the cargo, the mass of the cargo, and the reference moment of inertia. The calculation formula is η=(||ΔC||×m) / Ibase, where ΔC represents the centroid offset of the cargo, m represents the mass of the cargo, and Ibase represents the reference moment of inertia;

[0031] Determine the risk parameter corresponding to the cargo risk according to the ratio of the centroid offset of the cargo to the inertial momentum and the proportional threshold. The calculation formula is Rc = η - ηmax, where ηmax represents the proportional threshold.

[0032] In addition, to achieve the above object, the present invention also proposes a dynamic safety detection device for a transport vehicle, and the dynamic safety detection device for the transport vehicle includes:

[0033] An acquisition module, configured to obtain the pose information of the transport vehicle through a multi-source positioning module, and the multi-source positioning module integrates dual-frequency GPS, inertial navigation, and roadside units;

[0034] The acquisition module is configured to perceive the surrounding vehicle dynamic information based on the fusion of a millimeter-wave radar array and binocular vision;

[0035] The acquisition module is configured to obtain the cargo status information by using a distributed pressure sensing and three-dimensional laser scanning device;

[0036] A construction module, configured to construct a safety evaluation model, and the safety evaluation model includes path risk, distance risk, and cargo risk;

[0037] An evaluation module, configured to determine the safety level of the transport vehicle according to the safety evaluation model based on the pose information of the transport vehicle, the surrounding vehicle dynamic information, and the cargo status information.

[0038] In addition, to achieve the above object, the present invention further provides a dynamic safety detection device for a transport vehicle, where the dynamic safety detection device for a transport vehicle includes: a memory, a processor, and a dynamic safety detection program for a transport vehicle stored on the memory and executable on the processor, and the dynamic safety detection program for a transport vehicle is configured to implement the steps of the dynamic safety detection method for a transport vehicle as described above.

[0039] In addition, to achieve the above object, the present invention further provides a storage medium, on which a dynamic safety detection program for a transport vehicle is stored, and when the dynamic safety detection program for a transport vehicle is executed by a processor, it implements the steps of the dynamic safety detection method for a transport vehicle as described above.

[0040] The present invention obtains the pose information of a transport vehicle through a multi-source positioning module; perceives the dynamic information of surrounding vehicles based on the fusion of a millimeter-wave radar array and binocular vision; obtains the cargo state information by using a distributed pressure sensor and a three-dimensional laser scanning device; constructs a safety evaluation model; and determines the safety level of the transport vehicle based on the pose information of the transport vehicle, the dynamic information of the surrounding vehicles, and the cargo state information according to the safety evaluation model. By the above method, multi-dimensional risk monitoring is realized, and at the same time, the risk level is divided, improving the comprehensiveness and rationality of safety detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic flowchart of the first embodiment of the dynamic safety detection method for a transport vehicle of the present invention;

[0042] Figure 2 It is a structural block diagram of the first embodiment of the dynamic safety detection device for a transport vehicle of the present invention.

[0043] The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] The embodiments of the present invention provide a dynamic safety detection method for a transport vehicle. Refer to Figure 1 , Figure 1 It is a schematic flowchart of the first embodiment of a dynamic safety detection method for a transport vehicle of the present invention.

[0046] In this embodiment, the dynamic safety detection method for a transport vehicle includes the following steps:

[0047] Step S10: Obtain the pose information of the transport vehicle through a multi-source positioning module.

[0048] In this embodiment, the execution subject of this embodiment is a transportation vehicle dynamic safety detection device. Among them, the transportation vehicle dynamic safety detection device has functions such as data processing, data communication, and program operation. The transportation vehicle dynamic safety detection device can be a computer terminal device or other network devices. Of course, it can also be other devices with similar functions. This embodiment does not limit this.

[0049] It should be noted that currently, the path anomaly detection of transportation vehicles relies on a single GPS signal, and the cargo status monitoring is only based on weight threshold alarms, resulting in a lack of a multi-dimensional risk coupling mechanism for safety detection. Moreover, the early warning strategy uses a unified threshold and does not achieve dynamic risk level division.

[0050] To solve the above technical problems, in this embodiment, a multi-source positioning module is used to obtain the pose information of the transportation vehicle; the dynamic information of surrounding vehicles is perceived based on the fusion of a millimeter-wave radar array and binocular vision; a distributed pressure sensor and a three-dimensional laser scanning device are used to obtain the cargo status information; a safety assessment model is constructed; and according to the safety assessment model, the safety level of the transportation vehicle is determined based on the pose information of the transportation vehicle, the dynamic information of surrounding vehicles, and the cargo status information. Through the above method, multi-dimensional risk monitoring is realized, and at the same time, the risk level is divided, improving the comprehensiveness and rationality of safety detection. Specifically, it can be realized in the following way.

[0051] In specific implementation, to avoid the situation of obtaining vehicle positions by a single GPS, in this embodiment, a multi-source positioning module is used to obtain the pose information of the transportation vehicle. The multi-source positioning module integrates dual-frequency GPS, inertial navigation, and roadside units. Among them, dual-frequency GPS refers to using two different frequency GPS signals for positioning, such as the L1 (1575.42 MHz) and L2 (1227.60 MHz) frequency bands. Using dual-frequency signals can provide more accurate positioning results. Inertial navigation determines its position and attitude by measuring the acceleration and angular velocity of the vehicle, including accelerometers, gyroscopes, and (sometimes) magnetometers. The roadside unit (RSU) is part of the intelligent transportation system (ITS) and can provide communication between vehicles and infrastructure. The correction signals sent by these units can be used to correct the vehicle's positioning data and improve the accuracy of positioning.

[0052] Step S20: Perceive the dynamic information of surrounding vehicles based on the fusion of a millimeter-wave radar array and binocular vision.

[0053] In this embodiment, FMCW millimeter-wave radar is used. The relative speed and distance of the target vehicle are obtained through the FMCW millimeter-wave radar, and the three-dimensional contour of the target vehicle is determined using the stereo vision algorithm of binocular vision, thereby perceiving the dynamic information of surrounding vehicles.

[0054] It should be noted that the fusion algorithm adopted in this embodiment can be the weighted average method, Kalman filtering or machine learning method. Among them, the weighted average method assigns weights to different sensor data according to the reliability of the sensors. Kalman filtering is used to estimate the state of the target, combining the ranging and speed measurement information of the radar with the shape and position information of the vision. Machine learning methods such as neural networks can learn how to optimally fuse data from different sensors.

[0055] Step S30: Obtain the cargo state information by using the distributed pressure sensing and three-dimensional laser scanning device.

[0056] In a specific implementation, the pressure distribution is monitored through the flexible piezoelectric sensing array to obtain the pressure information of the cargo; a three-dimensional point cloud model of the cargo is constructed by a rotating laser scanner; the pressure information of the cargo is fused with the three-dimensional point cloud model of the cargo to obtain the cargo state information.

[0057] It should be noted that the fusion in this embodiment can be to segment the point cloud data into different regions or objects, each region or object corresponding to different parts of the cargo, and map the pressure distribution data to the corresponding point cloud regions. And according to the position of the sensors and the spatial distribution of the point cloud, the pressure values are assigned to the corresponding positions in the point cloud. At the same time, if the area covered by the pressure sensors does not exactly correspond to the area segmented by the point cloud, interpolation or weighted average methods can be used to assign the pressure values.

[0058] Step S40: Construct a safety assessment model.

[0059] It should be noted that the safety assessment model constructed in this embodiment includes path risk, distance risk and cargo risk. The safety assessment model is specifically R = α×Rp + β×Rd + γ×Rc, where Rp, Rd and Rc are the risk parameters corresponding to the path risk, distance risk and cargo risk respectively, and α, β, γ are the dynamic weight factors of the path risk, distance risk and cargo risk respectively. In addition, it should be emphasized that α, β, γ can be adjusted in real time according to road and weather conditions, α + β + γ = 1, and the specific parameter values can be set according to the actual situation, which are not limited in this embodiment.

[0060] Step S50: Determine the safety level of the transport vehicle based on the safety assessment model, the pose information of the transport vehicle, the dynamic information of the surrounding vehicles and the cargo state information.

[0061] In specific implementation, a risk parameter corresponding to a path risk is determined according to the pose information of the transport vehicle; a risk parameter corresponding to a distance risk is determined according to the dynamic information of surrounding vehicles; a risk parameter corresponding to a cargo risk is determined according to the cargo state information; and the safety level of the transport vehicle is determined according to the safety assessment model based on the risk parameter corresponding to the path risk, the risk parameter corresponding to the distance risk, and the risk parameter corresponding to the cargo risk.

[0062] In specific implementation, the vehicle speed and the road curvature angle are obtained from the pose information of the transport vehicle; a dynamic safety threshold is determined according to the vehicle speed and the road curvature angle, and the calculation formula of the dynamic safety threshold is Tp = v×(0.5 + 0.1×sin(θ)), where v represents the vehicle speed and θ represents the road curvature angle; the lateral offset of the vehicle is obtained from the pose information of the transport vehicle, and a risk parameter corresponding to the path risk is determined based on the dynamic safety threshold and the lateral offset, and the calculation formula of the risk parameter is Rp = L - Tp, where L represents the lateral offset.

[0063] Further, the relative speed and the actual vehicle distance from the target vehicle are obtained according to the dynamic information of the surrounding vehicles; a risk parameter corresponding to the distance risk is calculated according to the relative speed and the actual vehicle distance, and the calculation formula of the risk parameter is Rd = (Vrel / D)×e a , where Vrel represents the relative speed, D represents the actual vehicle distance, and a represents the deceleration of the transport vehicle itself.

[0064] Further, the centroid offset of the cargo is obtained according to the cargo state information; the ratio of the centroid offset of the cargo to the inertial momentum is calculated according to the centroid offset of the cargo, the cargo mass, and the reference moment of inertia, and the calculation formula is η = (||ΔC||×m) / Ibase, where ΔC represents the centroid offset of the cargo, m represents the cargo mass, and Ibase represents the reference moment of inertia; a risk parameter corresponding to the cargo risk is determined according to the ratio of the centroid offset of the cargo to the inertial momentum and the proportional threshold, and the calculation formula is Rc = η - ηmax, where ηmax represents the proportional threshold.

[0065] After calculating the risk parameters in the above three dimensions, substituting them into the safety assessment model can determine the safety level. The safety level is divided into multiple intervals, which respectively correspond to the values calculated by different safety assessment models. For example, when R is in the interval of R1 to R2, the corresponding safety level is level three; when R is in the interval of R2 to R3, the corresponding safety level is level two; when R is in the interval of R3 to R4, the corresponding safety level is level one, where R1 < R2 < R3 < R4, and the safety of a vehicle with safety level three is higher than that of a vehicle with safety level one. In addition, different control strategies can be adopted for different safety levels. For safety level two, a warning prompt is given, and for safety level one, active intervention is used to brake the vehicle.

[0066] In this embodiment, the multi-source positioning module is used to obtain the pose information of the transport vehicle; the millimeter-wave radar array and binocular vision are fused to sense the dynamic information of surrounding vehicles; the distributed pressure sensing and three-dimensional laser scanning device are used to obtain the cargo state information; a safety evaluation model is constructed; and according to the safety evaluation model, the safety level of the transport vehicle is determined based on the pose information of the transport vehicle, the dynamic information of surrounding vehicles, and the cargo state information. Through the above method, multi-dimensional risk monitoring is realized, and at the same time, the risk level is divided, improving the comprehensiveness and rationality of safety detection.

[0067] In addition, an embodiment of the present invention also provides a storage medium, on which a transport vehicle dynamic safety detection program is stored. When the transport vehicle dynamic safety detection program is executed by a processor, the steps of the transport vehicle dynamic safety detection method described above are implemented.

[0068] Refer to Figure 2 , Figure 2 which is the structural block diagram of the first embodiment of the transport vehicle dynamic safety detection device of the present invention.

[0069] As Figure 2 shown, the transport vehicle dynamic safety detection device proposed by the embodiment of the present invention includes:

[0070] An acquisition module 10, configured to obtain the pose information of the transport vehicle through a multi-source positioning module, and the multi-source positioning module integrates dual-frequency GPS, inertial navigation, and roadside units;

[0071] The acquisition module 10 is configured to sense the dynamic information of surrounding vehicles based on the fusion of a millimeter-wave radar array and binocular vision;

[0072] The acquisition module 10 is configured to obtain the cargo state information by using a distributed pressure sensing and three-dimensional laser scanning device;

[0073] A construction module 20, configured to construct a safety evaluation model, and the safety evaluation model includes path risk, distance risk, and cargo risk;

[0074] An evaluation module 30, configured to determine the safety level of the transport vehicle based on the safety evaluation model, the pose information of the transport vehicle, the dynamic information of surrounding vehicles, and the cargo state information.

[0075] In this embodiment, the multi-source positioning module is used to obtain the pose information of the transport vehicle; the millimeter-wave radar array and binocular vision are used to fuse and sense the dynamic information of surrounding vehicles; the distributed pressure sensing and three-dimensional laser scanning device are used to obtain the cargo state information; a safety evaluation model is constructed; and based on the safety evaluation model, the safety level of the transport vehicle is determined according to the pose information of the transport vehicle, the dynamic information of surrounding vehicles, and the cargo state information. Through the above method, multi-dimensional risk monitoring is realized, and at the same time, the risk level is divided, improving the comprehensiveness and rationality of safety detection.

[0076] In some embodiments, the distributed pressure sensor is a flexible piezoelectric sensing array arranged on the bottom surface of the cargo box, and the acquisition module 10 is used to monitor the pressure distribution through the flexible piezoelectric sensing array to obtain the pressure information of the cargo;

[0077] A three-dimensional point cloud model of the cargo is constructed by a rotating laser scanner;

[0078] The pressure information of the cargo is fused with the three-dimensional point cloud model of the cargo to obtain the cargo state information.

[0079] In some embodiments, the safety evaluation model is \(R = \alpha\times R_p+\beta\times R_d+\gamma\times R_c\), where \(R_p\), \(R_d\), and \(R_c\) are the risk parameters corresponding to path risk, distance risk, and cargo risk respectively, and \(\alpha\), \(\beta\), and \(\gamma\) are the dynamic weight factors of path risk, distance risk, and cargo risk respectively.

[0080] In some embodiments, the evaluation module 30 is used to determine the risk parameter corresponding to the path risk according to the pose information of the transport vehicle;

[0081] Determine the risk parameter corresponding to the distance risk according to the dynamic information of surrounding vehicles;

[0082] Determine the risk parameter corresponding to the cargo risk according to the cargo state information;

[0083] Based on the safety evaluation model, determine the safety level of the transport vehicle according to the risk parameter corresponding to the path risk, the risk parameter corresponding to the distance risk, and the risk parameter corresponding to the cargo risk.

[0084] In some embodiments, the evaluation module 30 is used to obtain the vehicle speed and road curvature angle from the pose information of the transport vehicle;

[0085] Determine the dynamic safety threshold according to the vehicle speed and the road curvature angle, and the calculation formula of the dynamic safety threshold is \(T_p = v\times(0.5 + 0.1\times\sin(\theta))\), where \(v\) represents the vehicle speed and \(\theta\) represents the road curvature angle;

[0086] Obtain the lateral offset of the vehicle from the pose information of the transport vehicle, and determine the risk parameter corresponding to the path risk based on the dynamic safety threshold and the lateral offset. The calculation formula of the risk parameter is Rp = L - Tp, where L represents the lateral offset.

[0087] In some embodiments, the evaluation module 30 is configured to obtain the relative speed and the actual vehicle distance between the target vehicle according to the dynamic information of the surrounding vehicles;

[0088] Calculate the risk parameter corresponding to the distance risk according to the relative speed and the actual vehicle distance. The calculation formula of the risk parameter is Rd = (Vrel / D) × e a , where Vrel represents the relative speed, D represents the actual vehicle distance, and a represents the deceleration of the transport vehicle itself.

[0089] In some embodiments, the evaluation module 30 is configured to obtain the centroid offset of the goods according to the goods status information;

[0090] Calculate the ratio of the centroid offset of the goods to the inertial momentum according to the centroid offset of the goods, the mass of the goods, and the reference moment of inertia. The calculation formula is η = (||ΔC|| × m) / Ibase, where ΔC represents the centroid offset of the goods, m represents the mass of the goods, and Ibase represents the reference moment of inertia;

[0091] Determine the risk parameter corresponding to the goods risk according to the ratio of the centroid offset of the goods to the inertial momentum and the ratio threshold. The calculation formula is Rc = η - ηmax, where ηmax represents the ratio threshold.

[0092] An embodiment of the present application further provides a transport vehicle dynamic safety detection device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus. The memory is used to store the transport vehicle dynamic safety detection program; the processor is used to implement the above-mentioned transport vehicle dynamic safety detection method when executing the program stored on the memory.

[0093] The communication bus mentioned in the above transport vehicle dynamic safety detection device may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0094] The communication interface is used for communication between the above transport vehicle dynamic safety detection device and other devices.

[0095] The memory may include a Random Access Memory (RAM), or may also include a Non-Volatile Memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0096] The aforementioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0097] In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium may be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a Solid State Disk (SSD)).

[0098] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0099] Each embodiment in this specification is described in a related manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and reference can be made to the corresponding part of the method embodiment for the related content.

[0100] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0101] It should be understood that the above is only an example for illustration and does not constitute any limitation to the technical solutions of the present invention. In specific applications, those skilled in the art can make settings according to needs, and the present invention does not limit this.

[0102] It should be noted that the above-described work process is only illustrative and does not limit the protection scope of the present invention. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and no limitation is imposed here.

[0103] In addition, for the technical details not described in detail in this embodiment, reference can be made to the method for dynamically detecting the safety of transportation vehicles provided in any embodiment of the present invention, and details will not be repeated here.

[0104] In addition, it should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or system comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or system comprising such element.

[0105] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0106] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as Read Only Memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0107] The above are only the preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

[0108] It can be understood that the system provided by the embodiments of the present invention corresponds to the method provided by the embodiments of the present invention. For the explanations, examples and beneficial effects of related content, reference can be made to the corresponding parts in the above method.

Claims

1. A dynamic safety detection method for a transportation vehicle, characterized in that, The dynamic safety detection method for the transport vehicle includes: Obtaining the pose information of the transport vehicle through a multi-source positioning module, which integrates dual-frequency GPS, inertial navigation, and roadside units; Perceiving the dynamic information of surrounding vehicles based on the fusion of a millimeter-wave radar array and binocular vision; Obtaining the cargo state information by using a distributed pressure sensing and three-dimensional laser scanning device; Constructing a safety evaluation model, which includes path risk, distance risk, and cargo risk; Determining the safety level of the transport vehicle according to the safety evaluation model based on the pose information of the transport vehicle, the dynamic information of surrounding vehicles, and the cargo state information.

2. The dynamic safety detection method for a transport vehicle according to claim 1, wherein, The distributed pressure sensor is a flexible piezoelectric sensing array arranged on the bottom surface of the cargo box. The obtaining of the cargo state information by using the distributed pressure sensing and three-dimensional laser scanning device includes: Monitoring the pressure distribution through the flexible piezoelectric sensing array to obtain the pressure information of the cargo; Constructing a three-dimensional point cloud model of the cargo by a rotating laser scanner; Fusing the pressure information of the cargo with the three-dimensional point cloud model of the cargo to obtain the cargo state information.

3. The dynamic safety detection method for a transport vehicle according to claim 1, wherein, The safety evaluation model R = α×Rp + β×Rd + γ×Rc, where Rp, Rd, and Rc are the risk parameters corresponding to path risk, distance risk, and cargo risk respectively, and α, β, and γ are the dynamic weight factors of path risk, distance risk, and cargo risk respectively.

4. The dynamic safety detection method for a transport vehicle according to claim 3, characterized in that, The determining of the safety level of the transport vehicle according to the safety evaluation model based on the pose information of the transport vehicle, the dynamic information of surrounding vehicles, and the cargo state information includes: Determining the risk parameter corresponding to the path risk according to the pose information of the transport vehicle; Determining the risk parameter corresponding to the distance risk according to the dynamic information of surrounding vehicles; Determining the risk parameter corresponding to the cargo risk according to the cargo state information; Determining the safety level of the transport vehicle according to the safety evaluation model based on the risk parameter corresponding to the path risk, the risk parameter corresponding to the distance risk, and the risk parameter corresponding to the cargo risk.

5. The dynamic safety detection method for a transport vehicle according to claim 4, wherein The determining of the risk parameter corresponding to the path risk according to the pose information of the transport vehicle includes: Obtaining the vehicle speed and road curvature angle from the pose information of the transport vehicle; Determining a dynamic safety threshold according to the vehicle speed and the road curvature angle. The calculation formula of the dynamic safety threshold is Tp = v×(0.5 + 0.1×sin(θ)), where v represents the vehicle speed and θ represents the road curvature angle; Obtaining the lateral offset of the vehicle from the pose information of the transport vehicle, and determining the risk parameter corresponding to the path risk based on the dynamic safety threshold and the lateral offset. The calculation formula of the risk parameter is Rp = L - Tp, where L represents the lateral offset.

6. The dynamic safety detection method for a transport vehicle according to claim 4, wherein The determining of the risk parameter corresponding to the distance risk according to the dynamic information of surrounding vehicles includes: Obtaining the relative speed and actual vehicle distance from the target vehicle according to the dynamic information of surrounding vehicles; Calculate a risk parameter corresponding to a distance risk based on the relative speed and the actual vehicle distance, and the calculation formula of the risk parameter is Rd = (Vrel / D) × e a , where Vrel represents the relative speed, D represents the actual vehicle distance, and a represents the deceleration of the transport vehicle itself.

7. The dynamic safety detection method for a transport vehicle according to claim 4, characterized in that, The determining of the risk parameter corresponding to the cargo risk according to the cargo state information includes: Obtaining the centroid offset of the cargo according to the cargo state information; Calculate the ratio of the cargo centroid offset to the inertial momentum according to the cargo centroid offset, the cargo mass, and the reference moment of inertia. The calculation formula is η = (||ΔC|| × m) / Ibase, where ΔC represents the cargo centroid offset, m represents the cargo mass, and Ibase represents the reference moment of inertia; Determine the risk parameter corresponding to the cargo risk according to the ratio of the cargo centroid offset to the inertial momentum and the ratio threshold. The calculation formula is Rc = η - ηmax, where ηmax represents the ratio threshold.

8. A dynamic safety detection device for a transportation vehicle, characterized in that, The dynamic safety detection device for the transport vehicle includes: An acquisition module, configured to obtain the pose information of the transport vehicle through a multi-source positioning module, and the multi-source positioning module integrates dual-frequency GPS, inertial navigation, and roadside units; The acquisition module is configured to perceive the dynamic information of surrounding vehicles based on the fusion of a millimeter-wave radar array and binocular vision; The acquisition module is configured to obtain the cargo state information by using a distributed pressure sensing and three-dimensional laser scanning device; A construction module, configured to construct a safety assessment model, and the safety assessment model includes path risk, distance risk, and cargo risk; An evaluation module, configured to determine the safety level of the transport vehicle based on the safety assessment model, the pose information of the transport vehicle, the dynamic information of surrounding vehicles, and the cargo state information.

9. A dynamic safety detection device for a transport vehicle, characterized in that, The dynamic safety detection device for the transport vehicle includes: a memory, a processor, and a dynamic safety detection program for the transport vehicle stored on the memory and executable on the processor. The dynamic safety detection program for the transport vehicle is configured to implement the steps of the dynamic safety detection method for the transport vehicle according to any one of claims 1 to 7.

10. A storage medium, characterized in that, A dynamic safety detection program for the transport vehicle is stored on the storage medium. When the dynamic safety detection program for the transport vehicle is executed by a processor, the steps of the dynamic safety detection method for the transport vehicle according to any one of claims 1 to 7 are implemented.

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