CN112800812A - Target object lane change identification method and device, readable storage medium and electronic equipment - Google Patents
Target object lane change identification method and device, readable storage medium and electronic equipment Download PDFInfo
- Publication number
- CN112800812A CN112800812A CN201911107876.5A CN201911107876A CN112800812A CN 112800812 A CN112800812 A CN 112800812A CN 201911107876 A CN201911107876 A CN 201911107876A CN 112800812 A CN112800812 A CN 112800812A
- Authority
- CN
- China
- Prior art keywords
- target object
- lane
- relative
- distance
- change
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Traffic Control Systems (AREA)
Abstract
The embodiment of the disclosure discloses a target object lane change identification method and device, a readable storage medium and electronic equipment, wherein the method comprises the following steps: determining at least one target object in front of the vehicle; for each target object of the at least one target object, respectively determining n groups of relative distances between the target object at n time points and a lane line; each group of the relative distances comprises a distance relative to a left lane line of a lane where the target object is located and/or a distance relative to a right lane line of the lane where the target object is located; determining a rate of change of the target object relative to the lane line distance based on the n time points and the n sets of relative distances; determining whether the target object makes a lane change based on the rate of change and the relative distance. The embodiment ensures the stability of the vehicle relative to the change rate of the lane line distance, improves the accuracy of lane change behavior identification, and ensures the rapidity and the accuracy of vehicle motion trend identification.
Description
Technical Field
The present disclosure relates to computer vision technologies, and in particular, to a lane change recognition method and apparatus for a target object, a readable storage medium, and an electronic device.
Background
In advanced driving assistance systems based on visual perception, lane change behaviors of front target objects are often required to be recognized In advance, so that the system can rapidly switch CIPV (closed In-Path Vehicle) and give timely warning information for potential dangers. In addition, the behavior of recognizing the switching of the target object into and out of the own lane is required by the acc (adaptive Cruise control) function of the system. If there is no recognition of the target lane change behavior, the system will only perform CIPV selection when the vehicle enters the own lane, resulting in a delay in CIPV selection and collision warning.
Disclosure of Invention
The present disclosure is proposed to solve the above technical problems. The embodiment of the disclosure provides a target object lane change identification method and device, a readable storage medium and electronic equipment.
According to an aspect of the embodiments of the present disclosure, there is provided a target object lane change identification method, including:
determining at least one target object in front of the vehicle;
for each target object of the at least one target object, respectively determining n groups of relative distances between the target object at n time points and a lane line; each group of the relative distances comprises a distance relative to a left lane line of a lane where the target object is located and/or a distance relative to a right lane line of the lane where the target object is located;
determining a rate of change of the target object relative lane line distance over time based on the n time points and the n sets of relative distances;
determining whether the target object makes a lane change based on the rate of change and the relative distance.
According to another aspect of the embodiments of the present disclosure, there is provided a target object lane change recognition apparatus including:
a target determination module for determining at least one target object in front of the vehicle;
a relative distance determination module, configured to determine, for each of the at least one target object determined by the target determination module, n sets of relative distances between the n time points and a lane line of the target object, respectively; each group of the relative distances comprises a distance relative to a left lane line of a lane where the target object is located and/or a distance relative to a right lane line of the lane where the target object is located;
a change rate determination module, configured to determine a change rate of the target object with respect to the lane line distance over time based on the n time points and the n sets of relative distances determined by the relative distance determination module;
and the lane change identification module is used for determining whether the target object changes lanes or not based on the change rate determined by the change rate determination module and the relative distance determined by the relative distance determination module.
According to still another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program for executing the target object lane change identification method of the above embodiments.
According to still another aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the lane change identification method for the target object according to the above embodiment.
Based on the lane change identification method and device for the target object, the readable storage medium and the electronic device provided by the above embodiments of the present disclosure, at least one target object in front of the vehicle is determined; for each target object of the at least one target object, respectively determining n groups of relative distances between the target object at n time points and a lane line; each group of the relative distances comprises a distance relative to a left lane line of a lane where the target object is located and/or a distance relative to a right lane line of the lane where the target object is located; determining a rate of change of the target object relative to the lane line distance based on the n time points and the n sets of relative distances; determining whether the target object makes a lane change based on the rate of change and the relative distance. According to the method and the device, the appropriate periodicity n is selected, the distance change rate of the target object relative to the lane line in the periodicity is used as the final distance change rate of the target object relative to the lane line, the stability of the change rate of the vehicle relative to the lane line distance is guaranteed, the accuracy of lane change behavior recognition is improved, and the rapidity and the accuracy of vehicle motion trend recognition are guaranteed.
The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in more detail embodiments of the present disclosure with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the principles of the disclosure and not to limit the disclosure. In the drawings, like reference numbers generally represent like parts or steps.
Fig. 1 is a schematic flowchart of a lane change identification method for a target object according to an exemplary embodiment of the present disclosure.
Fig. 2A is a schematic diagram of determining a vehicle movement trend by using a first counting mode in the prior art.
Fig. 2B is a schematic diagram of determining a vehicle movement trend by using a second counting method in the prior art.
Fig. 3A is a graph of a change in yaw rate when the number of cycles N is 15 in the target object lane change identification method provided by the embodiment of the present disclosure.
Fig. 3B is a graph of a change in yaw rate when the number of cycles N is 30 in the target object lane change identification method provided by the embodiment of the present disclosure.
Fig. 3C is a graph of the distance between the vehicle and the lane line as a function of the number of frames according to the embodiment of the disclosure.
Fig. 3D is a graph of the rate of change of the distance between the vehicle and the left lane line as a function of the number of frames, provided by the embodiment of the disclosure.
Fig. 3E is a graph of the rate of change of the distance between the vehicle and the right lane line as a function of the number of frames according to the embodiment of the disclosure.
Fig. 3F is a graph of the rate of change of the distance between the vehicle and the lane line as a function of the number of frames according to the embodiment of the disclosure.
Fig. 4 is a flowchart illustrating a lane change identification method for a target object according to another exemplary embodiment of the present disclosure.
Fig. 5 is a schematic flow chart of step 403 in the embodiment shown in fig. 4 of the present disclosure.
Fig. 6 is a flowchart illustrating a lane change recognition method for a target object according to still another exemplary embodiment of the present disclosure.
Fig. 7 is a flowchart illustrating a lane change recognition method for a target object according to still another exemplary embodiment of the present disclosure.
Fig. 8 is a flowchart illustrating a lane change recognition method for a target object according to still another exemplary embodiment of the present disclosure.
Fig. 9 is a schematic structural diagram of a target object lane change recognition apparatus according to an exemplary embodiment of the present disclosure.
Fig. 10 is a schematic structural diagram of a target object lane change recognition apparatus according to another exemplary embodiment of the present disclosure.
Fig. 11 is a block diagram of an electronic device provided in an exemplary embodiment of the present disclosure.
Detailed Description
Hereinafter, example embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of the embodiments of the present disclosure and not all embodiments of the present disclosure, with the understanding that the present disclosure is not limited to the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
It will be understood by those of skill in the art that the terms "first," "second," and the like in the embodiments of the present disclosure are used merely to distinguish one element from another, and are not intended to imply any particular technical meaning, nor is the necessary logical order between them.
It is also understood that in embodiments of the present disclosure, "a plurality" may refer to two or more and "at least one" may refer to one, two or more.
It is also to be understood that any reference to any component, data, or structure in the embodiments of the disclosure, may be generally understood as one or more, unless explicitly defined otherwise or stated otherwise.
In addition, the term "and/or" in the present disclosure is only one kind of association relationship describing an associated object, and means that three kinds of relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in the present disclosure generally indicates that the former and latter associated objects are in an "or" relationship.
It should also be understood that the description of the various embodiments of the present disclosure emphasizes the differences between the various embodiments, and the same or similar parts may be referred to each other, so that the descriptions thereof are omitted for brevity.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
The disclosed embodiments may be applied to electronic devices such as terminal devices, computer systems, servers, etc., which are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with electronic devices, such as terminal devices, computer systems, servers, and the like, include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, networked personal computers, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
Summary of the application
In the course of implementing the present disclosure, the inventors found that, in the prior art, lane change behavior recognition of a target object (such as a vehicle) is mainly determined by the yaw rate of the target object relative to a predicted trajectory of the vehicle and the lateral distance of the target object relative to the trajectory. The technical scheme at least has the following problems: the accuracy requirement on the predicted track of the vehicle is high, the predicted track of the vehicle depends on the predicted track of the vehicle, and the vehicle lane changing behavior is mistakenly identified if the predicted track of the vehicle deviates.
Exemplary System
Practice shows that the target object often has a certain yaw speed in the lane changing process, and lane changing behavior identification needs to be combined with the movement trend of the target object.
Fig. 1 is a schematic flowchart of a lane change identification method for a target object according to an exemplary embodiment of the present disclosure. As shown in fig. 1, the method of this embodiment includes:
When the lateral deviation speed of the target object relative to the lane line is calculated, the distance from the target object to the lane line in the historical N periods is utilized, the linear relation of the distance relative to the time change is fitted by adopting a least square method, and the slope of the fitted straight line is the change rate of the distance of the target object relative to the lane line.
Setting the distance of the target object in N periods relative to the lane line as y1，y2，...，yNCorresponding times are respectively t1，t2，...，tNAccording to the least square method, the time-dependent change rate k of the distance between the target object and the lane line can be expressed as:
in the Frenet coordinate system, when the target object runs rightwards relative to the lane line, k is negative; when the target object is traveling to the left with respect to the lane line, k is positive.
And 102, recognizing the behaviors of cutting into and cutting out the lane according to the distance between the target object and the lane line and the change rate of the distance. The behavior recognition specifically includes: and comparing whether the distance between the target object and the lane line is greater than a threshold value V or less than-V or not according to the lane information of the target object, and obtaining a judgment result of the behavior of the target object in and out of the lane.
Through configuration (N, V, D), the accuracy of identifying the lane-changing behavior of the target object can be ensured, and the parameter selection strategy comprises the following steps:
firstly, the number of cycles N needs to be determined, and usually, a target object has a certain fluctuation in a short period relative to the lane line distance, and a proper number of cycles N needs to be selected. When the number N of cycles is selected too small, the method is easily influenced by the distance fluctuation of lane lines, the sidesway speed calculation jitter is large, and the judgment of lane change behaviors is not facilitated; when the number of cycles N is selected to be too large, the influence of the fluctuation of the sensing distance of the lane line can be eliminated, the yaw speed is stable, but the number of cycles N is large, the required time is long, the hysteresis is easy, and the vehicle lane change behavior identification is not facilitated to be carried out in advance, for example, the number of cycles N can be 10-50, the effect is good, and the number of cycles N can be 25-35.
After the periodicity N is determined, a suitable lane line distance change rate V and a lane line distance D need to be selected. When the vehicle changes lanes, a certain lateral deviation speed exists; when the V is too small, the lane change behavior of the vehicle is easy to be identified by mistake, and when the V is too small, the lane change behavior of the vehicle is easy to be identified by omission, for example, the change rate V of the lane line distance can be 0.1-0.9, and the effect is better, and the change rate V of the lane line distance is 0.3-0.6. The target object runs in the lane, often moves in the middle of the lane in a small range from left to right, and lane change behavior recognition needs to be combined with the distance between the target object and the lane line. The lane line distance D is smaller, the delay of the lane changing behavior of the vehicle is recognized, and the distance D is larger, so that the lane changing behavior of the vehicle is easily recognized by mistake; for example, the lane line distance D may be 0.2-2.0, which is preferable, and the lane line distance D may be 0.8-1.2.
In an monocular vision driving assistance (ADAS) system, a lane line is used as important perception data, lane line information is easy to obtain and high in accuracy, the lane line information can be used as a lane change behavior identification basis of a target object, and the vehicle cut-in and cut-out conditions cannot be judged simply according to the distance between a vehicle and the lane line. In the prior art, a method of recording the vehicle distance change trend by combining the vehicle with the lane line distance and a counting mode is generally adopted, so that the difficulty in designing a unified counting rule is high, and the simple counting rule is strongly coupled with the distance and is not beneficial to identifying the lane change behavior of the vehicle. Fig. 2A is a schematic diagram of determining a vehicle movement trend by using a first counting mode in the prior art. As shown in fig. 2A, in the first counting manner, a right counter (rightcounter), a master counter (hostcount), and a left counter (leftcounter) are respectively used to record a lane change tendency of the vehicle to the right, a lane keeping tendency, and a lane change tendency to the left, and when a target object is not changed, the moving tendency of the vehicle can be better expressed. If the counter is reset, the vehicle motion trend of the historical record is cleared; if the counter value is exchanged, the exchange rule is complex and is not easy to implement. Fig. 2B is a schematic diagram of determining a vehicle movement trend by using a second counting method in the prior art. The second counting mode adopts rightcounter and leftcounter to record the left and right trends of the vehicle, and the method is simple and easy to operate in counting, but is strongly coupled with the distance of the lane line, so that the method is not beneficial to identifying the lane change behavior of the vehicle.
The method has the advantages that the change rate of the distance of the vehicle relative to the lane line is calculated by adopting least squares to represent the vehicle movement trend, and the method is strong in implementation. In order to ensure the accuracy and continuity of the calculation of the change rate of the vehicle and the lane line, the embodiment is optimized in the following parts respectively:
1) and selecting proper cycle number to ensure the accuracy of lane change behavior identification. And reasonably optimizing the selected cycle number N, and ensuring the rapidity and the accuracy of the vehicle motion trend identification.
When the number N of cycles is selected to be small, the method is easily affected by the fluctuation of the lane line distance, the calculation jitter of the yaw rate is large, and the judgment of the lane change behavior is not facilitated, as shown in fig. 3A, fig. 3A is a graph of the change of the yaw rate when the number N of cycles is 15 in the target object lane change identification method provided by the embodiment of the present disclosure. When the cycle number N is selected to be too large, the influence of the fluctuation of the lane line sensing distance can be eliminated, the yaw speed is relatively stable, but the cycle number N is large, the required time is long, the hysteresis is easy, and the vehicle lane change behavior identification is not facilitated to be performed in advance, in the implementation process, the cycle number N is 25-35, and a good effect can be achieved, as shown in fig. 3B, fig. 3B is a curve diagram of the yaw speed change when the cycle number N is 30 in the target object lane change identification method provided by the embodiment of the disclosure.
2) And (4) distance continuity processing of the vehicle relative to the lane line is carried out, so that the continuity of the change rate of the distance of the vehicle relative to the lane line is ensured. When the vehicle just crosses a lane line on a certain side, the distance between the vehicle and the lane line changes suddenly, and fig. 3C is a graph of the change of the distance between the vehicle and the lane line with the number of frames according to the embodiment of the disclosure. As shown in FIG. 3C, near 69400 frames, the vehicle crosses the right lane line and the distance of the vehicle from the left lane line is abrupt. If optimization processing is not carried out, the change rate of the vehicle and the lane line is calculated by least squares, and judgment of the vehicle motion trend is not facilitated. According to the method, when the vehicle crosses a lane line on a certain side, the distance of the vehicle relative to the lane line in the frame is added with the distance of the frame, and the continuity of the distance of the vehicle relative to the lane line is ensured.
3) And taking the average value of the distance change rates relative to the left lane line and the right lane line as the final distance change rate relative to the lane line of the target object, and ensuring the stability of the change rate of the distance of the vehicle relative to the lane line. In practice, the distance perceived distance between the vehicle and a certain lane line on a certain side fluctuates, and in order to eliminate the influence of lane line perception and vehicle perception noise on an estimation result, the average value of the distance change rates relative to the left lane line and the right lane line is taken as the final distance change rate relative to the lane line of the target object. Fig. 3D is a graph of the rate of change of the distance between the vehicle and the left lane line as a function of the number of frames, provided by the embodiment of the disclosure. Fig. 3E is a graph of the rate of change of the distance between the vehicle and the right lane line as a function of the number of frames according to the embodiment of the disclosure. Fig. 3F is a graph of the rate of change of the distance between the vehicle and the lane line as a function of the number of frames according to the embodiment of the disclosure. As shown in fig. 3E, the distance change rate (from frame69360 to 69440) between the vehicle and the right lane line fluctuates greatly compared to the distance change rate (as shown in fig. 3D) between the vehicle and the left lane line, and the average value (as shown in fig. 3F) is obtained, so that the influence of the distance perception noise of the lane line on a certain side of the vehicle on the estimation result can be reduced.
Exemplary method
Fig. 4 is a flowchart illustrating a lane change identification method for a target object according to another exemplary embodiment of the present disclosure. The embodiment can be applied to an electronic device, as shown in fig. 4, and includes the following steps:
at step 401, at least one target object in front of a vehicle is determined.
In this embodiment, the target object may include an object such as a vehicle or an obstacle that travels ahead of the vehicle, and the specific type of the target object is not limited by the embodiments of the present disclosure.
Alternatively, an image of the front of the vehicle may be acquired by an image acquisition device such as a camera provided on the vehicle, and at least one target object in front of the vehicle may be determined by recognizing the image.
Each group of relative distances comprises the distance of a left lane line of a lane where the relative target object is located; or each group of relative distances comprises the distance of the right lane line of the lane where the target object is located; or each group of relative distances comprises the distance of the left lane line of the lane where the target object is located and the distance of the right lane line of the lane where the target object is located.
And step 403, determining the change rate of the target object relative to the lane line distance based on the n time points and the n groups of relative distances.
The n time points include a current time point and n-1 historical time points. Optionally, in the method for determining the change rate of the target object relative to the lane line distance in this embodiment, as shown in step 101 in the embodiment provided in fig. 1, a least square method is used to fit a linear relationship between the distance and the time change, and the slope of the fitted straight line is the change rate of the target object relative to the lane line distance.
In this embodiment, the change rate indicates a change of a distance between the target object and the lane line with time, and the relative distance indicates a distance between the target object and the lane line.
Alternatively, the lane change behavior of the target object in this embodiment may include, but is not limited to, a behavior of cutting into and cutting out of the own lane, and the like, and since the behavior of cutting into and cutting out of the own lane may affect the driving of the vehicle in this embodiment, it needs to be recognized, and a specific recognition process thereof may be shown in step 102 in the embodiment provided in fig. 1.
The lane change identification method for the target object provided by the above embodiment of the present disclosure determines at least one target object in front of a vehicle; for each target object of the at least one target object, respectively determining n groups of relative distances between the target object at n time points and a lane line; each group of the relative distances comprises a distance relative to a left lane line of a lane where the target object is located and/or a distance relative to a right lane line of the lane where the target object is located; determining a rate of change of the target object relative to the lane line distance based on the n time points and the n sets of relative distances; determining whether the target object makes a lane change based on the rate of change and the relative distance. According to the method and the device, the appropriate periodicity n is selected, the distance change rate of the target object relative to the lane line in the periodicity is used as the final distance change rate of the target object relative to the lane line, the stability of the change rate of the vehicle relative to the lane line distance is guaranteed, the accuracy of lane change behavior recognition is improved, and the rapidity and the accuracy of vehicle motion trend recognition are guaranteed.
As shown in fig. 5, based on the embodiment shown in fig. 4, step 403 may include the following steps:
In this embodiment, the linear relationship of the distance with respect to the time variation can be expressed by determining the optimal function matching by using the least square method for the relative distance between each time point and the corresponding time point. Among them, the least square method (also called the least squares method) is a mathematical optimization technique. It finds the best functional match of the data by minimizing the sum of the squares of the errors. Unknown data can be easily obtained by the least square method, and the sum of squares of errors between these obtained data and actual data is minimized.
Optionally, the slope of the straight line is determined based on a linear relationship, and the slope of the straight line is taken as the change rate of the target object relative to the lane line distance.
In the embodiment, a least square method is adopted to fit a linear relation of distance relative to time change, and the slope of a fitted straight line is the change rate of the distance of the obstacle vehicle relative to a lane line.
In this embodiment, a specific process of obtaining the change rate k may be implemented by referring to formula (1) in the embodiment provided in fig. 1, and after obtaining the value of the change rate, the driving direction of the target object relative to the lane line may be determined by combining a relationship between the value of the change rate and the driving direction of the target object relative to the lane line (for example, when the target object drives to the right relative to the lane line, k is negative, and when the target object drives to the left relative to the lane line, k is positive).
Fig. 6 is a flowchart illustrating a lane change recognition method for a target object according to still another exemplary embodiment of the present disclosure. The embodiment can be applied to an electronic device, as shown in fig. 6, and includes the following steps:
at step 401, at least one target object in front of a vehicle is determined.
Each group of relative distances comprises the distance of a left lane line of a lane where the relative target object is located; or each group of relative distances comprises the distance of the right lane line of the lane where the target object is located; or each group of relative distances comprises the distance of the left lane line of the lane where the target object is located and the distance of the right lane line of the lane where the target object is located.
And step 403, determining the change rate of the target object relative to the lane line distance based on the n time points and the n groups of relative distances.
Wherein the n time points include a current time point and n-1 historical time points.
And step 604, determining that the target object changes the lane in response to the fact that the change rate and the relative distance corresponding to the current time point meet the preset condition.
In this embodiment, the lane change behavior of the target object is identified by combining the change rate and the relative distance, and whether lane change is specifically performed or not is determined, in this embodiment, the lane change identification is limited by setting a preset condition, and the preset condition may be set according to a specific situation, and this embodiment specifically limits, for example, optionally, step 604 may include: and determining that the target object changes the lane in response to the fact that the absolute value of the change rate is larger than a first preset change rate and the relative distance corresponding to the current time point is smaller than a preset value.
In the embodiment, the change rate and the relative distance are limited by setting a first preset change rate and a preset value, and the behavior of a target object cutting into or out of the lane is recognized; for example, if the target object is closer to the lane line when the distance between the target object and the lane line is less than a preset value (e.g., D), and the absolute value of the change rate of the target object relative to the lane line is greater than a first preset change rate (e.g., V), the target object has a greater yaw rate and a lane change tendency, and it is determined that the target object is cut out of the lane or cut into the lane.
Fig. 7 is a flowchart illustrating a lane change recognition method for a target object according to still another exemplary embodiment of the present disclosure. The embodiment can be applied to an electronic device, as shown in fig. 7, and includes the following steps:
at step 401, at least one target object in front of a vehicle is determined.
Each group of relative distances comprises the distance of a left lane line of a lane where the relative target object is located; or each group of relative distances comprises the distance of the right lane line of the lane where the target object is located; or each group of relative distances comprises the distance of the left lane line of the lane where the target object is located and the distance of the right lane line of the lane where the target object is located.
And step 403, determining the change rate of the target object relative to the lane line distance based on the n time points and the n groups of relative distances.
Optionally, the lane in which the target object is located may include the following situations: and determining that the target object is in the left lane, or determining that the target object is in the right lane, or determining that the target object is in the lane where the vehicle is located.
Optionally, step 705 may include: responding to the fact that the target object is located on the right lane, and determining whether the target object cuts into the lane where the vehicle is located according to whether the distance between the target object and the left lane line in the right lane is smaller than a preset value and whether the absolute value of the change rate is larger than a first preset change rate;
responding to the situation that the target object is in the left lane, and determining whether the target object cuts into the lane where the vehicle is located according to whether the distance between the target object and the right lane line in the left lane is smaller than a preset value and whether the absolute value of the change rate is smaller than a second preset change rate;
and responding to the fact that the target object is located in the lane where the vehicle is located, and determining whether the target object is cut out of the lane where the vehicle is located according to whether the distance between the target object and the right lane line in the left lane is smaller than a preset value and whether the absolute value of the change rate is larger than a first preset change rate or not, or whether the distance between the target object and the right lane line is smaller than a preset value and whether the change rate is smaller than a second preset change rate or not.
In the embodiment, for the behavior that the cutting-in of the own lane is necessarily performed from other lanes (the left lane or the right lane), and the cutting-out of the own lane is performed, the corresponding target object is necessarily driven on the own lane before the cutting-out is performed; therefore, in order to specifically determine whether the target object is cut-in (which may affect the driving of the vehicle in the lane) or cut-out, this embodiment combines the lane information where the target object is located, compares whether the distance from the target object to the left or right lane line is smaller than a preset value D, and then compares whether the distance from the target object to the lane line is greater than a first preset change rate V or smaller than a second preset change rate-V according to the change rate (the direction is positive left or negative right), so as to obtain the judgment result of the behavior of cutting-in and cutting-out the target object from the lane.
Fig. 8 is a flowchart illustrating a lane change recognition method for a target object according to still another exemplary embodiment of the present disclosure. The embodiment can be applied to an electronic device, as shown in fig. 8, and includes the following steps:
And step 803, determining the change rate of the target object relative to the lane line distance by using a least square method.
Step 8053, judging whether the distance between the target object and the left lane line is smaller than a preset value; if so, go to step 8063, otherwise, go to step 8073.
8103, judging whether the change rate is greater than a first preset change rate, and if so, determining that the target object does not cut out the lane of the vehicle; otherwise, ending.
The present embodiment is a detailed example of the target object lane change recognition method provided by the present disclosure, in which each different case is explained in detail.
Any one of the target object lane change identification methods provided by the embodiments of the present disclosure may be performed by any suitable device having data processing capabilities, including but not limited to: terminal equipment, a server and the like. Alternatively, any one of the target object lane change identification methods provided by the embodiments of the present disclosure may be executed by a processor, for example, the processor may execute any one of the target object lane change identification methods mentioned in the embodiments of the present disclosure by calling a corresponding instruction stored in a memory. And will not be described in detail below.
Exemplary devices
Fig. 9 is a schematic structural diagram of a target object lane change recognition apparatus according to an exemplary embodiment of the present disclosure. As shown in fig. 9, the apparatus of the present embodiment includes:
a target determination module 91 for determining at least one target object in front of the vehicle.
A relative distance determining module 92, configured to determine, for each of the at least one target object determined by the target determining module, n sets of relative distances between the target object at n time points and the lane line, respectively.
And each group of relative distances comprises the distance of the left lane line of the lane where the target object is located and/or the distance of the right lane line of the lane where the target object is located.
And a change rate determining module 93, configured to determine a change rate of the target object with respect to the lane line distance over time based on the n time points and the n groups of relative distances determined by the relative distance determining module.
And a lane change identification module 94 for determining whether the target object has made a lane change based on the change rate determined by the change rate determination module and the relative distance determined by the relative distance determination module.
The lane change recognition device for the target object provided by the above embodiment of the present disclosure determines at least one target object in front of the vehicle; for each target object of the at least one target object, respectively determining n groups of relative distances between the target object at n time points and a lane line; each group of the relative distances comprises a distance relative to a left lane line of a lane where the target object is located and/or a distance relative to a right lane line of the lane where the target object is located; determining a rate of change of the target object relative to the lane line distance based on the n time points and the n sets of relative distances; determining whether the target object makes a lane change based on the rate of change and the relative distance. According to the method and the device, the appropriate periodicity n is selected, the distance change rate of the target object relative to the lane line in the periodicity is used as the final distance change rate of the target object relative to the lane line, the stability of the change rate of the vehicle relative to the lane line distance is guaranteed, the accuracy of lane change behavior recognition is improved, and the rapidity and the accuracy of vehicle motion trend recognition are guaranteed.
Fig. 10 is a schematic structural diagram of a target object lane change recognition apparatus according to another exemplary embodiment of the present disclosure. As shown in fig. 10, the apparatus of the present embodiment includes:
a rate of change determination module 93 comprising:
a linear relationship determining unit 931 configured to determine a linear relationship of the distance with respect to the time change based on the n time points and the n sets of relative distances by using a least square method.
A distance change rate unit 932 for determining a rate of change of the target object with respect to the lane line distance based on the linear relationship.
Optionally, the distance change rate unit 932 is specifically configured to determine a slope of the straight line based on the linear relationship, and use the slope of the straight line as a change rate of the target object relative to the lane line distance.
In some alternative embodiments, the n time points include a current time point and n-1 historical time points; and the lane change identification module 94 is configured to determine that the target object changes lanes in response to that the change rate and the relative distance corresponding to the current time point satisfy a preset condition.
The lane change identification module 94 is specifically configured to determine that the target object changes lanes in response to that the absolute value of the change rate is greater than the first preset change rate and that the relative distance corresponding to the current time point is smaller than a preset value.
The apparatus provided in this embodiment further includes:
and the lane determining module 11 is used for determining the lane where the target object is located.
The lane change recognition module 94 is specifically configured to determine, according to the lane where the target object is located, whether the target object cuts in or cuts out the lane where the vehicle is located according to the fact that the absolute value of the change rate is greater than a first preset change rate and the relative distance corresponding to the current time point is smaller than a preset value.
In some optional embodiments, the lane determining module 11 is specifically configured to determine that the target object is in a left lane, or determine that the target object is in a right lane, or determine that the target object is in a lane where the vehicle is located;
the lane change recognition module 94 is configured to, in response to the target object being in the right lane, determine whether the target object cuts into the lane where the vehicle is located according to whether the distance between the target object and the left lane line in the right lane is smaller than a preset value and whether the absolute value of the change rate is greater than a first preset change rate; responding to the situation that the target object is in the left lane, and determining whether the target object cuts into the lane where the vehicle is located according to whether the distance between the target object and the right lane line in the left lane is smaller than a preset value and whether the absolute value of the change rate is smaller than a second preset change rate; and responding to the fact that the target object is located in the lane where the vehicle is located, and determining whether the target object is cut out of the lane where the vehicle is located according to whether the distance between the target object and the right lane line in the left lane is smaller than a preset value and whether the absolute value of the change rate is larger than a first preset change rate or not, or whether the distance between the target object and the right lane line is smaller than a preset value and whether the change rate is smaller than a second preset change rate or not.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present disclosure is described with reference to fig. 11. The electronic device may be either or both of the first device 100 and the second device 200, or a stand-alone device separate from them that may communicate with the first device and the second device to receive the collected input signals therefrom.
FIG. 11 illustrates a block diagram of an electronic device in accordance with an embodiment of the disclosure.
As shown in fig. 11, electronic device 110 includes one or more processors 111 and memory 112.
In one example, the electronic device 110 may further include: an input device 113 and an output device 114, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
For example, when the electronic device is the first device 100 or the second device 200, the input device 113 may be a microphone or a microphone array as described above for capturing an input signal of a sound source. When the electronic device is a stand-alone device, the input means 113 may be a communication network connector for receiving the acquired input signals from the first device 100 and the second device 200.
The input device 113 may also include, for example, a keyboard, a mouse, and the like.
The output device 114 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 114 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, among others.
Of course, for simplicity, only some of the components of the electronic device 110 relevant to the present disclosure are shown in fig. 11, omitting components such as buses, input/output interfaces, and the like. In addition, electronic device 110 may include any other suitable components, depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present disclosure may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the target object lane change identification method according to various embodiments of the present disclosure described in the "exemplary methods" section of this specification above.
The computer program product may write program code for carrying out operations for embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present disclosure may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform the steps in the target object lane change identification method according to various embodiments of the present disclosure described in the "exemplary methods" section above in this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The block diagrams of devices, apparatuses, systems referred to in this disclosure are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
It is also noted that in the devices, apparatuses, and methods of the present disclosure, each component or step can be decomposed and/or recombined. These decompositions and/or recombinations are to be considered equivalents of the present disclosure.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the disclosure to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (10)
1. A lane change identification method for a target object comprises the following steps:
determining at least one target object in front of the vehicle;
for each target object of the at least one target object, respectively determining n groups of relative distances between the target object at n time points and a lane line; each group of the relative distances comprises a distance relative to a left lane line of a lane where the target object is located and/or a distance relative to a right lane line of the lane where the target object is located;
determining a rate of change of the target object relative to the lane line distance based on the n time points and the n sets of relative distances;
determining whether the target object makes a lane change based on the rate of change and the relative distance.
2. The method of claim 1, wherein said determining a rate of change of said target object relative lane-line distance based on said n time points and said n sets of relative distances comprises:
determining a linear relation of distance relative time change based on the n time points and the n groups of relative distances by using a least square method;
and determining the change rate of the target object relative to the lane line distance based on the linear relation.
3. The method of claim 2, wherein said determining a rate of change of said target object relative to lane-line distance based on said linear relationship comprises:
and determining the slope of a straight line based on the linear relation, and taking the slope of the straight line as the change rate of the target object relative to the distance of the lane line.
4. The method according to any one of claims 1-3, wherein the n time points include a current time point and n-1 historical time points;
the determining whether the target object makes a lane change based on the rate of change and the relative distance includes:
and determining that the target object changes the lane in response to the fact that the change rate and the relative distance corresponding to the current time point meet a preset condition.
5. The method of claim 4, wherein the determining that the target object makes a lane change in response to the rate of change and the relative distance corresponding to the current time point satisfying a preset condition comprises:
and determining that the target object changes the lane in response to the fact that the absolute value of the change rate is larger than a first preset change rate and the relative distance corresponding to the current time point is smaller than a preset value.
6. The method of claim 5, wherein, in response to the absolute value of the change rate being greater than a first preset change rate and the relative distance corresponding to the current time point being less than a preset value, before determining that the target object makes a lane change, further comprising:
determining a lane where the target object is located;
when the absolute value of the change rate is larger than a first preset change rate and the relative distance corresponding to the current time point is smaller than a preset value, determining that the target object changes the lane, including:
and determining that the target object is switched into or out of the lane in which the vehicle is positioned according to the lane in which the target object is positioned when the absolute value of the change rate is larger than a first preset change rate and the relative distance corresponding to the current time point is smaller than a preset value.
7. The method of claim 6, wherein the determining the lane in which the target object is located comprises:
determining that the target object is in a left lane, or determining that the target object is in a right lane, or determining that the target object is in a lane where the vehicle is located;
the determining that the target object switches in or out of the lane where the vehicle is located according to the lane where the target object is located comprises:
in response to the target object being in a right lane, determining whether the target object cuts into the lane in which the vehicle is located according to whether the distance of the target object relative to a left lane line in the right lane is smaller than a preset value and whether the absolute value of the change rate is larger than a first preset change rate;
in response to the target object being in a left lane, determining whether the target object cuts into the lane in which the vehicle is located according to whether the distance of the target object relative to a right lane line in the left lane is smaller than a preset value and whether the absolute value of the change rate is smaller than a second preset change rate;
and responding to the fact that the target object is located in the lane where the vehicle is located, and determining whether the target object is cut out of the lane where the vehicle is located according to whether the distance between the target object and a right lane line in the left lane is smaller than a preset value and whether the absolute value of the change rate is larger than a first preset change rate or not, or whether the distance between the target object and the right lane line is smaller than a preset value and whether the absolute value of the change rate is smaller than a second preset change rate or not.
8. An object lane change recognition apparatus comprising:
a target determination module for determining at least one target object in front of the vehicle;
a relative distance determination module, configured to determine, for each of the at least one target object determined by the target determination module, n sets of relative distances between the n time points and a lane line of the target object, respectively; each group of the relative distances comprises a distance relative to a left lane line of a lane where the target object is located and/or a distance relative to a right lane line of the lane where the target object is located;
a change rate determination module, configured to determine a change rate of the target object with respect to the lane line distance over time based on the n time points and the n sets of relative distances determined by the relative distance determination module;
and the lane change identification module is used for determining whether the target object changes lanes or not based on the change rate determined by the change rate determination module and the relative distance determined by the relative distance determination module.
9. A computer-readable storage medium storing a computer program for executing the target object lane change identification method according to any one of claims 1 to 7.
10. An electronic device, the electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for reading the executable instructions from the memory and executing the instructions to realize the target object lane change identification method of any one of the claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911107876.5A CN112800812A (en) | 2019-11-13 | 2019-11-13 | Target object lane change identification method and device, readable storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911107876.5A CN112800812A (en) | 2019-11-13 | 2019-11-13 | Target object lane change identification method and device, readable storage medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112800812A true CN112800812A (en) | 2021-05-14 |
Family
ID=75803362
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911107876.5A Pending CN112800812A (en) | 2019-11-13 | 2019-11-13 | Target object lane change identification method and device, readable storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112800812A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113044042A (en) * | 2021-06-01 | 2021-06-29 | 禾多科技（北京）有限公司 | Vehicle predicted lane change image display method and device, electronic equipment and readable medium |
| CN113569800A (en) * | 2021-08-09 | 2021-10-29 | 北京地平线机器人技术研发有限公司 | Lane recognition and verification method and device, readable storage medium and electronic equipment |
| CN116110216A (en) * | 2022-10-21 | 2023-05-12 | 中国第一汽车股份有限公司 | Vehicle line crossing time determining method and device, storage medium and electronic device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101089917A (en) * | 2007-06-01 | 2007-12-19 | 清华大学 | Quick identification method for object vehicle lane changing |
| CN104290753A (en) * | 2014-09-29 | 2015-01-21 | 长安大学 | Tracking and predicting device of motion state of front vehicle and predicating method thereof |
| DE102016106983A1 (en) * | 2016-04-15 | 2017-10-19 | Valeo Schalter Und Sensoren Gmbh | Method for detecting a possible lane change maneuver of a target vehicle, control device, driver assistance system and motor vehicle |
| CN107415945A (en) * | 2016-05-24 | 2017-12-01 | 通用汽车环球科技运作有限责任公司 | For assessing the automotive drive system and its application method of track lane-change |
| DE102016220228A1 (en) * | 2016-10-17 | 2018-04-19 | Bayerische Motoren Werke Aktiengesellschaft | A method, driver assistance system, and vehicle comprising the driver assistance system for adjusting a vehicle distance between an ego vehicle and a first, preceding vehicle in response to a second, preceding vehicle |
| CN110406532A (en) * | 2019-06-21 | 2019-11-05 | 重庆长安汽车股份有限公司 | A kind of method, system and the automobile of the possible lane change of identification target vehicle |
-
2019
- 2019-11-13 CN CN201911107876.5A patent/CN112800812A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101089917A (en) * | 2007-06-01 | 2007-12-19 | 清华大学 | Quick identification method for object vehicle lane changing |
| CN104290753A (en) * | 2014-09-29 | 2015-01-21 | 长安大学 | Tracking and predicting device of motion state of front vehicle and predicating method thereof |
| DE102016106983A1 (en) * | 2016-04-15 | 2017-10-19 | Valeo Schalter Und Sensoren Gmbh | Method for detecting a possible lane change maneuver of a target vehicle, control device, driver assistance system and motor vehicle |
| CN107415945A (en) * | 2016-05-24 | 2017-12-01 | 通用汽车环球科技运作有限责任公司 | For assessing the automotive drive system and its application method of track lane-change |
| DE102016220228A1 (en) * | 2016-10-17 | 2018-04-19 | Bayerische Motoren Werke Aktiengesellschaft | A method, driver assistance system, and vehicle comprising the driver assistance system for adjusting a vehicle distance between an ego vehicle and a first, preceding vehicle in response to a second, preceding vehicle |
| CN110406532A (en) * | 2019-06-21 | 2019-11-05 | 重庆长安汽车股份有限公司 | A kind of method, system and the automobile of the possible lane change of identification target vehicle |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113044042A (en) * | 2021-06-01 | 2021-06-29 | 禾多科技（北京）有限公司 | Vehicle predicted lane change image display method and device, electronic equipment and readable medium |
| CN113569800A (en) * | 2021-08-09 | 2021-10-29 | 北京地平线机器人技术研发有限公司 | Lane recognition and verification method and device, readable storage medium and electronic equipment |
| CN116110216A (en) * | 2022-10-21 | 2023-05-12 | 中国第一汽车股份有限公司 | Vehicle line crossing time determining method and device, storage medium and electronic device |
| CN116110216B (en) * | 2022-10-21 | 2024-04-12 | 中国第一汽车股份有限公司 | Vehicle line crossing time determining method and device, storage medium and electronic device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112800812A (en) | Target object lane change identification method and device, readable storage medium and electronic equipment | |
| US11977675B2 (en) | Primary preview region and gaze based driver distraction detection | |
| US9063834B2 (en) | Filtering method and filter device for sensor data | |
| US11654899B2 (en) | Method and apparatus for avoidance control of vehicle, electronic device and storage medium | |
| US10627805B2 (en) | Method, device, and terminal device for servo movement smoothing | |
| US11094080B2 (en) | Method and device for determining whether a hand cooperates with a manual steering element of a vehicle | |
| CN112818778B (en) | Lane line fitting method, lane line fitting device, lane line fitting medium and electronic equipment | |
| CN113022580A (en) | Trajectory prediction method, trajectory prediction device, storage medium and electronic equipment | |
| US20210055737A1 (en) | Method of pedestrian activity recognition using limited data and meta-learning | |
| CN114170826B (en) | Automatic driving control method and device, electronic device and storage medium | |
| WO2018103024A1 (en) | Intelligent guidance method and apparatus for visually handicapped person | |
| CN112991684A (en) | Driving early warning method and device | |
| CN112406884B (en) | Vehicle driving state recognition method and device, storage medium and electronic equipment | |
| US11080562B1 (en) | Key point recognition with uncertainty measurement | |
| KR20180007511A (en) | Apparatus and method of considering driver's characteristics | |
| Rangesh et al. | Predicting take-over time for autonomous driving with real-world data: Robust data augmentation, models, and evaluation | |
| WO2020115571A1 (en) | A system and method for video compression using key frames and sums of absolute differences | |
| de Alcantara et al. | Action identification using a descriptor with autonomous fragments in a multilevel prediction scheme | |
| WO2023093306A1 (en) | Vehicle lane change control method and apparatus, electronic device, and storage medium | |
| JP6493154B2 (en) | Information providing apparatus and information providing method | |
| EP4047566A1 (en) | Prevention of low-speed sideswipe collisions with non-moving objects | |
| CN112308923A (en) | Lane line-based camera pose adjusting method and device, storage medium and equipment | |
| CN114743174A (en) | Determination method and device for observed lane line, electronic equipment and storage medium | |
| CN112184821B (en) | Method and device for adjusting roll angle of camera, storage medium and electronic equipment | |
| CN110765970B (en) | Method and device for determining nearest obstacle, storage medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |