Vehicle avoidance method, device and equipment and vehicle

Abstract

The invention provides a vehicle avoiding method, device and equipment and a vehicle, and the vehicle avoiding method comprises the steps: when a vehicle is in a self-adaptive cruise control state following a front vehicle, if a target vehicle is detected, judging whether the vehicle and the target vehicle meet a preset parallel driving condition or not; if the own vehicle and the target vehicle meet the parallel driving condition, determining a target vehicle following distance of the own vehicle according to the current vehicle following distance of the own vehicle, the traffic environment information around the own vehicle and the characteristic parameters of the own vehicle; and controlling the own vehicle to run based on the target vehicle following distance, so that the own vehicle avoids the target vehicle. According to the invention, the vehicle and the target vehicle can be prevented from entering a long-time parallel driving state.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and specifically to a vehicle avoidance method, device, equipment and vehicle. Background Art

[0002] With the development of China's automobile market, driving assistance technology has become a research hotspot. Currently, common driving assistance functions include Adaptive Cruise Control (ACC). ACC mainly controls the vehicle to follow the vehicle in front and controls the vehicle to maintain a safe following distance with the vehicle in front during the process of following the vehicle in front, ensuring that the vehicle can complete driving assistance functions such as adaptive cruise and cruise driving on actual roads.

[0003] Currently, ACC usually determines the following distance based on the speed of the vehicle in front and the following distance time set by the driver. For example, if the speed of the vehicle in front is 20 meters per second and the fixed following distance time set by the driver is 2 seconds, the following distance is 20m / s * 2s = 40 meters.

[0004] The existing ACC only focuses on the speed of the vehicle in front and the following distance set by the driver when determining the following distance, ignoring the dynamic information of traffic participants on the road that affects the following distance. It has limitations, poses safety hazards and reduces the driving experience. Summary of the invention

[0005] In view of the above, it is necessary to propose a vehicle avoidance method, device, equipment and vehicle to solve the technical problems that the existing ACC has safety hazards and reduces the driving experience.

[0006] In a first aspect, the present application provides a vehicle avoidance method, the method comprising: when a self-vehicle is in an adaptive cruise control state following a preceding vehicle, if a target vehicle is detected, determining whether the self-vehicle and the target vehicle satisfy a preset parallel driving condition; if the self-vehicle and the target vehicle satisfy the parallel driving condition, determining a target following distance of the self-vehicle based on the current following distance of the self-vehicle, traffic environment information around the self-vehicle, and characteristic parameters of the self-vehicle; and controlling the driving of the self-vehicle based on the target following distance so that the self-vehicle avoids the target vehicle.

[0007] In the vehicle avoidance method of the above embodiment, by judging whether the self-vehicle and the target vehicle meet the preset parallel driving conditions, it is possible to identify whether there is a potential risk of parallel driving between the self-vehicle and the target vehicle. When there is a potential risk of parallel driving between the self-vehicle and the target vehicle, the target following distance is determined based on the current following distance of the self-vehicle, the traffic environment information around the self-vehicle, and the characteristic parameters of the self-vehicle, ensuring that ACC can determine the following distance in combination with the traffic environment information, thereby improving ACC's perception of the surrounding environment, being able to dynamically adjust the following distance between the self-vehicle and the vehicle in front, allowing the self-vehicle to avoid the target vehicle, such as a large vehicle, thereby preventing the self-vehicle and the target vehicle from entering a long-term parallel driving state, thereby improving the safety of ACC and the user's driving experience. In addition, the present application can adapt to different traffic environments, enhance the adaptability of ACC, and make ACC more intelligent.

[0008] In some embodiments of the present application, the traffic environment information includes image perception information, and determining the target following distance of the own vehicle based on the current following distance of the own vehicle, the traffic environment information around the own vehicle, and the characteristic parameters of the own vehicle includes: determining the longitudinal distance between the leading vehicle and the target vehicle, and the length of the target vehicle based on the image perception information; determining a preset avoidance strategy applicable to the own vehicle based on the current following distance, the length of the target vehicle, and the longitudinal distance; determining the target following distance based on the preset avoidance strategy, the longitudinal distance, the length of the target vehicle, and the characteristic parameters.

[0009] In some embodiments of the present application, determining the preset avoidance strategy applicable to the self-vehicle based on the current following distance, the length of the target vehicle, and the longitudinal distance includes: determining the difference between the current following distance and the longitudinal distance, and determining the ratio of the difference to the length of the target vehicle; if the ratio is less than or equal to a first preset ratio, determining that the preset avoidance strategy is a forward avoidance strategy, wherein the first preset ratio is the ratio of the front part of the target vehicle to the length of the target vehicle; if the ratio is greater than or equal to a second preset ratio, determining that the preset avoidance strategy is a rearward avoidance strategy, wherein the second preset ratio is the ratio of the body part of the target vehicle to the length of the target vehicle.

[0010] In some embodiments of the present application, the characteristic parameters include the length of the self-vehicle, and determining the target following distance based on the preset avoidance strategy, the longitudinal distance, the length of the target vehicle, and the characteristic parameters includes: if the preset avoidance strategy is the forward avoidance strategy, determining the target following distance according to the longitudinal distance and the length of the self-vehicle; if the preset avoidance strategy is the backward avoidance strategy, determining the target following distance according to the longitudinal distance and the length of the target vehicle.

[0011] In some embodiments of the present application, the method further includes: determining the driving speed of the leading vehicle and the driving speed of the target vehicle based on the radar perception information or the image perception information; wherein the parallel driving conditions include: the current following distance of the own vehicle is greater than the longitudinal distance and the current following distance of the own vehicle is less than the sum of the longitudinal distance and the length of the target vehicle, the driving speed of the leading vehicle is greater than a preset speed threshold and the difference between the driving speed of the leading vehicle and the driving speed of the target vehicle is less than or equal to a preset difference.

[0012] In some embodiments of the present application, the method further includes: adjusting the driving speed of the vehicle according to the current following distance and the target following distance, and adjusting the current following distance of the vehicle to the target following distance.

[0013] In some embodiments of the present application, the target vehicle is a large vehicle traveling in a lane adjacent to the lane where the ego vehicle is located.

[0014] In a second aspect, the present application also provides a vehicle avoidance device, which includes: a judgment module, which is used to judge whether the self-vehicle and the target vehicle meet a preset parallel driving condition if a target vehicle is detected when the self-vehicle is in an adaptive cruise control state following the front vehicle; a determination module, which is used to determine the target following distance of the self-vehicle according to the current following distance of the self-vehicle, the traffic environment information around the self-vehicle, and the characteristic parameters of the self-vehicle if the self-vehicle and the target vehicle meet the parallel driving condition; and a control module, which is used to control the driving of the self-vehicle based on the target following distance so that the self-vehicle avoids the target vehicle.

[0015] In a third aspect, the present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the vehicle avoidance method described in the above embodiment.

[0016] In a fourth aspect, the present application also provides a vehicle, which includes the electronic device described in the above embodiment.

[0017] It can be understood that the vehicle avoidance device of the second aspect, the electronic device of the third aspect and the vehicle of the fourth aspect provided above all correspond to the vehicle avoidance method of the first aspect. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding vehicle avoidance method provided above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of an application scenario of a vehicle avoidance method provided in one embodiment of the present application.

[0019] Figure 2 It is a flowchart of a vehicle avoidance method provided in one embodiment of the present application.

[0020] Figure 3 It is a detailed flowchart of a vehicle avoidance method provided in one embodiment of the present application.

[0021] Figure 4 It is a schematic diagram of an application scenario of the forward avoidance strategy provided in an embodiment of the present application.

[0022] Figure 5 It is a schematic diagram of an application scenario of the backward avoidance strategy provided in an embodiment of the present application.

[0023] Figure 6 It is a schematic diagram of the functional modules of a vehicle avoidance device provided in one embodiment of the present application.

[0024] Component Symbols Vehicle 1 Electronic devices 10 Memory 11 Processor 12 Vehicle Sensor 20 Vehicle avoidance device 100 Determination module 110 Determination module 120 Control module 130 The following specific implementation methods will further illustrate the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0025] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0026] In the embodiments of the present application, it should be noted that, unless otherwise clearly specified and limited, words such as "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" is intended to present related concepts in a specific way.

[0027] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or mutual communication; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0028] In the description of the present application, it should be noted that, unless otherwise expressly specified and limited, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In addition, in the description of the present application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically limited.

[0029] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0030] See also Figure 1 , is a schematic diagram of an application scenario of a vehicle avoidance method provided in an embodiment of the present application.

[0031] The embodiment of the present application provides a vehicle avoidance method, which can be applied to one or more electronic devices 10. The electronic device 10 is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field-programmable gate array (Field-Programmable Gate Array, FPGA), a digital processor (Digital Signal Processor, DSP), an embedded device, etc.

[0032] Specifically, the electronic device 10 is used for: when the vehicle is in an adaptive cruise control state following the vehicle ahead, if a target vehicle is detected, judging whether the vehicle and the target vehicle meet a preset parallel driving condition based on the current following distance of the vehicle and the traffic environment information around the vehicle; if the vehicle and the target vehicle meet the parallel driving condition, determining a target following distance of the vehicle based on the current following distance, traffic environment information, and characteristic parameters of the vehicle; and controlling the driving of the vehicle based on the target following distance so that the vehicle avoids the target vehicle.

[0033] In some embodiments of the present application, the electronic device 10 can be communicatively connected to a desktop computer, a notebook, a PDA, a cloud server, or other devices.

[0034] In some embodiments of the present application, the electronic device 10 can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touch pad, or a voice control device.

[0035] In some embodiments of the present application, the electronic device 10 may also include a network device and / or a client device. The network device includes but is not limited to a single network server, a server group consisting of multiple network servers, and a cloud server consisting of a large number of hosts or network servers based on cloud computing.

[0036] In some embodiments of the present application, the network where the electronic device 10 is located includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0037] In some embodiments of the present application, the present application also provides a vehicle 1, which includes an electronic device 10. The electronic device 10 may be an onboard device of the vehicle 1, such as a body control module (BCM), a vehicle control unit (VCU), etc.

[0038] In some embodiments of the present application, the vehicle 1 may further include an on-board sensor 20 , and the on-board sensor 20 is used to collect traffic environment information around the vehicle 1 .

[0039] See also Figure 2 , is a schematic diagram of the steps of a vehicle avoidance method provided in one embodiment of the present application.

[0040] Specifically, the vehicle avoidance method includes the following steps. According to different requirements, the order of some steps in the flowchart can be changed, and some steps can be omitted.

[0041] Step S10: When the vehicle is in an adaptive cruise control state following the preceding vehicle, a target vehicle is detected.

[0042] In some embodiments of the present application, Figure 4 As shown, the ego vehicle refers to the vehicle 1 currently being controlled by the electronic device 10, the front vehicle refers to the vehicle that is traveling in the same lane as the ego vehicle, is located in front of the ego vehicle and is being followed by the ego vehicle, and the target vehicle includes but is not limited to large vehicles traveling in lanes adjacent to the lane where the ego vehicle is located, such as vans, trucks, buses, tank trucks, trailers, fire trucks, ambulances, military vehicles, agricultural vehicles, etc.

[0043] In some embodiments of the present application, adaptive cruise control (ACC) is mainly used to control the vehicle to follow the vehicle in front, and to control the vehicle to maintain a safe following distance with the vehicle in front during the process of following the vehicle in front, so as to ensure that the vehicle can complete adaptive cruise control, cruising driving and other driving assistance functions on the actual road.

[0044] In some embodiments of the present application, the electronic device 10 can detect the target vehicle based on the traffic environment information around the vehicle. The traffic environment information includes but is not limited to image perception information.

[0045] The vehicle-mounted sensor 20 of the vehicle may include a camera, which is used to collect image perception information around the vehicle. The camera may include but is not limited to a periscopic camera and a surround camera. The camera may also collect environmental images at a preset acquisition frame rate. For example, an environmental image is collected every 0.5 seconds.

[0046] In some embodiments of the present application, the electronic device 10 detects the target vehicle based on the image perception information. Specifically, the electronic device 10 can analyze the image perception information using a deep learning model to obtain the detection information of the target vehicle. The specific steps may include: first preprocessing the image perception information, such as image cleaning, image enhancement, image standardization, etc. Then, according to the characteristics of the target vehicle and the detection requirements, select a suitable deep learning model, such as a convolutional neural network (CNN), a recurrent neural network (RNN), etc. Then, the deep learning model is trained using a preset image perception information data set, wherein the image perception information data set includes multiple image perception information with annotation information. For example, the annotation information includes but is not limited to a combination of one or more of the following information: the annotation box of the target vehicle in the image perception information, the boundary contour of the target vehicle, the characteristic points of the target vehicle, the size requirements of the target vehicle (such as length, width, height), etc. Then, the image perception information collected by the camera is input into the trained deep learning model, and the deep learning model performs target detection on the input image perception information to obtain the detection result. The electronic device 10 determines whether there is a target vehicle in the adjacent lane of the vehicle based on the detection result of the image perception information.

[0047] In the above embodiments, the automatic detection of target vehicles is achieved through the application of deep learning models, which greatly improves the accuracy of target vehicle detection.

[0048] Step S11, if a target vehicle is detected, determine whether the self-vehicle and the target vehicle meet the preset parallel driving conditions.

[0049] In some embodiments of the present application, traffic environment information may also include radar perception information.

[0050] In some embodiments of the present application, the vehicle-mounted sensor 20 of the vehicle may also include a radar. The radar is used to collect radar perception information around the vehicle.

[0051] In some embodiments of the present application, the vehicle avoidance method further includes: determining the driving speed of the preceding vehicle and the driving speed of the target vehicle based on radar perception information and / or image perception information.

[0052] Specifically, the electronic device 10 can determine the driving speed of the preceding vehicle and the driving speed of the target vehicle according to the radar sensing information, wherein the radar sensing information includes the driving speed of the preceding vehicle and the driving speed of the target vehicle.

[0053] In some embodiments of the present application, the vehicle avoidance method further includes: determining the longitudinal distance between the leading vehicle and the target vehicle, and the length of the target vehicle based on the image perception information.

[0054] Specifically, the image perception information includes a first environment image in front of the vehicle and a second environment image on the side of the vehicle. The electronic device 10 can use a deep learning model to determine the position and depth of the front vehicle in the first environment image based on the first environment image, and use a deep learning model to determine the position, depth, and length of the target vehicle in the second environment based on the second environment image. The position of the front vehicle in the real world is determined based on the position and depth of the front vehicle in the first environment image, and the position of the target vehicle in the real world is determined based on the position and depth of the target vehicle in the second environment. The longitudinal distance between the front vehicle and the target vehicle is determined based on the position of the front vehicle in the real world and the position of the target vehicle in the real world.

[0055] In some embodiments of the present application, the parallel driving conditions include: the current following distance of the own vehicle is greater than the longitudinal distance and the current following distance of the own vehicle is less than the sum of the longitudinal distance and the length of the target vehicle, the driving speed of the leading vehicle is greater than a preset speed threshold (for example, 80km / h) and the difference between the driving speed of the leading vehicle and the driving speed of the target vehicle is less than or equal to a preset difference (for example, 1m / s).

[0056] In some embodiments of the present application, when the ego vehicle and the target vehicle meet the preset parallel driving condition, it indicates that the ego vehicle and the target vehicle will enter a parallel driving state within a period of time in the future.

[0057] In some embodiments of the present application, if the self-vehicle and the target vehicle meet the parallel driving condition, the electronic device 10 returns to execute step S10.

[0058] In some embodiments of the present application, if the ego vehicle and the target vehicle meet the parallel driving condition, the electronic device 10 executes step S12.

[0059] Step S12, determining a target following distance of the vehicle according to the current following distance of the vehicle, the traffic environment information around the vehicle, and the characteristic parameters of the vehicle.

[0060] In some embodiments of the present application, step S12, determining the target following distance of the own vehicle based on the current following distance of the own vehicle, the traffic environment information around the own vehicle, and the characteristic parameters of the own vehicle specifically includes: determining the longitudinal distance between the front vehicle and the target vehicle, and the length of the target vehicle based on the image perception information; determining a preset avoidance strategy applicable to the own vehicle based on the current following distance, the length of the target vehicle, and the longitudinal distance; determining the target following distance based on the preset avoidance strategy, the longitudinal distance, the length of the target vehicle, and the characteristic parameters.

[0061] In some embodiments of the present application, determining the avoidance strategy based on the current following distance of the vehicle, the length of the target vehicle, and the longitudinal distance includes: determining the difference between the current following distance of the vehicle and the longitudinal distance, and determining the ratio of the difference to the length of the target vehicle; if the ratio is less than or equal to a first preset ratio, determining the avoidance strategy to be a forward avoidance strategy, wherein the first preset ratio is the ratio of the front part of the target vehicle to the length of the target vehicle; if the ratio is greater than or equal to a second preset ratio, determining the avoidance strategy to be a rearward avoidance strategy, wherein the second preset ratio is the ratio of the body part of the target vehicle to the length of the target vehicle.

[0062] In some embodiments of the present application, the driving parameters of the vehicle also include the length of the vehicle, and determining the target following distance based on the avoidance strategy includes: when the avoidance strategy is a forward avoidance strategy, determining the target following distance based on the longitudinal distance and the length of the vehicle; when the avoidance strategy is a backward avoidance strategy, determining the target following distance based on the longitudinal distance and the length of the target vehicle.

[0063] It should be noted that how to determine the target following distance of the vehicle in the subsequent following process based on the current following distance of the vehicle, the driving parameters of the preceding vehicle, and the driving parameters of the target vehicle Figure 3 The steps shown are described in detail and will not be repeated here to avoid repetition.

[0064] Step S13, controlling the driving of the vehicle based on the target following distance so that the vehicle avoids the target vehicle.

[0065] In some embodiments of the present application, the vehicle avoidance method may further include: adjusting the driving speed of the vehicle according to the current following distance of the vehicle and the target following distance, and adjusting the current following distance of the vehicle to the target following distance.

[0066] Specifically, if the current following distance of the vehicle is greater than the target following distance, the electronic device 10 controls the vehicle to increase the driving speed. If the current following distance of the vehicle is less than the target following distance, the electronic device 10 controls the vehicle to reduce the driving speed.

[0067] In the vehicle avoidance method of the above embodiment, by judging whether the self-vehicle and the target vehicle meet the preset parallel driving conditions, it is possible to identify whether there is a potential risk of parallel driving between the self-vehicle and the target vehicle. When there is a potential risk of parallel driving between the self-vehicle and the target vehicle, the target following distance is determined based on the current following distance of the self-vehicle, the traffic environment information around the self-vehicle, and the characteristic parameters of the self-vehicle, ensuring that ACC can determine the following distance in combination with the traffic environment information, thereby improving ACC's perception of the surrounding environment, being able to dynamically adjust the following distance between the self-vehicle and the vehicle in front, allowing the self-vehicle to avoid the target vehicle, such as a large vehicle, thereby preventing the self-vehicle and the target vehicle from entering a long-term parallel driving state, thereby improving the safety of ACC and the user's driving experience. In addition, the present application can adapt to different traffic environments, enhance the adaptability of ACC, and make ACC more intelligent.

[0068] See also Figure 3 , which is a detailed flow chart of a vehicle avoidance method provided in one embodiment of the present application.

[0069] This embodiment is a specific description of step S11 in the above embodiment, and further describes how to determine the target following distance of the vehicle based on the current following distance of the vehicle, the traffic environment information around the vehicle, and the characteristic parameters of the vehicle. Specifically, the following steps are included: Step S110, determining the longitudinal distance between the front vehicle and the target vehicle, and the length of the target vehicle according to the image perception information.

[0070] Specifically, the image perception information includes a first environment image in front of the vehicle and a second environment image on the side of the vehicle. The electronic device 10 can use a deep learning model to determine the position and depth of the front vehicle in the first environment image based on the first environment image, and use a deep learning model to determine the position, depth, and length of the target vehicle in the second environment based on the second environment image. The position of the front vehicle in the real world is determined based on the position and depth of the front vehicle in the first environment image, and the position of the target vehicle in the real world is determined based on the position and depth of the target vehicle in the second environment. The longitudinal distance between the front vehicle and the target vehicle is determined based on the position of the front vehicle in the real world and the position of the target vehicle in the real world.

[0071] Step S111, determining a preset avoidance strategy applicable to the own vehicle according to the current following distance, the length of the target vehicle, and the longitudinal distance.

[0072] In some embodiments of the present application, step S111, determining a preset avoidance strategy applicable to the vehicle according to the current following distance, the length of the target vehicle, and the longitudinal distance specifically includes: Determine the difference between the current following distance S1 and the longitudinal distance S1, and determine the ratio of the difference to the length L2 of the target vehicle; If the ratio is less than or equal to the first preset ratio (for example, 20%), it indicates that the current position of the vehicle is close to the front part of the target vehicle, and the preset avoidance strategy is determined to be the forward avoidance strategy, that is, (S1-S2) / L2≤20%. The first preset ratio is the ratio of the front part of the target vehicle to the length of the target vehicle.

[0073] If the ratio is greater than or equal to the second preset ratio (e.g., 80%), it indicates that the current position of the vehicle is close to the rear of the target vehicle, and the preset avoidance strategy is determined to be the rear avoidance strategy, that is, (S1-S2) / L2≥80%. The second preset ratio is the ratio of the body of the target vehicle to the length of the target vehicle.

[0074] Step S112, determining the target following distance based on the preset avoidance strategy, the longitudinal distance, the length of the target vehicle, and the characteristic parameters.

[0075] In some embodiments of the present application, the characteristic parameter of the vehicle includes the length L1 of the vehicle.

[0076] In some embodiments of the present application, step S221, determining the target following distance based on the preset avoidance strategy, the longitudinal distance, the length of the target vehicle, and the characteristic parameters specifically includes: If the preset avoidance strategy is the forward avoidance strategy, the target following distance is determined based on the longitudinal distance and the length of the vehicle. Figure 4 As shown, the target following distance S=S2-L1, so that the ego vehicle is located in front of the target vehicle.

[0077] If the preset avoidance strategy is the backward avoidance strategy, the target following distance is determined based on the longitudinal distance and the length of the target vehicle. Figure 5 As shown, the target following distance S=S2+L2, so that the ego vehicle is located behind the target vehicle.

[0078] In the above embodiment, according to the relative position of the ego vehicle and the target vehicle (for example, the current position of the ego vehicle is close to the front part or the rear part of the target vehicle), the forward avoidance strategy or the backward avoidance strategy is intelligently selected, so that the avoidance behavior of the ego vehicle is more reasonable and effective, reducing the operating burden of the driver and improving the accuracy and reliability of the avoidance strategy. By considering the length of the ego vehicle and the target vehicle and adjusting the following distance in real time, it is possible to quickly respond to changes in the traffic environment and improve the response speed of ACC.

[0079] See also Figure 6 , is a schematic diagram of the functional modules of a vehicle avoidance device 100 provided in one embodiment of the present application.

[0080] In this embodiment, based on the above Figure 2The present application also provides a vehicle avoidance device 100 with the same concept as the vehicle avoidance method in the illustrated embodiment, which can be used to perform the above-mentioned vehicle avoidance method. For ease of explanation, the schematic diagram of the composition of the embodiment of the vehicle avoidance device 100 only shows the parts related to the embodiment of the present application. Those skilled in the art can understand that the illustrated structure does not constitute a limitation on the vehicle avoidance device 100, which can include more or fewer components than shown in the illustration, or combine certain components, or arrange the components differently.

[0081] Specifically, the vehicle avoidance device 100 provided in the embodiment of the present application includes a judgment module 110 , a determination module 120 , and a recommendation module 130 .

[0082] The judgment module 110 is used to judge whether the self-vehicle and the target vehicle meet the preset parallel driving conditions according to the current following distance of the self-vehicle and the traffic environment information of the self-vehicle when the self-vehicle is in the state of adaptive cruise control following the front vehicle, if a target vehicle is detected.

[0083] In some embodiments of the present application, Figure 4 As shown, the ego vehicle refers to the vehicle 1 currently being controlled by the electronic device 10, the front vehicle refers to the vehicle that is traveling in the same lane as the ego vehicle, is located in front of the ego vehicle and is being followed by the ego vehicle, and the target vehicle includes but is not limited to large vehicles traveling in lanes adjacent to the lane where the ego vehicle is located, such as vans, trucks, buses, tank trucks, trailers, fire trucks, ambulances, military vehicles, agricultural vehicles, etc.

[0084] In some embodiments of the present application, adaptive cruise control (ACC) is mainly used to control the vehicle to follow the vehicle in front, and to control the vehicle to maintain a safe following distance with the vehicle in front during the process of following the vehicle in front, so as to ensure that the vehicle can complete adaptive cruise control, cruising driving and other driving assistance functions on the actual road.

[0085] In some embodiments of the present application, the electronic device 10 can detect the target vehicle based on the traffic environment information around the vehicle. The traffic environment information includes but is not limited to image perception information.

[0086] The vehicle-mounted sensor 20 of the vehicle may include a camera, which is used to collect image perception information around the vehicle. The camera may include but is not limited to a periscopic camera and a surround camera. The camera may also collect environmental images at a preset acquisition frame rate. For example, an environmental image is collected every 0.5 seconds.

[0087] In some embodiments of the present application, the electronic device 10 detects the target vehicle based on the image perception information. Specifically, the electronic device 10 can analyze the image perception information using a deep learning model to obtain the detection information of the target vehicle. The specific steps may include: first preprocessing the image perception information, such as image cleaning, image enhancement, image standardization, etc. Then, according to the characteristics of the target vehicle and the detection requirements, select a suitable deep learning model, such as a convolutional neural network (CNN), a recurrent neural network (RNN), etc. Then, the deep learning model is trained using a preset image perception information data set, wherein the image perception information data set includes multiple image perception information with annotation information. For example, the annotation information includes but is not limited to a combination of one or more of the following information: the annotation box of the target vehicle in the image perception information, the boundary contour of the target vehicle, the characteristic points of the target vehicle, the size requirements of the target vehicle (such as length, width, height), etc. Then, the image perception information collected by the camera is input into the trained deep learning model, and the deep learning model performs target detection on the input image perception information to obtain the detection result. The electronic device 10 determines whether there is a target vehicle in the adjacent lane of the vehicle based on the detection result of the image perception information.

[0088] In the above embodiments, the automatic detection of target vehicles is achieved through the application of deep learning models, which greatly improves the accuracy of target vehicle detection.

[0089] In some embodiments of the present application, traffic environment information may also include radar perception information.

[0090] In some embodiments of the present application, the vehicle-mounted sensor 20 of the vehicle may also include a radar. The radar is used to collect radar perception information around the vehicle.

[0091] In some embodiments of the present application, the vehicle avoidance method further includes: determining the driving speed of the preceding vehicle and the driving speed of the target vehicle based on radar perception information and / or image perception information.

[0092] Specifically, the electronic device 10 can determine the driving speed of the preceding vehicle and the driving speed of the target vehicle according to the radar sensing information, wherein the radar sensing information includes the driving speed of the preceding vehicle and the driving speed of the target vehicle.

[0093] In some embodiments of the present application, the vehicle avoidance method further includes: determining the longitudinal distance between the leading vehicle and the target vehicle, and the length of the target vehicle based on the image perception information.

[0094] Specifically, the image perception information includes a first environment image in front of the vehicle and a second environment image on the side of the vehicle. The electronic device 10 can use a deep learning model to determine the position and depth of the front vehicle in the first environment image based on the first environment image, and use a deep learning model to determine the position, depth, and length of the target vehicle in the second environment based on the second environment image. The position of the front vehicle in the real world is determined based on the position and depth of the front vehicle in the first environment image, and the position of the target vehicle in the real world is determined based on the position and depth of the target vehicle in the second environment. The longitudinal distance between the front vehicle and the target vehicle is determined based on the position of the front vehicle in the real world and the position of the target vehicle in the real world.

[0095] In some embodiments of the present application, the parallel driving conditions include: the current following distance of the own vehicle is greater than the longitudinal distance and the current following distance of the own vehicle is less than the sum of the longitudinal distance and the length of the target vehicle, the driving speed of the leading vehicle is greater than a preset speed threshold (for example, 80km / h) and the difference between the driving speed of the leading vehicle and the driving speed of the target vehicle is less than or equal to a preset difference (for example, 1m / s).

[0096] In some embodiments of the present application, when the ego vehicle and the target vehicle meet the preset parallel driving condition, it indicates that the ego vehicle and the target vehicle will enter a parallel driving state within a period of time in the future.

[0097] The determination module 120 is used to determine the target following distance of the ego vehicle according to the current following distance, traffic environment information, and characteristic parameters of the ego vehicle if the ego vehicle and the target vehicle meet the parallel driving condition.

[0098] In some embodiments of the present application, determining the target following distance of the own vehicle based on the current following distance of the own vehicle, the traffic environment information around the own vehicle, and the characteristic parameters of the own vehicle specifically includes: determining the longitudinal distance between the front vehicle and the target vehicle, and the length of the target vehicle based on image perception information; determining a preset avoidance strategy applicable to the own vehicle based on the current following distance, the length of the target vehicle, and the longitudinal distance; determining the target following distance based on the preset avoidance strategy, the longitudinal distance, the length of the target vehicle, and the characteristic parameters.

[0099] In some embodiments of the present application, determining the avoidance strategy based on the current following distance of the vehicle, the length of the target vehicle, and the longitudinal distance includes: determining the difference between the current following distance of the vehicle and the longitudinal distance, and determining the ratio of the difference to the length of the target vehicle; if the ratio is less than or equal to a first preset ratio, determining the avoidance strategy to be a forward avoidance strategy, wherein the first preset ratio is the ratio of the front part of the target vehicle to the length of the target vehicle; if the ratio is greater than or equal to a second preset ratio, determining the avoidance strategy to be a rearward avoidance strategy, wherein the second preset ratio is the ratio of the body part of the target vehicle to the length of the target vehicle.

[0100] In some embodiments of the present application, the driving parameters of the vehicle also include the length of the vehicle, and determining the target following distance based on the avoidance strategy includes: when the avoidance strategy is a forward avoidance strategy, determining the target following distance based on the longitudinal distance and the length of the vehicle; when the avoidance strategy is a backward avoidance strategy, determining the target following distance based on the longitudinal distance and the length of the target vehicle.

[0101] It should be noted that how to determine the target following distance of the vehicle based on the current following distance of the vehicle, the driving parameters of the preceding vehicle, and the driving parameters of the target vehicle is described in the above Figure 3 The steps shown are described in detail and will not be repeated here to avoid repetition.

[0102] The control module 130 is used to control the driving of the ego vehicle based on the target following distance so that the ego vehicle avoids the target vehicle.

[0103] In some embodiments of the present application, the vehicle avoidance method may further include: adjusting the driving speed of the vehicle according to the current following distance of the vehicle and the target following distance, and adjusting the current following distance of the vehicle to the target following distance.

[0104] Specifically, if the current following distance of the vehicle is greater than the target following distance, the electronic device 10 controls the vehicle to increase the driving speed. If the current following distance of the vehicle is less than the target following distance, the electronic device 10 controls the vehicle to reduce the driving speed.

[0105] In the vehicle avoidance device 100 of the above embodiment, by judging whether the self-vehicle and the target vehicle meet the preset parallel driving conditions, it is possible to identify whether the self-vehicle and the target vehicle have a potential risk of parallel driving. When there is a potential risk of parallel driving between the self-vehicle and the target vehicle, the target following distance is determined based on the current following distance of the self-vehicle, the traffic environment information around the self-vehicle, and the characteristic parameters of the self-vehicle, ensuring that ACC can determine the following distance in combination with the traffic environment information, thereby improving ACC's perception of the surrounding environment, and being able to dynamically adjust the following distance between the self-vehicle and the vehicle in front, so that the self-vehicle avoids the target vehicle, such as a large vehicle, thereby preventing the self-vehicle and the target vehicle from entering a long-term parallel driving state, thereby improving the safety of ACC and the user's driving experience. In addition, the present application can adapt to different traffic environments, enhance the adaptability of ACC, and make ACC more intelligent.

[0106] Combination Figure 1 As shown, in some embodiments of the present application, the electronic device 10 includes, but is not limited to, a memory 11, a processor 12, and a computer program stored in the memory 11 and executable on the processor 12, such as a vehicle avoidance program. When the computer program is executed by the processor, the vehicle avoidance method as described in the above embodiment is implemented.

[0107] Figure 1 Only the electronic device 10 having the memory 11 and the processor 12 is shown, and those skilled in the art can understand that Figure 1 The structure shown does not constitute a limitation on the electronic device 10 , and the electronic device 10 may include fewer or more components than shown in the figure, or combine some components, or arrange the components differently.

[0108] The memory 11 in the electronic device 10 stores multiple computer-readable instructions to implement a vehicle avoidance method, and the processor 12 can execute multiple instructions to achieve: when the self-vehicle is in an adaptive cruise control state following the front vehicle, if a target vehicle is detected, it is determined whether the self-vehicle and the target vehicle meet the preset parallel driving condition based on the current following distance of the self-vehicle and the traffic environment information around the self-vehicle; if the self-vehicle and the target vehicle meet the parallel driving condition, the target following distance of the self-vehicle is determined based on the current following distance, traffic environment information, and characteristic parameters of the self-vehicle; based on the target following distance, the self-vehicle is controlled to avoid the target vehicle.

[0109] Specifically, the specific implementation method of the processor 12 for the above instructions can refer to Figure 2 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0110] Those skilled in the art will appreciate that the schematic diagram is merely an example of the electronic device 10 and does not constitute a limitation on the electronic device 10. The electronic device 10 may be a bus-type structure or a star-type structure. The electronic device 10 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components. For example, the electronic device 10 may also include input and output devices, network access devices, etc.

[0111] It should be noted that the electronic device 10 is only an example, and other existing or future electronic products that are suitable for the present application should also be included in the protection scope of the present application and included here by reference.

[0112] Among them, the memory 11 includes at least one type of computer-readable storage medium, and the computer-readable storage medium can be non-volatile or volatile. Computer-readable storage media include flash memory, mobile hard disk, multimedia card, card-type memory (such as SD memory, DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 10, such as a mobile hard disk of the electronic device 10. In other embodiments, the memory 11 can also be an external storage device of the electronic device 10, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 10. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 10, such as a code of a vehicle avoidance program, etc., but also can be used to temporarily store data that has been output or is to be output.

[0113] In some embodiments, the processor 12 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 12 is the control core (Control Unit) of the electronic device 10, and uses various interfaces and lines to connect various components of the entire electronic device 10, and executes various functions and processes data of the electronic device 10 by running or executing programs or modules stored in the memory 11 (for example, executing a vehicle avoidance program, etc.), and calling data stored in the memory 11.

[0114] The processor 12 executes the operating system of the electronic device 10 and various installed applications. The processor 12 executes the application to implement the steps in each of the above-mentioned vehicle avoidance method embodiments, such as Figure 2 Steps shown.

[0115] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 11, and executed by the processor 12 to complete the present application. One or more modules / units may be a series of computer-readable instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device 10. For example, the computer program may be divided into a judgment module 110, a determination module 120, and a control module 130.

[0116] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, and includes a number of instructions for enabling a computer device (which can be a personal computer, a computer device or a network device, etc.) or a processor to execute a part of a vehicle avoidance method in each embodiment of the present application.

[0117] If the module / unit integrated in the electronic device 10 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also instruct the relevant hardware devices to complete through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented.

[0118] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. Computer readable media may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory and other memory, etc.

[0119] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0120] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. Figure 1 Only one arrow is used in the figure, but it does not mean that there is only one bus or one type of bus. The bus is configured to realize the connection and communication between the memory 11 and at least one processor 12, etc.

[0121] The embodiment of the present application also provides a computer-readable storage medium (not shown), in which computer-readable storage medium is stored computer-readable instructions, and the computer-readable instructions are executed by the processor 12 in the electronic device 10 to implement a vehicle avoidance method in any of the above embodiments.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0123] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0125] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the specification can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any specific order.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical solution of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present application.

Claims

1. A vehicle avoidance method, characterized in that: The method comprises: When the self-vehicle is in an adaptive cruise control state following the preceding vehicle, if a target vehicle is detected, determining whether the self-vehicle and the target vehicle meet a preset parallel driving condition; If the ego vehicle and the target vehicle meet the parallel driving condition, determining the target following distance of the ego vehicle according to the current following distance of the ego vehicle, the traffic environment information around the ego vehicle, and the characteristic parameters of the ego vehicle; The driving of the own vehicle is controlled based on the target following distance so that the own vehicle avoids the target vehicle.

2. The vehicle avoidance method according to claim 1, characterized in that: The traffic environment information includes image perception information, and determining the target following distance of the vehicle according to the current following distance of the vehicle, the traffic environment information around the vehicle, and the characteristic parameters of the vehicle includes: Determining a longitudinal distance between the preceding vehicle and the target vehicle, and a length of the target vehicle according to the image perception information; Determining a preset avoidance strategy applicable to the self-vehicle according to the current following distance, the length of the target vehicle, and the longitudinal distance; The target following distance is determined based on the preset avoidance strategy, the longitudinal distance, the length of the target vehicle, and the characteristic parameter.

3. The vehicle avoidance method according to claim 2, characterized in that: The determining of a preset avoidance strategy applicable to the vehicle according to the current following distance, the length of the target vehicle, and the longitudinal distance includes: Determining a difference between the current following distance and the longitudinal distance, and determining a ratio of the difference to the length of the target vehicle; If the ratio is less than or equal to a first preset ratio, determining that the preset avoidance strategy is a forward avoidance strategy, wherein the first preset ratio is a ratio of the front portion of the target vehicle to the length of the target vehicle; If the ratio is greater than or equal to a second preset ratio, it is determined that the preset avoidance strategy is a rearward avoidance strategy, wherein the second preset ratio is a ratio of the body portion of the target vehicle to the length of the target vehicle.

4. The vehicle avoidance method according to claim 3, characterized in that: The characteristic parameter includes the length of the vehicle, and determining the target following distance based on the preset avoidance strategy, the longitudinal distance, the length of the target vehicle, and the characteristic parameter includes: If the preset avoidance strategy is the forward avoidance strategy, determining the target following distance according to the longitudinal distance and the length of the vehicle; If the preset avoidance strategy is the rearward avoidance strategy, the target following distance is determined according to the longitudinal distance and the length of the target vehicle.

5. The vehicle avoidance method according to claim 2, characterized in that: The traffic environment information also includes radar perception information, and the method further includes: Determining the driving speed of the preceding vehicle and the driving speed of the target vehicle according to the radar sensing information or the image sensing information; Among them, the parallel driving conditions include: the current following distance of the own vehicle is greater than the longitudinal distance and the current following distance of the own vehicle is less than the sum of the longitudinal distance and the length of the target vehicle, the driving speed of the leading vehicle is greater than a preset speed threshold and the difference between the driving speed of the leading vehicle and the driving speed of the target vehicle is less than or equal to a preset difference.

6. The vehicle avoidance method according to claim 1, characterized in that: The method further comprises: The driving speed of the vehicle is adjusted according to the current following distance and the target following distance, and the current following distance of the vehicle is adjusted to the target following distance.

7. The vehicle avoidance method according to claim 1, characterized in that: The target vehicle is a large vehicle traveling in a lane adjacent to the lane where the own vehicle is located.

8. A vehicle avoidance device, characterized in that: The device comprises: A judgment module, used for judging whether the self-vehicle and the target vehicle meet a preset parallel driving condition when the self-vehicle is in an adaptive cruise control state following the preceding vehicle and a target vehicle is detected; a determination module, configured to determine a target following distance of the ego vehicle according to the current following distance of the ego vehicle, the traffic environment information around the ego vehicle, and the characteristic parameters of the ego vehicle if the ego vehicle and the target vehicle meet the parallel driving condition; The control module is used to control the driving of the ego vehicle based on the target following distance so that the ego vehicle avoids the target vehicle.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the vehicle avoidance method as described in any one of claims 1 to 7.

10. A vehicle, characterized in that: The vehicle comprises the electronic device as claimed in claim 9.

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