Vehicle in-situ turning-around judgment method and device and vehicle

Abstract

The embodiment of the invention provides a vehicle in-situ turning-around judgment method and device and a vehicle, and relates to the technical field of vehicle endurance. The method comprises the steps that in response to in-situ U-turn operation of a user, current U-turn data of a vehicle and multiple pieces of historical U-turn data are obtained; respectively calculating a plurality of first weighted vectors corresponding to the plurality of historical U-turn data and a second weighted vector corresponding to the current U-turn data, and carrying out cosine similarity calculation on the plurality of first weighted vectors and the second weighted vector to obtain a corresponding first cosine similarity; determining target historical U-turn data according to the maximum first cosine similarity in the multiple pieces of historical U-turn data; and if the historical turning-around result corresponding to the target historical turning-around data is that in-situ turning-around is successful, the vehicle is controlled to turn around in situ. By the adoption of the technical scheme, when a user turns around in situ, whether the vehicle can turn around in situ or not can be judged in time, and the accidental tire burst condition is avoided.

Description

Technical Field

[0001] The disclosed embodiments relate to the field of vehicle technology, and more particularly to a method and device for determining a vehicle U-turn on the spot, and a vehicle. Background Art

[0002] Currently, most of the on-the-spot turning functions on the market, except for a small number of those achieved by wheel-side motors and large-angle steering, are achieved by long-distance turning through the forward and reverse rotation of the front and rear wheels. This means that the tires will be subjected to excessive wear and tear beyond that of daily car use.

[0003] During use, users have no way of knowing the current degree of tire wear, and the tire may suddenly burst due to unsatisfactory conditions while turning around on the spot. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the embodiments of the present disclosure provide a method, a device and a vehicle for determining whether a vehicle should turn around on the spot.

[0005] A first aspect of an embodiment of the present disclosure provides a method for determining a vehicle U-turn on the spot, the method comprising:

[0006] In response to a user's on-the-spot U-turn operation, obtaining current U-turn data of the vehicle and obtaining a plurality of historical U-turn data;

[0007] Respectively calculating a plurality of first weighted vectors corresponding to a plurality of the historical U-turn data and a second weighted vector corresponding to the current U-turn data, and performing cosine similarity calculation on the plurality of the first weighted vectors and the second weighted vectors to obtain corresponding first cosine similarities;

[0008] Determine target historical U-turn data according to the largest first cosine similarity among the plurality of historical U-turn data;

[0009] If the historical U-turn result corresponding to the target historical U-turn data is a successful U-turn on the spot, the vehicle is controlled to perform a U-turn on the spot.

[0010] In one example, in response to the user's on-the-spot U-turn operation, obtaining the current U-turn data of the vehicle and obtaining a plurality of historical U-turn data include:

[0011] In response to the user's on-the-spot U-turn operation, acquiring the current U-turn data of the vehicle through a sensor device;

[0012] Sending a data acquisition request to the cloud, wherein the data acquisition request is used to request the cloud to send a plurality of historical U-turn data of the same type of vehicles and the same operating conditions;

[0013] Receive the plurality of historical U-turn data sent by the cloud in response to the data acquisition request.

[0014] In one example, the current U-turn data includes current ground environment data and current vehicle parameter data, and the historical U-turn data includes historical ground environment data and historical vehicle parameter data.

[0015] In one example, the method further includes:

[0016] If the historical U-turn result corresponding to the target historical U-turn data is a failure to turn around on the spot, corresponding failure reminder information is fed back to the user, and the failure reminder information includes the reason for the failure to turn around on the spot.

[0017] In one example, after controlling the vehicle to make a U-turn on the spot, the method further includes:

[0018] Acquire current friction data and historical friction data, wherein the current friction data includes current tire friction noise data and current road friction coefficient, and the historical friction data includes historical tire friction noise data and historical road friction coefficient;

[0019] respectively calculating a third weighted vector corresponding to the current friction data and a fourth weighted vector corresponding to the historical friction data, and performing cosine similarity calculation on the third weighted vector and the fourth weighted vector to obtain a corresponding second cosine similarity;

[0020] The corresponding tire wear degree is determined according to the largest second cosine similarity.

[0021] In one example, obtaining current friction data includes:

[0022] The tire friction noise data corresponding to each tire is directionally acquired through an external noise detection device, and the current road friction coefficient is acquired through an external image acquisition device.

[0023] In one example, after determining the corresponding tire wear degree according to the largest second cosine similarity, the method further includes:

[0024] If the tire wear degree reaches a preset critical value, the vehicle is controlled to stop and turn around on the spot, and corresponding stop reminder information is fed back to the user.

[0025] In one example, the method further includes:

[0026] After the vehicle completes the turn on the spot, the corresponding completion reminder information is fed back to the user;

[0027] The current U-turn data, the result of the U-turn on the spot, the current friction data and the degree of tire wear are stored and uploaded to the cloud.

[0028] A second aspect of an embodiment of the present disclosure provides a vehicle turn-around determination device, the device comprising:

[0029] A first acquisition module, configured to acquire current U-turn data of the vehicle and a plurality of historical U-turn data in response to a user's U-turn operation on the spot;

[0030] A first calculation module, used to respectively calculate a plurality of first weighted vectors corresponding to the plurality of historical U-turn data and a second weighted vector corresponding to the current U-turn data, and perform cosine similarity calculation on the plurality of first weighted vectors and the second weighted vector to obtain corresponding first cosine similarities;

[0031] A first determination module, configured to determine target historical U-turn data according to the largest first cosine similarity among the plurality of historical U-turn data;

[0032] The vehicle control module is used to control the vehicle to make a U-turn on the spot if the historical U-turn result corresponding to the target historical U-turn data is a successful U-turn on the spot.

[0033] A third aspect of an embodiment of the present disclosure provides a vehicle, the server comprising: a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method of the first aspect above.

[0034] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of the first aspect described above can be implemented.

[0035] The disclosed embodiment provides a method, device and vehicle for determining a vehicle U-turn on the spot, the method comprising: in response to a user's U-turn operation on the spot, obtaining the vehicle's current U-turn data and a plurality of historical U-turn data, then respectively calculating a plurality of first weighted vectors corresponding to the plurality of historical U-turn data and a second weighted vector corresponding to the current U-turn data, and performing cosine similarity calculation on the plurality of the first weighted vectors and the second weighted vectors to obtain the corresponding first cosine similarity, then determining the target historical U-turn data according to the largest first cosine similarity among the plurality of the historical U-turn data, and finally controlling the vehicle to perform a U-turn on the spot if the historical U-turn result corresponding to the target historical U-turn data is a successful U-turn on the spot. By adopting the technical solution, when the user performs a U-turn on the spot, it is possible to timely determine whether the vehicle can perform a U-turn on the spot, thereby avoiding accidental tire blowouts. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0038] Figure 1 It is a flow chart of a method for determining a vehicle U-turn on the spot provided by an embodiment of the present disclosure;

[0039] Figure 2 is a flow chart of another method for determining a vehicle U-turn on the spot provided by an embodiment of the present disclosure;

[0040] Figure 3 It is a structural schematic diagram of a vehicle turn-around determination device provided by an embodiment of the present disclosure;

[0041] Figure 4 It is a structural schematic diagram of a vehicle in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0042] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0044] Figure 1 The present invention provides a flow chart of a method for determining a vehicle turning around in place, which can be executed by an electronic device. The electronic device may include but is not limited to a vehicle-mounted device, a vehicle controller, and the like.

[0045] like Figure 1 As shown, the method provided in this embodiment includes the following steps:

[0046] S110 . In response to the user's on-the-spot U-turn operation, obtain current U-turn data of the vehicle and obtain historical U-turn data.

[0047] In the embodiment of the present disclosure, the electronic device may obtain the current U-turn data and the historical U-turn data of the vehicle in response to the user's on-the-spot U-turn operation.

[0048] Optionally, the on-the-spot U-turn operation may be an operation in which the user controls the vehicle to perform an on-the-spot U-turn. For example, the on-the-spot U-turn operation may be an operation in which the user initiates an on-the-spot U-turn request to the vehicle through a soft or hard switch, including various mobile terminals and hardware switches on the vehicle.

[0049] Optionally, the current U-turn data may be data related to the scenario in which the current vehicle turns around in place. The current U-turn data includes current ground environment data and current vehicle parameter data. For example, the current ground environment data may include ground wear coefficient data (such as based on ground material), weather environment humidity, temperature, etc., and the current vehicle parameter data may include vehicle model data, tire model data, steering speed, wheel rotation speed, vehicle weight and load, etc., which are not limited here.

[0050] Optionally, the historical U-turn data can be historical parameter data for various vehicles saved in the cloud for turning around on the spot. Among them, the historical U-turn data includes historical ground environment data and historical vehicle parameter data. For example, for the same type of vehicles and the same operating conditions, the historical ground environment data of other vehicles may include ground wear coefficient data (such as based on ground material), weather environment humidity, temperature, etc., and the historical vehicle parameter data may include vehicle model data, tire model data, steering speed, wheel rotation speed, vehicle weight and load, tire wear, etc., as well as friction noise, results of turning around on the spot, etc., which are not limited here.

[0051] Specifically, when the user wants the vehicle to turn around on the spot, the operation can be performed, such as issuing a turn-around request to the vehicle through various soft and hard switches, and then the electronic device can respond to the turn-around request, obtain the vehicle's current turn data and obtain historical turn data.

[0052] S120. Calculate a plurality of first weighted vectors corresponding to a plurality of the historical U-turn data and a second weighted vector corresponding to the current U-turn data respectively, and perform cosine similarity calculation on the plurality of the first weighted vectors and the second weighted vectors to obtain corresponding first cosine similarities.

[0053] In an embodiment of the present disclosure, the electronic device can respectively calculate multiple first weighted vectors corresponding to the multiple historical U-turn data and the second weighted vector corresponding to the current U-turn data, and perform cosine similarity calculation on the multiple first weighted vectors and the second weighted vectors to obtain corresponding first cosine similarities.

[0054] Specifically, the electronic device first calculates a plurality of first weighted vectors corresponding to the plurality of historical U-turn data and a second weighted vector corresponding to the current U-turn data. For example, weights may be assigned to the ground environment data and the vehicle parameter data in advance, such as a weight of 60% for the historical ground environment data and a weight of 40% for the historical vehicle parameter data, etc., which are not limited at this time. The plurality of first weighted vectors corresponding to the plurality of historical U-turn data and the second weighted vector corresponding to the current U-turn data are calculated based on the weights corresponding to the different data, and then the cosine similarity of the plurality of the first weighted vectors and the second weighted vectors is calculated to obtain the corresponding first cosine similarity. The first cosine similarity ranges from [-1,1], and the closer the value is to 1, the more similar the vectors are.

[0055] The formula for calculating cosine similarity is: similarity = (sum(w_i*x_i*y_i)) / (sqrt(sum(w_i*x_i^2))*sqrt(sum(w_i*y_i^2))). similarity is the first cosine similarity, w is used to represent the weights corresponding to different data, and x and y represent the two vectors to be compared, namely the first weighted vector and the second weighted vector.

[0056] S130 . Determine target historical U-turn data according to the largest first cosine similarity among the plurality of historical U-turn data.

[0057] In the embodiment of the present disclosure, the electronic device may determine the target historical U-turn data according to the largest first cosine similarity among the plurality of historical U-turn data.

[0058] Specifically, the electronic device may calculate first cosine similarities between the current U-turn data and a plurality of historical U-turn data respectively, and determine the target historical U-turn data corresponding to the largest first cosine similarity among the plurality of first cosine similarities.

[0059] S140: If the historical U-turn result corresponding to the target historical U-turn data is a successful U-turn on the spot, control the vehicle to perform a U-turn on the spot.

[0060] In an embodiment of the present disclosure, if the historical U-turn result corresponding to the target historical U-turn data is a successful U-turn on the spot, the electronic device can control the vehicle to make a U-turn on the spot.

[0061] Specifically, after determining the target historical U-turn data with the highest matching degree with the current U-turn data, the electronic device determines the historical U-turn result corresponding to the target historical U-turn data. If the historical U-turn result is a successful U-turn on the spot, the electronic device can determine that the U-turn on the spot can be performed and control the vehicle to perform the U-turn on the spot.

[0062] Therefore, in the disclosed embodiment, in response to the user's U-turn operation on the spot, the current U-turn data of the vehicle and multiple historical U-turn data are obtained, and then multiple first weighted vectors corresponding to the multiple historical U-turn data and the second weighted vector corresponding to the current U-turn data are calculated respectively, and the cosine similarity calculation is performed on the multiple first weighted vectors and the second weighted vectors to obtain the corresponding first cosine similarity, and then the target historical U-turn data is determined according to the largest first cosine similarity among the multiple historical U-turn data, and finally, if the historical U-turn result corresponding to the target historical U-turn data is a successful U-turn on the spot, the vehicle is controlled to make a U-turn on the spot. The use of this technical solution can timely determine whether the vehicle can make a U-turn on the spot when the user makes a U-turn on the spot, so as to avoid unexpected tire blowouts.

[0063] Optionally, S110 may specifically include: in response to the user's on-the-spot U-turn operation, obtaining the vehicle's current U-turn data through a sensor device; sending a data acquisition request to the cloud, wherein the data acquisition request is used to request the cloud to send multiple historical U-turn data for the same type of vehicles and under the same operating conditions; and receiving the multiple historical U-turn data sent by the cloud in response to the data acquisition request.

[0064] In an embodiment of the present disclosure, the electronic device may obtain the current turning data of the vehicle through a sensor device in response to the user's on-the-spot turning operation, such as obtaining the current turning data of the vehicle through camera devices inside and outside the vehicle, infrared devices, etc.

[0065] Furthermore, the electronic device may send a data acquisition request to the cloud.

[0066] Optionally, the data acquisition request may be used to request the cloud to send multiple historical U-turn data of the same type of vehicles and under the same operating conditions.

[0067] Specifically, when the user wants to make a U-turn on the spot, the electronic device can send a data acquisition request to the cloud. After receiving the data acquisition request, the cloud can obtain the historical U-turn data of other vehicles of the same type and under the same operating conditions, and send down the multiple historical U-turn data.

[0068] Furthermore, the electronic device may receive a plurality of the historical U-turn data sent by the cloud in response to the data acquisition request.

[0069] Optionally, the vehicle turn-on-the-spot determination method may further include: if the historical turn result corresponding to the target historical turn data is a turn-on-the-spot failure, feeding back corresponding failure reminder information to the user, wherein the failure reminder information includes the cause of the turn-on-the-spot failure.

[0070] In an embodiment of the present disclosure, if the historical U-turn result corresponding to the target historical U-turn data is a failure to turn around on the spot, the electronic device can feedback corresponding failure reminder information to the user.

[0071] Optionally, the failure reminder information may include the reason for the failure of the turn-around. For example, the failure reminder information may be information that the turn-around is not supported due to high tire wear to the user through voice, image, etc.

[0072] Specifically, after the electronic device makes a judgment, if the judgment result is that the vehicle does not meet the conditions for turning around on the spot, that is, the vehicle cannot turn around on the spot, the electronic device can feedback corresponding failure reminder information to the user, such as feedback to the user through voice, image, etc. that the vehicle cannot support turning around on the spot due to the high degree of tire wear.

[0073] During the process of a vehicle turning around on the spot, an emergency may occur that affects the vehicle's turning around on the spot, causing the vehicle to fail to turn around on the spot. In order to ensure that the vehicle's turning around on the spot is normal or to stop the vehicle's turning around in time after an emergency occurs, auxiliary judgment can be made through the tire friction noise data of a mobile phone. The specific implementation method is as follows.

[0074] Optionally, after controlling the vehicle to turn around on the spot, the method also includes: obtaining current friction data and historical friction data, the current friction data including current tire friction noise data and current road friction coefficient, and the historical friction data including historical tire friction noise data and historical road friction coefficient; respectively calculating a third weighted vector corresponding to the current friction data and a fourth weighted vector corresponding to the historical friction data, and performing cosine similarity calculation on the third weighted vector and the fourth weighted vector to obtain a corresponding second cosine similarity; and determining the corresponding degree of tire wear according to the maximum second cosine similarity.

[0075] In the embodiment of the present disclosure, the electronic device can obtain current friction data and historical friction data.

[0076] The current friction data includes current tire friction noise data and current road friction coefficient, and the historical friction data includes historical tire friction noise data and historical road friction coefficient.

[0077] Furthermore, the electronic device may respectively calculate a third weighted vector corresponding to the current friction data and a fourth weighted vector corresponding to the historical friction data, and perform cosine similarity calculation on the third weighted vector and the fourth weighted vector to obtain a corresponding second cosine similarity.

[0078] For example, the electronic device can pre-assign weights to the tire friction noise data and the road friction coefficient, such as a tire friction noise data weight of 70% and a road friction coefficient weight of 30%, etc., which are not limited at this time. According to the weights corresponding to different data, the third weighted vector corresponding to the current friction data and the fourth weighted vector corresponding to the historical friction data are calculated, and then the cosine similarity of the third weighted vector and the fourth weighted vector is calculated to obtain the corresponding second cosine similarity. The second cosine similarity ranges from [-1,1]. The closer the value is to 1, the more similar the vectors are.

[0079] The formula for calculating cosine similarity is: similarity = (sum(w_i*x_i*y_i)) / (sqrt(sum(w_i*x_i^2))*sqrt(sum(w_i*y_i^2))). similarity is the second cosine similarity, w is used to represent the weights corresponding to different data, and x and y represent the two vectors to be compared, namely the third weighted vector and the fourth weighted vector.

[0080] Furthermore, the electronic device may determine the corresponding tire wear degree according to the largest second cosine similarity.

[0081] Specifically, the electronic device can calculate multiple second cosine similarities between the current friction data and the historical friction data, and determine the tire wear degree corresponding to the largest second cosine similarity. For example, in the historical friction data corresponding to the largest second cosine similarity, the wear consumed for completing a turn on the spot is 1, and when the turn on the spot is not performed, the tire wear degree is 10, and the wear 1 is also consumed when performing a turn on the spot, that is, the tire wear degree after performing a turn on the spot is 9.

[0082] Optionally, S130 may specifically include: directionally acquiring the tire friction noise data corresponding to each tire through an external vehicle noise detection device, and acquiring a current road friction coefficient through an external vehicle image acquisition device.

[0083] Specifically, when the vehicle is turning on the spot, the electronic device can use the external noise detection equipment to directionally obtain the tire friction noise data corresponding to each tire, that is, obtain the noise data generated by each tire rubbing against the ground, and obtain the material of the current road (such as cement, asphalt pavement, etc.) and the ambient weather (rainy days / wet roads, snowy days / icy roads, etc.) through equipment such as external cameras to determine the current road friction coefficient.

[0084] Optionally, after S140, the method for determining whether the vehicle is turning around on the spot may further include: if the tire wear degree reaches a preset critical value, controlling the vehicle to stop turning around on the spot, and feeding back corresponding stop reminder information to the user.

[0085] In the disclosed embodiment, if the tire wear degree reaches a preset critical value, the electronic device can control the vehicle to stop and turn around on the spot, and feedback corresponding stop reminder information to the user.

[0086] Optionally, the preset critical value may be a preset wear degree critical value. The preset critical value may be determined according to the wear degree required for the tire to perform a turn on the spot.

[0087] Optionally, the stop reminder information can be used to remind the user to stop and turn around on the spot.

[0088] Specifically, when the vehicle is turning on the spot, the electronic device can calculate the degree of tire wear in real time based on the tire friction noise data, and judge the degree of tire wear. If the degree of tire wear reaches a preset critical value, it means that if the vehicle continues to turn on the spot, the tire will suddenly burst. Therefore, the electronic device can control the vehicle to stop turning on the spot and feedback corresponding stop reminder information to the user, such as feedback to the user through voice, image, etc. that if the vehicle continues to turn on the spot, the tire will burst.

[0089] Optionally, after S140, the vehicle turn-on-the-spot determination method may further include: feeding back corresponding completion reminder information to the user; storing and uploading the current turn data, turn-on-the-spot result, tire friction noise data and tire wear degree to the cloud.

[0090] In the embodiment of the present disclosure, after the vehicle completes the on-the-spot U-turn, the electronic device may feedback corresponding completion reminder information to the user. For example, the completion reminder information may be a reminder to the user that the on-the-spot U-turn has been completed.

[0091] Furthermore, the electronic device can store and upload the current U-turn data, the result of the U-turn on the spot, the tire friction noise data and the tire wear degree to the cloud.

[0092] Figure 2 It is a flow chart of another method for determining a vehicle U-turn on the spot provided by an embodiment of the present disclosure.

[0093] like Figure 2 As shown, when the user wants to make a U-turn on the spot, he starts to use the U-turn function on the vehicle (through soft and hard switches, including hardware switches on various mobile terminals and vehicles), and then the electronic device can respond to the U-turn request on the spot and send a data acquisition request to the cloud. After the cloud receives the data acquisition request, it can obtain the historical U-turn data of other vehicles of the same type and under the same operating conditions, and send the historical U-turn data. The electronic device can receive the historical U-turn data sent by the cloud through the body processor.

[0094] Optionally, the current U-turn data includes current ground environment data and current vehicle parameter data. For example, the current ground environment data may include ground wear coefficient data (such as based on ground material), weather environment humidity, temperature, etc., and the current vehicle parameter data may include vehicle model data, tire model data, vehicle weight and load, etc., which are not limited here.

[0095] Optionally, the historical U-turn data includes historical ground environment data and historical vehicle parameter data. For example, for vehicles of the same type and under the same operating conditions, the historical ground environment data of other vehicles may include ground wear coefficient data (such as based on ground material), weather environment humidity, temperature, etc., and the historical vehicle parameter data may include vehicle model data, tire model data, vehicle weight and load, tire wear, etc., as well as friction noise, results of U-turns on the spot, etc., which are not limited here.

[0096] Furthermore, the electronic device can respectively calculate multiple first weighted vectors corresponding to multiple historical U-turn data and second weighted vectors corresponding to the current U-turn data in the acquired historical U-turn data, and perform cosine similarity calculation on multiple first weighted vectors and second weighted vectors to obtain corresponding first cosine similarities, and determine the target historical U-turn data according to the largest first cosine similarity in the multiple historical U-turn data. If the historical U-turn result corresponding to the target historical U-turn data is a successful U-turn on the spot, the electronic device can determine that the U-turn on the spot can be performed, and control the vehicle to perform the U-turn on the spot; if the historical U-turn result corresponding to the target historical U-turn data is a failed U-turn on the spot, the corresponding failure reminder information is fed back to the user, such as feedback to the user through voice, image, etc. that the U-turn on the spot is not supported due to the high degree of tire wear.

[0097] Furthermore, after controlling the vehicle to make a U-turn on the spot, the electronic device can obtain current friction data and historical friction data, wherein the current friction data includes current tire friction noise data and current road friction coefficient, and the historical friction data includes historical tire friction noise data and historical road friction coefficient; respectively calculate the third weighted vector corresponding to the current friction data and the fourth weighted vector corresponding to the historical friction data, and perform cosine similarity calculation on the third weighted vector and the fourth weighted vector to obtain the corresponding second cosine similarity; determine the corresponding tire wear degree according to the largest second cosine similarity. For example, in the historical friction data corresponding to the largest second cosine similarity, the wear consumed to complete a U-turn on the spot is 1, and when the U-turn on the spot is not performed, the tire wear degree is 10, and the wear 1 is also consumed when the U-turn on the spot is performed, that is, the tire wear degree is 9 after a U-turn on the spot.

[0098] When the vehicle is turning on the spot, the electronic device can calculate the degree of tire wear in real time based on the tire friction noise data, and judge the degree of tire wear. If the degree of tire wear reaches a preset critical value, it means that if the vehicle continues to turn on the spot, the tire will suddenly burst. Therefore, the electronic device can control the vehicle to stop turning on the spot and feedback corresponding stop reminder information to the user, such as feedback to the user through voice, image, etc. that if the vehicle continues to turn on the spot, the tire will burst.

[0099] After completing the on-the-spot U-turn, the electronic device can feedback the corresponding completion reminder information to the user, and store and upload the current U-turn data, the judgment result, the tire friction noise data and the tire wear degree to the cloud, such as the data request speed, comparison speed, comparison result, execution result, tire grinding noise, tire wear degree, etc. related to the current U-turn for use by other vehicles.

[0100] Figure 3 1 is a schematic diagram of the structure of a vehicle turn-around determination device provided by an embodiment of the present disclosure. The vehicle turn-around determination device can be understood as the above-mentioned vehicle or a part of the functional modules in the above-mentioned vehicle. Figure 3 As shown, the vehicle turn-around determination device 300 comprises:

[0101] A first acquisition module 310 is used to acquire current U-turn data of the vehicle and a plurality of historical U-turn data in response to a user's U-turn operation on the spot;

[0102] A first calculation module 320, used to respectively calculate a plurality of first weighted vectors corresponding to the plurality of historical U-turn data and a second weighted vector corresponding to the current U-turn data, and perform cosine similarity calculation on the plurality of first weighted vectors and the second weighted vector to obtain corresponding first cosine similarities;

[0103] A first determination module 330 is used to determine target historical U-turn data according to the largest first cosine similarity among the plurality of historical U-turn data;

[0104] The vehicle control module 340 is configured to control the vehicle to perform an on-the-spot U-turn if the historical U-turn result corresponding to the target historical U-turn data is a successful on-the-spot U-turn.

[0105] In an example, the first acquisition module 310 may specifically include:

[0106] The data acquisition unit is used to acquire the current turning data of the vehicle through a sensor device in response to the user's turning operation on the spot.

[0107] A request sending unit is used to send a data acquisition request to the cloud, wherein the data acquisition request is used to request the cloud to send multiple historical U-turn data of the same type of vehicles and under the same operating conditions.

[0108] A data receiving unit is used to receive the multiple historical U-turn data sent by the cloud in response to the data acquisition request.

[0109] In one example, the current U-turn data includes current ground environment data and current vehicle parameter data, and the historical U-turn data includes historical ground environment data and historical vehicle parameter data.

[0110] In one example, the vehicle turn-around determination device 300 may further include:

[0111] The first feedback module is used to feedback corresponding failure reminder information to the user if the historical U-turn result corresponding to the target historical U-turn data is a failure of the U-turn on the spot, and the failure reminder information includes a reason for the failure of the U-turn on the spot.

[0112] In one example, the vehicle turn-around determination device 300 may further include:

[0113] The second acquisition module is used to acquire current friction data and historical friction data, wherein the current friction data includes current tire friction noise data and current road friction coefficient, and the historical friction data includes historical tire friction noise data and historical road friction coefficient.

[0114] The second calculation module is used to respectively calculate a third weighted vector corresponding to the current friction data and a fourth weighted vector corresponding to the historical friction data, and perform cosine similarity calculation on the third weighted vector and the fourth weighted vector to obtain a corresponding second cosine similarity.

[0115] The second determination module is used to determine the corresponding tire wear degree according to the maximum second cosine similarity.

[0116] In an example, the second acquisition module may specifically include:

[0117] The data acquisition unit is used to directionally acquire the tire friction noise data corresponding to each tire through an external noise detection device, and to acquire the current road friction coefficient through an external image acquisition device.

[0118] In one example, the vehicle turn-around determination device 300 may further include:

[0119] The second feedback module is used to control the vehicle to stop and turn around on the spot if the tire wear degree reaches a preset critical value, and to feed back corresponding stop reminder information to the user.

[0120] In one example, the vehicle turn-around determination device 300 may further include:

[0121] The third feedback module is used to feed back corresponding completion reminder information to the user.

[0122] The data uploading module is used to store and upload the current U-turn data, the result of the U-turn on the spot, the tire friction noise data and the tire wear degree to the cloud.

[0123] The device provided in this embodiment can execute the method of any of the above embodiments, and its execution method and beneficial effects are similar, which will not be repeated here.

[0124] The embodiments of the present disclosure further provide a vehicle, which includes: a memory, in which a computer program is stored; and a processor, which is used to execute the computer program. When the computer program is executed by the processor, the method of any of the above embodiments can be implemented.

[0125] For example, Figure 4 Schematic diagram of a vehicle structure in the embodiment of the present disclosure. Figure 4 , which shows a schematic diagram of a structure of a vehicle 1000 suitable for implementing the embodiment of the present disclosure. The vehicle 1000 in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The vehicle shown is merely an example and should not bring any limitation to the functionality and scope of use of the embodiments of the present disclosure.

[0126] like Figure 4 As shown, the vehicle 1000 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1008 to a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the vehicle 1000 are also stored. The processing device 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0127] Typically, the following devices may be connected to the I / O interface 1005: input devices 1006 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 1007 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1008 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 may allow the vehicle 1000 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The vehicle 1000 is shown with various devices, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have instead.

[0128] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 1009, or installed from a storage device 1008, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0129] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0130] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperTextTransferProtocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an adhoc peer-to-peer network), as well as any currently known or future developed network.

[0131] The computer-readable medium may be included in the vehicle, or may exist independently without being installed in the vehicle.

[0132] The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the vehicle, the vehicle: calculates the average power consumption corresponding to the vehicle's most recent target distance; receives the regional benchmark power consumption and the upper and lower floating range sent by the cloud within a pre-defined area; calculates the estimated dynamic energy consumption based on the average power consumption and the regional benchmark power consumption, and calculates the estimated mileage based on the remaining available power and the estimated dynamic energy consumption, and the estimated dynamic energy consumption is in line with the upper and lower floating range; updates the displayed endurance value based on the estimated mileage.

[0133] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0134] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0135] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0136] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0137] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0138] The embodiments of the present disclosure also provide a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of any of the above embodiments can be implemented. The execution method and beneficial effects are similar and will not be repeated here.

[0139] It should be noted that, in this article, 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 such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0140] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining a vehicle turning around in place, characterized in that: include: In response to a user's on-the-spot U-turn operation, obtaining current U-turn data of the vehicle and obtaining a plurality of historical U-turn data; Respectively calculating a plurality of first weighted vectors corresponding to a plurality of the historical U-turn data and a second weighted vector corresponding to the current U-turn data, and performing cosine similarity calculation on the plurality of the first weighted vectors and the second weighted vectors to obtain corresponding first cosine similarities; Determine target historical U-turn data according to the largest first cosine similarity among the plurality of historical U-turn data; If the historical U-turn result corresponding to the target historical U-turn data is a successful U-turn on the spot, the vehicle is controlled to perform a U-turn on the spot.

2. The method according to claim 1, characterized in that: The step of obtaining the current U-turn data of the vehicle and a plurality of historical U-turn data in response to the user's U-turn operation on the spot includes: In response to the user's on-the-spot U-turn operation, acquiring the current U-turn data of the vehicle through a sensor device; Sending a data acquisition request to the cloud, wherein the data acquisition request is used to request the cloud to send a plurality of historical U-turn data of the same type of vehicles and the same operating conditions; Receive the plurality of historical U-turn data sent by the cloud in response to the data acquisition request.

3. The method according to claim 1, characterized in that: The current U-turn data includes current ground environment data and current vehicle parameter data, and the historical U-turn data includes historical ground environment data and historical vehicle parameter data.

4. The method according to claim 1, characterized in that: The method further comprises: If the historical U-turn result corresponding to the target historical U-turn data is a failure to turn around on the spot, corresponding failure reminder information is fed back to the user, and the failure reminder information includes the reason for the failure to turn around on the spot.

5. The method according to claim 1, characterized in that After controlling the vehicle to make a U-turn on the spot, the method further includes: Acquire current friction data and historical friction data, wherein the current friction data includes current tire friction noise data and current road friction coefficient, and the historical friction data includes historical tire friction noise data and historical road friction coefficient; respectively calculating a third weighted vector corresponding to the current friction data and a fourth weighted vector corresponding to the historical friction data, and performing cosine similarity calculation on the third weighted vector and the fourth weighted vector to obtain a corresponding second cosine similarity; The corresponding tire wear degree is determined according to the largest second cosine similarity.

6. The method according to claim 5, characterized in that The obtaining of current friction data includes: The tire friction noise data corresponding to each tire is directionally acquired through an external noise detection device, and the current road friction coefficient is acquired through an external image acquisition device.

7. The method according to claim 6, characterized in that After determining the corresponding tire wear degree according to the largest second cosine similarity, the method further includes: If the tire wear degree reaches a preset critical value, the vehicle is controlled to stop and turn around on the spot, and corresponding stop reminder information is fed back to the user.

8. The method according to claim 1, characterized in that The method further comprises: After the vehicle completes the turn on the spot, the corresponding completion reminder information is fed back to the user; The current U-turn data, the result of the U-turn on the spot, the current friction data and the degree of tire wear are stored and uploaded to the cloud.

9. A vehicle turn-around determination device, characterized in that: The device comprises: A first acquisition module, configured to acquire current U-turn data of the vehicle and a plurality of historical U-turn data in response to a user's U-turn operation on the spot; A first calculation module, used to respectively calculate a plurality of first weighted vectors corresponding to the plurality of historical U-turn data and a second weighted vector corresponding to the current U-turn data, and perform cosine similarity calculation on the plurality of first weighted vectors and the second weighted vector to obtain corresponding first cosine similarities; A first determination module is used to determine target historical U-turn data according to the largest first cosine similarity among the plurality of historical U-turn data; The vehicle control module is used to control the vehicle to make a U-turn on the spot if the historical U-turn result corresponding to the target historical U-turn data is a successful U-turn on the spot.

10. A vehicle, characterized in that: include: A processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method according to any one of claims 1 to 8.

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