CN113447037A - Stroke yaw detection method and device - Google Patents
Stroke yaw detection method and device Download PDFInfo
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- CN113447037A CN113447037A CN202110625382.7A CN202110625382A CN113447037A CN 113447037 A CN113447037 A CN 113447037A CN 202110625382 A CN202110625382 A CN 202110625382A CN 113447037 A CN113447037 A CN 113447037A
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Abstract
The present specification discloses a stroke yaw detection method and apparatus, wherein the method includes: receiving a yaw detection request, wherein the yaw detection request comprises a plurality of track points of a target journey in a target time period and at least one planned route of the target journey; determining an enclosing frame corresponding to each planned route in at least one planned route, wherein the enclosing frame corresponding to each planned route comprises each planned route; generating a target characteristic vector of a target travel in a target time period based on a plurality of track points of the target time period, at least one planned route and surrounding frames corresponding to the planned routes; and judging whether the target travel deviates in the target time period or not according to the target characteristic vector. By the scheme, the accuracy of judging whether the stroke drifts can be improved, so that the driving safety is guaranteed.
Description
Technical Field
The present disclosure relates to the field of data mining and machine learning technologies, and in particular, to a method and an apparatus for detecting a travel yaw.
Background
In the travel business, some abnormal route situations may occur in the travel service process, for example, the service provider intentionally makes a detour or does not travel according to a specified route in order to obtain higher benefits. Currently, it is often determined whether the vehicle is yawing based on navigation. However, the method of determining whether to yaw based on navigation cannot solve the problem of yaw in the field of windward driving. The reason is that according to historical data, the car owner does not walk according to navigation, and if the car owner is judged to yaw according to navigation, an alarm disaster is easily caused, and the riding experience is influenced.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the specification provides a stroke yaw detection method and device, and aims to solve the problem that a method for effectively detecting the yaw of a downwind turbine is lacked in the prior art.
An embodiment of the present specification provides a stroke yaw detection method, including: receiving a yaw detection request, wherein the yaw detection request comprises a plurality of track points of a target journey in a target time period and at least one planned route of the target journey; determining an enclosing frame corresponding to each planned route in at least one planned route, wherein the enclosing frame corresponding to each planned route comprises each planned route; generating a target characteristic vector of a target travel in a target time period based on a plurality of track points of the target time period, at least one planned route and surrounding frames corresponding to the planned routes; and judging whether the target travel deviates in the target time period or not according to the target characteristic vector.
In one embodiment, determining the bounding box corresponding to each planned route in the at least one planned route includes: and determining the circumscribed rectangle frame of each planned route in the at least one planned route as the surrounding frame corresponding to each planned route in the at least one planned route.
In one embodiment, generating a target feature vector of a target trip in a target time period based on a plurality of track points of the target time period, at least one planned route, and a bounding box corresponding to each planned route, includes: determining a target direction for the target trip based on the at least one planned route; determining the current driving direction according to a plurality of track points of the target time period; determining whether an included angle between the target direction and the current driving direction is smaller than a first preset angle or not to obtain a first yaw characteristic; calculating the number of track points in at least one of the plurality of enclosing frames in the plurality of track points of the target time period to obtain a second yaw characteristic; determining whether the distance from the first track point of the plurality of track points in the target time period to the end point of the target travel is larger than the distance from the last track point of the plurality of track points in the target time period to the end point of the target travel to obtain a third yaw characteristic; generating a target feature vector based on the first yaw feature, the second yaw feature, and the third yaw feature.
In one embodiment, the yaw detection request further includes a plurality of trajectory points of the target trip over a previous time period of the target time period; correspondingly, generating a target feature vector of the target journey in the target time period based on the plurality of track points, the at least one planned route and the bounding boxes corresponding to the planned routes in the target time period comprises: determining a target direction for the target trip based on the at least one planned route; determining the current driving direction according to a plurality of track points of the target time period; determining whether an included angle between the target direction and the current driving direction is smaller than a first preset angle or not to obtain a first yaw characteristic; calculating the number of track points in at least one of the plurality of enclosing frames in the plurality of track points of the target time period to obtain a second yaw characteristic; determining whether the distance from the first track point of the plurality of track points in the target time period to the end point of the target travel is larger than the distance from the last track point of the plurality of track points in the target time period to the end point of the target travel to obtain a third yaw characteristic; determining the driving direction of the previous time period according to the plurality of track points of the previous time period; determining whether an included angle between the current driving direction and the driving direction of the previous time period is smaller than a second preset angle or not to obtain a fourth yaw characteristic; generating a target feature vector based on the first yaw feature, the second yaw feature, the third yaw feature, and the fourth yaw feature.
In one embodiment, the determining whether the target travel drifts within the target time period according to the target feature vector includes: and inputting the target characteristic vector into the trained logistic regression model to judge whether the target travel deviates at the current time end.
In one embodiment, the logistic regression model is trained by: obtaining a plurality of characteristic vectors, and calculating a yaw index corresponding to each characteristic vector in the plurality of characteristic vectors; when the yaw index is larger than the preset value, sending a yaw prompt to the user and receiving yaw confirmation information returned by the user; taking the plurality of characteristic vectors and yaw confirmation information corresponding to the plurality of characteristic vectors as a training sample set; and training the preset model by utilizing the training sample set to obtain a trained logistic regression model.
An embodiment of the present specification further provides a stroke yaw detection apparatus, including: the system comprises a receiving module, a judging module and a judging module, wherein the receiving module is used for receiving a yaw detection request, and the yaw detection request comprises a plurality of track points of a target travel in a target time period and at least one planned route of the target travel; the determining module is used for determining an enclosing frame corresponding to each planned route in at least one planned route, wherein the enclosing frame corresponding to each planned route comprises each planned route; the generating module is used for generating a target characteristic vector of the target journey in the target time period based on the plurality of track points, at least one planned route and the surrounding frames corresponding to the planned routes in the target time period; and the judging module is used for judging whether the target travel deviates within the target time period according to the target characteristic vector.
In one embodiment, the determining module is specifically configured to: and determining the circumscribed rectangle frame of each planned route in the at least one planned route as the surrounding frame corresponding to each planned route in the at least one planned route.
Embodiments of the present specification further provide a computer device comprising a processor and a memory for storing processor-executable instructions, which when executed by the processor implement the steps of the method of detecting a stroke yaw as described in any of the above embodiments.
Embodiments of the present specification also provide a computer readable storage medium having stored thereon computer instructions that, when executed, implement the steps of the method of detecting a stroke yaw described in any of the above embodiments.
In an embodiment of the present specification, a method for detecting a trip and a yaw is provided, where a yaw detection server may receive a yaw detection request, where the yaw detection request may include a plurality of trajectory points of a target trip in a target time period and at least one planned route of the target trip, and may determine an enclosure frame corresponding to each planned route in the at least one planned route, where the enclosure frame corresponding to each planned route includes each planned route, and a target feature vector of the target trip in the target time period is generated based on the plurality of trajectory points of the target time period, the at least one planned route, and the enclosure frame corresponding to each planned route, and then whether the target trip has a yaw in the target time period may be determined according to the target feature vector. In the above scheme, whether the target travel drifts in the target time period can be judged by combining the plurality of track points of the target travel in the target time period, the at least one planned route of the target travel and the bounding box corresponding to each planned route in the at least one planned route.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification, are incorporated in and constitute a part of this specification, and are not intended to limit the specification. In the drawings:
FIG. 1 illustrates a flow chart of a method of travel yaw detection in one embodiment of the present description;
FIG. 2 illustrates a flow chart of a method of travel yaw detection in one embodiment of the present description;
FIG. 3 illustrates a schematic view of a stroke yaw detection arrangement in one embodiment of the present description;
FIG. 4 shows a schematic diagram of a computer device in one embodiment of the present description.
Detailed Description
The principles and spirit of the present description will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are given solely to enable those skilled in the art to better understand and to implement the present description, and are not intended to limit the scope of the present description in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As will be appreciated by one skilled in the art, embodiments of the present description may be embodied as a system, an apparatus, a method, or a computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
The embodiment of the specification provides a stroke yaw detection method. In one scenario example of the present specification, the trip yaw detection method may be applied to a yaw detection server. The yaw detection server may receive a yaw detection request. In one embodiment, the yaw detection server may receive a yaw detection request sent by a trip service provider. The travel service provider may direct the driver user and the passenger user to a platform that provides online travel services. In another embodiment, the yaw detection server may receive a yaw detection request sent by a passenger user or a driver user through a client device. The yaw detection request can include a plurality of track points of the target journey in the target time period and at least one planned route of the target journey. The yaw detection server may determine an enclosure corresponding to each planned route of the at least one planned route of the target trip in response to the yaw detection request. Wherein, the bounding box corresponding to the planning route contains the planning route. And then, the yaw detection server can generate a target characteristic vector of the target travel in the target time period based on the plurality of track points, the at least one planned route and the surrounding frames corresponding to the planned routes in the target time period, so that whether the target travel has yaw in the target time period can be judged according to the target characteristic vector.
FIG. 1 shows a flow chart of a method of travel yaw detection in one embodiment of the present description. Although the present specification provides method operational steps or apparatus configurations as illustrated in the following examples or figures, more or fewer operational steps or modular units may be included in the methods or apparatus based on conventional or non-inventive efforts. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution sequence of the steps or the module structure of the apparatus is not limited to the execution sequence or the module structure described in the embodiments and shown in the drawings. When the described method or module structure is applied in an actual device or end product, the method or module structure according to the embodiments or shown in the drawings can be executed sequentially or executed in parallel (for example, in a parallel processor or multi-thread processing environment, or even in a distributed processing environment).
Specifically, as shown in fig. 1, a method for detecting a yaw stroke provided by an embodiment of the present disclosure may include the following steps.
Step S101, receiving a yaw detection request, wherein the yaw detection request comprises a plurality of track points of a target journey in a target time period and at least one planned route of the target journey.
The method in the embodiments of the present specification may be applied to a yaw detection server. The yaw detection server may receive a yaw detection request. In one embodiment, the yaw detection server may receive a yaw detection request sent by a trip service provider. The travel service provider may direct the driver user and the passenger user to a platform that provides online travel services. In another embodiment, the yaw detection server may receive a yaw detection request sent by a passenger user or a driver user through a client device.
The yaw detection request may include a plurality of trajectory points of the target trip over the target time period and at least one planned route of the target trip. The target trip is a trip to be detected and may at least include trip start point information and trip end point information. The type of target trip may include a taxi trip, a express trip, or a tailwind trip, etc. The target time period refers to a time period in which whether yaw occurs is to be detected. The target time period may be the current time period or a preset time period before the current time period. The current time period may be a time period corresponding to the time of generation of the yaw detection request, for example, within three minutes, within five minutes, or within ten minutes before the generation of the yaw detection request. The track point refers to position coordinate data of a vehicle corresponding to the target travel at a certain moment. The plurality of trace points of the target time period may include position coordinate data of the vehicle corresponding to the target trip at each of a plurality of times within the target time period. The at least one planned route of the target trip refers to at least one route from a start point of the target trip to an end point of the target trip. The planned route may be a route from the start of travel to the end of travel that the navigation server generates from the start of travel and the end of travel.
Step S102, determining an enclosing frame corresponding to each planned route in at least one planned route, wherein the enclosing frame corresponding to each planned route comprises each planned route.
The yaw detection server may determine an enclosure corresponding to each planned route in the at least one planned route after receiving the yaw detection request. Each planned route is positioned in the corresponding surrounding frame of the planned route. In one embodiment, the yaw detection server may determine a rectangular box containing each planned route of the at least one planned route as a bounding box corresponding to each planned route. In another embodiment, for each planned route, the yaw detection server may generate two routes that are parallel to the planned route and have a preset vertical distance from the planned route, and obtain the bounding box corresponding to the planned route after connecting the start point and the end point of the two routes respectively by using two line segments. One skilled in the art will appreciate that other ways may be used to determine the bounding box corresponding to each planned route of the at least one planned route of the target trip.
Step S103, generating a target characteristic vector of the target journey in the target time period based on the plurality of track points, the at least one planned route and the surrounding frames corresponding to the planned routes in the target time period.
And step S104, judging whether the target travel deviates in the target time period according to the target characteristic vector.
The yaw detection server can generate a target feature vector of the target journey in the target time period based on the plurality of track points of the target time period, the at least one planned route and the bounding box corresponding to each planned route. Wherein the target feature vector may comprise a plurality of features for characterizing the yaw probability. The yaw detection server can preprocess a plurality of track points of the target time period, at least one planned route and a bounding box corresponding to each planned route to obtain a plurality of yaw characteristics. And then, forming a target feature vector by the obtained multiple yaw features. For example, the yaw detection server may determine the current driving direction of the target time period according to the plurality of track points of the target time period, may determine the target direction according to the planned route of the target trip, and determine the relationship between the current driving direction and the target direction to obtain the yaw characteristic. For another example, the yaw detection server may determine whether each of the plurality of trajectory points is within at least one of the plurality of bounding boxes, resulting in the yaw characteristic. For another example, the yaw detection server may calculate distances from each of the plurality of track points to the stroke end point, and compare the distances to obtain the yaw characteristic.
After the target characteristic vector is obtained, the yaw detection server can judge whether the target travel has yaw in the target time period according to the target characteristic vector. In one embodiment, the yaw detection server may compare the target feature vector with feature vectors of a plurality of known yaw categories, and calculate the distance or similarity between the two, thereby determining whether the target trip has yawed at the target time period.
In the yaw detection method in the embodiment, whether the target travel drifts in the target time period can be judged by combining the multiple track points of the target travel in the target time period, the at least one planned route of the target travel and the bounding box corresponding to each planned route in the at least one planned route, and compared with the method for judging whether the target travel drifts in the target time period only based on the track of the target time period and the planned route of the target travel, whether the windward vehicle or other order travel drifts can be more accurately predicted, so that the driving experience can be improved, and the driving safety can be ensured.
In some embodiments of the present description, determining an enclosure box corresponding to each planned route in the at least one planned route may include: and determining the circumscribed rectangle frame of each planned route in the at least one planned route as the surrounding frame corresponding to each planned route in the at least one planned route.
Specifically, after receiving at least one planned route of a target trip sent by the navigation server, the yaw detection server may determine an enclosure corresponding to each planned route. For example, the bounding rectangle of each planned route in the at least one planned route may be determined as the bounding box corresponding to the planned route. For another example, a bounding box concentric with the circumscribed rectangle of each planned route may be determined as the bounding box corresponding to the planned route, and the distance between the two is a preset threshold. By the method, the enclosure frame corresponding to each planned route can be determined, so that whether yaw occurs or not is judged subsequently by combining the enclosure frame, and the accuracy of yaw detection can be improved.
In some embodiments of the present description, generating a target feature vector of a target trip in a target time period based on a plurality of track points of the target time period, at least one planned route, and a bounding box corresponding to each planned route, may include: determining a target direction for the target trip based on the at least one planned route; determining the current driving direction according to a plurality of track points of the target time period; determining whether an included angle between the target direction and the current driving direction is smaller than a first preset angle or not to obtain a first yaw characteristic; calculating the number of track points in at least one of the plurality of enclosing frames in the plurality of track points of the target time period to obtain a second yaw characteristic; determining whether the distance from the first track point of the plurality of track points in the target time period to the end point of the target travel is larger than the distance from the last track point of the plurality of track points in the target time period to the end point of the target travel to obtain a third yaw characteristic; generating a target feature vector based on the first yaw feature, the second yaw feature, and the third yaw feature.
Specifically, the yaw detection server may determine a target direction of the target journey from the at least one planned route, i.e. a direction from a start point to an end point of the planned route is determined as the target direction of the target journey. The current driving direction of the target vehicle can be determined according to the plurality of track points of the target time period. For example, the direction from the first track point to the last track point in the target time period may be determined as the current driving direction. For another example, the direction from the middle track point to the last track point in the current time may be determined as the current driving direction. And sequencing the plurality of track points in the target time period according to the time sequence. The first track point is the earliest track point in time, and the last track point is the latest track point in time. Thereafter, it may be determined whether an angle between the target direction and the current driving direction is less than a first preset angle. Wherein, the first preset angle may be set to 80 °, 90 °, 100 °, or the like. The first yaw characteristic may be set to 0 in a case where it is determined that an angle between the target direction and the current driving direction is smaller than a first preset angle, and may be set to 1 in a case where it is greater than or equal to the first preset angle.
The yaw detection server can also determine the number of track points located in at least one of the plurality of enclosure frames in the plurality of track points of the target time period to obtain a second yaw characteristic. For example, the second yaw feature may be directly set to the number of track points located within at least one of the plurality of bounding boxes. For another example, when the number of track points located in at least one of the plurality of bounding boxes is greater than the preset number, the second yaw characteristic may be set to 0; when the number of track points located in at least one of the plurality of bounding boxes is less than or equal to a preset number, the second yaw characteristic may be set to 1.
The yaw detection server can also determine whether the distance from the first track point in the plurality of track points in the target time period to the end point of the target trip is greater than the distance from the last track point in the target time period to the end point of the target trip. For example, if the distance between the first track point and the end point of the target trip is greater than the distance between the last track point and the end point of the target trip, the third yaw characteristic may be set to 0, otherwise set to 1.
After obtaining the first, second, and third yaw signatures, a target feature vector for the target stroke within the target time period may be generated based on the first, second, and third yaw signatures. For example, the target feature vector may be directly composed of the first yaw feature, the second yaw feature, and the third yaw feature. For another example, the target feature vector may consist of a first yaw feature multiplied by a first weight, a second yaw feature multiplied by a second weight, and a third yaw feature multiplied by a third weight. The first weight, the second weight and the third weight may be set manually, or the weights may be trained by logistic regression. This is not limited by the present description. In the above manner, the target feature vector is generated by combining a plurality of features, so that whether the yaw is more accurate is judged according to the target feature vector.
In some embodiments of the present description, the yaw detection request further includes a plurality of trajectory points of the target trip in a previous time period of the target time period; correspondingly, generating a target feature vector of the target trip in the target time period based on the plurality of track points, the at least one planned route, and the bounding box corresponding to each planned route in the target time period may include: determining a target direction for the target trip based on the at least one planned route; determining the current driving direction according to a plurality of track points of the target time period; determining whether an included angle between the target direction and the current driving direction is smaller than a first preset angle or not to obtain a first yaw characteristic; calculating the number of track points in at least one of the plurality of enclosing frames in the plurality of track points of the target time period to obtain a second yaw characteristic; determining whether the distance from the first track point of the plurality of track points in the target time period to the end point of the target travel is larger than the distance from the last track point of the plurality of track points in the target time period to the end point of the target travel to obtain a third yaw characteristic; determining the driving direction of the previous time period according to the plurality of track points of the previous time period; determining whether an included angle between the current driving direction and the driving direction of the previous time period is smaller than a second preset angle or not to obtain a fourth yaw characteristic; generating a target feature vector based on the first yaw feature, the second yaw feature, the third yaw feature, and the fourth yaw feature.
Specifically, the yaw detection server may determine a target direction of the target journey from the at least one planned route, i.e. a direction from a start point to an end point of the planned route is determined as the target direction of the target journey. The current driving direction of the target vehicle can be determined according to the plurality of track points of the target time period. For example, the direction from the first track point to the last track point in the target time period may be determined as the current driving direction. For another example, the direction from the middle track point to the last track point in the current time may be determined as the current driving direction. And sequencing the plurality of track points in the target time period according to the time sequence. The first track point is the earliest track point in time, and the last track point is the latest track point in time. Thereafter, it may be determined whether an angle between the target direction and the current driving direction is less than a first preset angle. Wherein, the first preset angle may be set to 80 °, 90 °, 100 °, or the like. The first yaw characteristic may be set to 0 in a case where it is determined that an angle between the target direction and the current driving direction is smaller than a first preset angle, and may be set to 1 in a case where it is greater than or equal to the first preset angle.
The yaw detection server can also determine the number of track points located in at least one of the plurality of enclosure frames in the plurality of track points of the target time period to obtain a second yaw characteristic. For example, the second yaw feature may be directly set to the number of track points located within at least one of the plurality of bounding boxes. For another example, when the number of track points located in at least one of the plurality of bounding boxes is greater than the preset number, the second yaw characteristic may be set to 0; when the number of track points located in at least one of the plurality of bounding boxes is less than or equal to a preset number, the second yaw characteristic may be set to 1.
The yaw detection server can also determine whether the distance from the first track point in the plurality of track points in the target time period to the end point of the target trip is greater than the distance from the last track point in the target time period to the end point of the target trip. For example, if the distance between the first track point and the end point of the target trip is greater than the distance between the last track point and the end point of the target trip, the third yaw characteristic may be set to 0, otherwise set to 1.
The yaw detection server may determine the driving direction of the previous time period according to the plurality of track points of the previous time period of the target time period carried in the yaw detection request. For example, the direction from the first track point to the last track point of the previous time period may be determined as the driving direction of the previous time period. For another example, the direction from the middle track point to the last track point in the previous time period may be determined as the driving direction in the previous time period. It may be determined whether an angle between the current driving direction and the driving direction of the previous time period is less than a second preset angle. Wherein the second preset angle may be set to 60 °, 70 °, 90 °, or the like. And setting the fourth yaw characteristic as 0 under the condition that the included angle between the current driving direction and the driving direction of the previous time period is smaller than a second preset angle, otherwise, setting the fourth yaw characteristic as 1.
After obtaining the first, second, third, and fourth yaw signatures, a target feature vector for the target stroke within the target time period may be generated based on the first, second, third, and fourth yaw signatures. For example, the target feature vector may be directly composed of the first yaw feature, the second yaw feature, the third yaw feature, and the fourth yaw feature. For another example, the target feature vector may be composed of a first yaw feature multiplied by a first weight, a second yaw feature multiplied by a second weight, a third yaw feature multiplied by a third weight, and a fourth yaw feature multiplied by a fourth weight. The first weight, the second weight, the third weight and the fourth weight may be manually set, or respective weights may be trained by logistic regression. This is not limited by the present description. In the above manner, the fourth yaw characteristic is generated by combining the track point of the previous time period to generate the target characteristic vector based on the plurality of characteristics, so that whether yaw is more accurate is judged according to the target characteristic vector.
In some embodiments of the present description, determining whether the target stroke is yawed within the target time period according to the target feature vector may include: and inputting the target characteristic vector into the trained logistic regression model to judge whether the target travel deviates at the current time end. Specifically, the target feature vector may be input into a trained logistic regression model, and a prediction result of whether the target journey drifts in the target time period is output. Whether the yaw occurs or not is judged through the logistic regression model, and the judgment can be quickly and accurately made.
In some embodiments of the present description, the logistic regression model may be trained by: obtaining a plurality of characteristic vectors, and calculating a yaw index corresponding to each characteristic vector in the plurality of characteristic vectors; when the yaw index is larger than the preset value, sending a yaw prompt to the user and receiving yaw confirmation information returned by the user; taking the plurality of characteristic vectors and yaw confirmation information corresponding to the plurality of characteristic vectors as a training sample set; and training the preset model by utilizing the training sample set to obtain a trained logistic regression model.
Specifically, track points in a plurality of time periods of a plurality of historical trips and at least one planned route of each of the plurality of historical trips may be obtained. And calculating a surrounding frame corresponding to each planned route in at least one planned route corresponding to each historical travel, and obtaining a feature vector of each historical travel in each time period based on the plurality of track points, the at least one planned route and the surrounding frame corresponding to each planned route in each time period. A yaw index may be calculated for each feature vector. Wherein the larger the yaw index, the greater the likelihood of yaw. And in the process of each historical trip, once the fact that the yaw index of the target time period is greater than the preset value is detected, sending a yaw prompt to a passenger user. The user may return a yaw confirmation message in response to the alert, i.e., determine whether a yaw has occurred. And taking the plurality of characteristic vectors and yaw confirmation information corresponding to the plurality of characteristic vectors as a training sample set, wherein the characteristic vectors are independent variables, and the yaw confirmation information is dependent variables or labels. The training sample set can be used for training the preset model to obtain a trained logistic regression model. And then, inputting the target feature vector to be detected into the trained logistic regression model to obtain a judgment result of whether the target feature vector deviates. In some embodiments, new models may be trained periodically to improve the accuracy of model predictions. Through the mode, model training can be performed by utilizing historical data.
The above method is described below with reference to a specific example, however, it should be noted that the specific example is only for better describing the present specification and should not be construed as an undue limitation on the present specification.
The embodiment of the invention provides a stroke yaw detection method. In the embodiment, the tracks reported by the driver and the crew are collected in real time, the circumscribed rectangle frame with the minimum area is calculated according to the track information, the characteristic change of the track points reported in each batch is described, and a yaw score is calculated. Referring to fig. 2, a flowchart of a method for detecting a yaw stroke in the present embodiment is shown. As shown in fig. 2, the specific steps are as follows.
Step 1, requesting the senior to obtain a planned route of starting and ending points of passengers and a planned route of starting and ending points of passengers by a driver.
And 2, acquiring track points of the driver-superior batch and the current batch which are acquired in real time (1 batch in 5 minutes).
And 3, calculating a circumscribed rectangular frame with the minimum area of each planned route.
And 4, calculating whether the included angle between the connecting line of the starting point and the finishing point of the passenger and the current driving direction of the passenger is an acute angle.
And 5, traversing the real-time acquisition points, and judging whether each point exists in a certain surrounding frame.
And 6, calculating whether the distances from the first point and the last point in the collection points of the batch of passengers to the terminal point are further.
And 7, calculating whether the included angle of the track point sets of the current batch and the previous batch of the passengers is an acute angle.
And 8, calculating a yaw score according to the calculated characteristics.
And 9, after the data are accumulated for a period of time after the data are online, training the weight of each feature by using logistic regression to obtain the respective weight. For example, after the first time on line, when the yaw score is greater than 3 points (i.e., a hit yaw), the passenger is asked if it is yaw, and the result is then dropped into the background database. After the data is accumulated, the positive sample of the logistic regression model is the passenger yaw, and the negative sample is the non-yaw and is a two-classification task. The weight of each feature can be trained through the data of the first period.
A new model is trained periodically based on the data, and a generalized trajectory yaw algorithm in the windward field can be realized. By the method in the embodiment, the problem that a tailgating driver cannot judge the pain point of yaw according to navigation can be solved, and the yaw accuracy of the algorithm reaches 90%, so that safe driving protection can be provided for drivers and passengers.
Based on the same inventive concept, embodiments of the present disclosure also provide a stroke yaw detection device, as described in the following embodiments. Because the principle of the stroke yaw detection device for solving the problems is similar to that of the stroke yaw detection method, the stroke yaw detection device can be implemented by the stroke yaw detection method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Fig. 3 is a block diagram of a structure of a stroke yaw detecting device according to an embodiment of the present disclosure, and as shown in fig. 3, the stroke yaw detecting device includes: the receiving module 301, the determining module 302, the generating module 303, and the determining module 304 are described below.
The receiving module 301 is configured to receive a yaw detection request, where the yaw detection request includes a plurality of track points of a target trip in a target time period and at least one planned route of the target trip.
The determining module 302 is configured to determine an enclosure corresponding to each planned route in at least one planned route, where the enclosure corresponding to each planned route includes each planned route.
The generating module 303 is configured to generate a target feature vector of the target trip in the target time period based on the multiple track points in the target time period, the at least one planned route, and the bounding box corresponding to each planned route.
The judging module 304 is configured to judge whether the target stroke is yawed within the target time period according to the target feature vector.
In some embodiments of the present description, the determining module may be specifically configured to: and determining the circumscribed rectangle frame of each planned route in the at least one planned route as the surrounding frame corresponding to each planned route in the at least one planned route.
In some embodiments of the present description, the generation module may be specifically configured to: determining a target direction for the target trip based on the at least one planned route; determining the current driving direction according to a plurality of track points of the target time period; determining whether an included angle between the target direction and the current driving direction is smaller than a first preset angle or not to obtain a first yaw characteristic; calculating the number of track points in at least one of the plurality of enclosing frames in the plurality of track points of the target time period to obtain a second yaw characteristic; determining whether the distance from the first track point of the plurality of track points in the target time period to the end point of the target travel is larger than the distance from the last track point of the plurality of track points in the target time period to the end point of the target travel to obtain a third yaw characteristic; generating a target feature vector based on the first yaw feature, the second yaw feature, and the third yaw feature.
In some embodiments of the present description, the yaw detection request may further include a plurality of trajectory points of the target trip over a previous time period of the target time period. Accordingly, the generating module may be specifically configured to: determining a target direction for the target trip based on the at least one planned route; determining the current driving direction according to a plurality of track points of the target time period; determining whether an included angle between the target direction and the current driving direction is smaller than a first preset angle or not to obtain a first yaw characteristic; calculating the number of track points in at least one of the plurality of enclosing frames in the plurality of track points of the target time period to obtain a second yaw characteristic; determining whether the distance from the first track point of the plurality of track points in the target time period to the end point of the target travel is larger than the distance from the last track point of the plurality of track points in the target time period to the end point of the target travel to obtain a third yaw characteristic; determining the driving direction of the previous time period according to the plurality of track points of the previous time period; determining whether an included angle between the current driving direction and the driving direction of the previous time period is smaller than a second preset angle or not to obtain a fourth yaw characteristic; generating a target feature vector based on the first yaw feature, the second yaw feature, the third yaw feature, and the fourth yaw feature.
In some embodiments of the present disclosure, the determining module may be specifically configured to input the target feature vector into a trained logistic regression model to determine whether the target journey drifts at the current time end.
In some embodiments of the present description, the logistic regression model is trained by: obtaining a plurality of characteristic vectors, and calculating a yaw index corresponding to each characteristic vector in the plurality of characteristic vectors; when the yaw index is larger than the preset value, sending a yaw prompt to the user and receiving yaw confirmation information returned by the user; taking the plurality of characteristic vectors and yaw confirmation information corresponding to the plurality of characteristic vectors as a training sample set; and training the preset model by utilizing the training sample set to obtain a trained logistic regression model.
From the above description, it can be seen that the embodiments of the present specification achieve the following technical effects: the method can be used for judging whether the target travel deviates in the target time period by combining a plurality of track points of the target travel in the target time period, at least one planned route of the target travel and the bounding box corresponding to each planned route in the at least one planned route, and can be used for more accurately predicting whether the windward vehicle or other order travel deviates compared with the method for judging whether the target travel deviates in the target time period only based on the track of the target time period and the planned route of the target travel, so that the method is favorable for improving the riding experience and ensuring the riding safety.
The embodiment of the present specification further provides a computer device, which may specifically refer to a schematic structural diagram of a computer device based on the travel yaw detection method provided in the embodiment of the present specification, shown in fig. 4, and the computer device may specifically include an input device 41, a processor 42, and a memory 43. Wherein the memory 43 is for storing processor executable instructions. The processor 42, when executing the instructions, performs the steps of the method of stroke yaw detection described in any of the embodiments above.
In this embodiment, the input device may be one of the main apparatuses for information exchange between a user and a computer system. The input device may include a keyboard, a mouse, a camera, a scanner, a light pen, a handwriting input board, a voice input device, etc.; the input device is used to input raw data and a program for processing the data into the computer. The input device can also acquire and receive data transmitted by other modules, units and devices. The processor may be implemented in any suitable way. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. The memory may in particular be a memory device used in modern information technology for storing information. The memory may include multiple levels, and in a digital system, the memory may be any memory as long as it can store binary data; in an integrated circuit, a circuit without a physical form and with a storage function is also called a memory, such as a RAM, a FIFO and the like; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card and the like.
In this embodiment, the functions and effects of the specific implementation of the computer device can be explained in comparison with other embodiments, and are not described herein again.
There is also provided in an embodiment of the present specification a computer storage medium based on a stroke yaw detection method, the computer storage medium storing computer program instructions which, when executed, implement the steps of the stroke yaw detection method of any of the above embodiments.
In this embodiment, the storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk Drive (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.
In this embodiment, the functions and effects specifically realized by the program instructions stored in the computer storage medium can be explained by comparing with other embodiments, and are not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the present specification described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed over a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different from that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, embodiments of the present description are not limited to any specific combination of hardware and software.
It is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and many applications other than the examples provided will be apparent to those of skill in the art upon reading the above description. The scope of the description should, therefore, be determined not with reference to the above description, but instead should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The above description is only a preferred embodiment of the present disclosure, and is not intended to limit the present disclosure, and it will be apparent to those skilled in the art that various modifications and variations can be made in the embodiment of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present specification shall be included in the protection scope of the present specification.
Claims (10)
1. A method of stroke yaw detection, comprising:
receiving a yaw detection request, wherein the yaw detection request comprises a plurality of track points of a target journey in a target time period and at least one planned route of the target journey;
determining an enclosure frame corresponding to each planned route in the at least one planned route, wherein the enclosure frame corresponding to each planned route comprises each planned route;
generating a target feature vector of the target journey in the target time period based on the plurality of track points of the target time period, the at least one planned route and the surrounding frame corresponding to each planned route;
and judging whether the target travel deviates in the target time period or not according to the target characteristic vector.
2. The method of claim 1, wherein determining the bounding box for each planned route of the at least one planned route comprises:
and determining a circumscribed rectangle frame of each planned route in the at least one planned route as an enclosure frame corresponding to each planned route in the at least one planned route.
3. The method of claim 1, wherein generating the target feature vector of the target journey at the target time period based on the plurality of track points of the target time period, the at least one planned route, and the bounding box corresponding to each planned route comprises:
determining a target direction for the target trip based on the at least one planned route; determining the current driving direction according to the plurality of track points of the target time period; determining whether an included angle between the target direction and the current driving direction is smaller than a first preset angle or not to obtain a first yaw characteristic;
calculating the number of track points in at least one of the plurality of enclosure frames in the plurality of track points of the target time period to obtain a second yaw characteristic;
determining whether the distance from the first track point of the plurality of track points of the target time period to the end point of the target travel is greater than the distance from the last track point of the plurality of track points of the target time period to the end point of the target travel to obtain a third yaw characteristic;
generating a target feature vector based on the first, second, and third yaw features.
4. The method of claim 1, wherein the yaw detection request further comprises a plurality of trajectory points of the target trip over a previous time period of a target time period;
correspondingly, generating the target feature vector of the target journey in the target time period based on the plurality of track points of the target time period, the at least one planned route and the bounding box corresponding to each planned route, includes:
determining a target direction for the target trip based on the at least one planned route; determining the current driving direction according to the plurality of track points of the target time period; determining whether an included angle between the target direction and the current driving direction is smaller than a first preset angle or not to obtain a first yaw characteristic;
calculating the number of track points in at least one of the plurality of enclosure frames in the plurality of track points of the target time period to obtain a second yaw characteristic;
determining whether the distance from the first track point of the plurality of track points of the target time period to the end point of the target travel is greater than the distance from the last track point of the plurality of track points of the target time period to the end point of the target travel to obtain a third yaw characteristic;
determining the driving direction of the previous time period according to the plurality of track points of the previous time period; determining whether an included angle between the current driving direction and the driving direction of the previous time period is smaller than a second preset angle or not to obtain a fourth yaw characteristic;
generating a target feature vector based on the first, second, third, and fourth yaw features.
5. The method of claim 1, wherein determining whether the target trip yaws within a target time period according to the target eigenvector comprises:
and inputting the target characteristic vector into a trained logistic regression model to judge whether the target travel deviates at the current time end.
6. The method of claim 5, wherein the logistic regression model is trained by:
obtaining a plurality of characteristic vectors, and calculating a yaw index corresponding to each characteristic vector in the plurality of characteristic vectors;
when the yaw index is larger than a preset value, sending a yaw prompt to a user and receiving yaw confirmation information returned by the user;
taking the plurality of feature vectors and yaw confirmation information corresponding to the plurality of feature vectors as a training sample set;
and training a preset model by using the training sample set to obtain a trained logistic regression model.
7. A stroke yaw detection device, comprising:
the system comprises a receiving module, a judging module and a judging module, wherein the receiving module is used for receiving a yaw detection request, and the yaw detection request comprises a plurality of track points of a target travel in a target time period and at least one planned route of the target travel;
a determining module, configured to determine an enclosure frame corresponding to each planned route in the at least one planned route, where the enclosure frame corresponding to each planned route includes each planned route;
the generating module is used for generating a target feature vector of the target journey in the target time period based on the plurality of track points of the target time period, the at least one planned route and the surrounding frame corresponding to each planned route;
and the judging module is used for judging whether the target travel deviates in the target time period according to the target characteristic vector.
8. The apparatus of claim 7, wherein the determining module is specifically configured to:
and determining a circumscribed rectangle frame of each planned route in the at least one planned route as an enclosure frame corresponding to each planned route in the at least one planned route.
9. A computer device comprising a processor and a memory for storing processor-executable instructions that, when executed by the processor, implement the steps of the method of any one of claims 1 to 6.
10. A computer-readable storage medium having computer instructions stored thereon which, when executed, implement the steps of the method of any one of claims 1 to 6.
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