CN115352444A

CN115352444A - Method, device and equipment for controlling driving behavior of vehicle and storage medium

Info

Publication number: CN115352444A
Application number: CN202210999599.9A
Authority: CN
Inventors: 文成; 车文耀; 李佳择; 王建军; 刘少耿; 施皓
Original assignee: Apollo Zhilian Beijing Technology Co Ltd; Apollo Zhixing Technology Guangzhou Co Ltd
Current assignee: Apollo Zhilian Beijing Technology Co Ltd; Apollo Zhixing Technology Guangzhou Co Ltd
Priority date: 2022-08-19
Filing date: 2022-08-19
Publication date: 2022-11-18

Abstract

The disclosure provides a method, a device, equipment and a storage medium for controlling vehicle driving behaviors, and relates to the fields of automatic driving, intelligent transportation, vehicle and road cooperation and the like. The specific implementation scheme is as follows: and acquiring the road emergency information. And determining the cellular data of the emergency road section by using historical road data related to the emergency time information and the emergency position information. And performing road simulation on the emergency road section by using the cellular data to obtain simulated road simulation data. The driving behavior of a first vehicle traveling on the emergency road segment is controlled based on the simulated road simulation data. According to the method and the device, the road condition of the emergency road section can be accurately simulated through the cellular data of the emergency road section. Therefore, planning is carried out based on the result obtained by simulation, congestion or secondary accidents of the road can be effectively avoided, and the planning effect of the accident road section is improved.

Description

Method, device and equipment for controlling driving behavior of vehicle and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, particularly to the field of intelligent transportation, and in general, to a method, an apparatus, a device, and a storage medium for controlling a driving behavior of a vehicle.

Background

With the development of society, vehicles are gradually popularized in people's lives. People often choose to ride or drive a car in a vehicle while traveling. With the continuous improvement of urban road infrastructure, vehicles traveling on urban roads are getting older and older.

For some high speed road sections or viaducts, loops, streets near residential areas, etc., the traffic flow is often large. When traffic accidents occur on the road sections, local traffic jam is usually caused, and if the time period with large traffic flow is long, a larger area of road jam is caused along with the passage of time.

Disclosure of Invention

The present disclosure provides a method, apparatus, device, and storage medium for controlling driving behavior of a vehicle.

According to a first aspect of the present disclosure, there is provided a method of controlling driving behaviour of a vehicle, the method comprising: and acquiring road emergency information. The road emergency information comprises emergency time information and emergency position information. And determining the cellular data of the emergency road section by using the historical road data related to the emergency time information and the emergency position information. The emergency road section comprises a position corresponding to the emergency position information, and the cellular data is used for describing the vehicle driving condition of the corresponding road section. And performing road simulation on the emergency road section by using the cellular data to obtain simulated road simulation data. The driving behavior of a first vehicle traveling on the emergency road segment is controlled based on the simulated road simulation data. According to the method and the device, the road condition of the emergency road section can be accurately simulated through the cellular data of the emergency road section. Therefore, planning is carried out based on the result obtained by simulation, congestion or secondary accidents of the road can be effectively avoided, and the planning effect of the accident road section is improved.

According to a second aspect of the present disclosure, there is provided an apparatus for controlling driving behavior of a vehicle, the apparatus comprising: the acquisition module is used for acquiring road emergency information, and the road emergency information comprises emergency time information and emergency position information; the determining module is used for determining cellular data of an emergency road section by utilizing historical road data related to the emergency time information and the emergency position information, wherein the emergency road section comprises a position corresponding to the emergency position information, and the cellular data is used for describing the vehicle driving condition of the corresponding road section; the simulation module is used for performing road simulation on the emergency road section by using the cellular data to obtain simulated road simulation data; and the control module is used for controlling the driving behavior of the first vehicle running on the emergency road section based on the simulated road simulation data. According to the method and the device, the road condition of the emergency road section can be accurately simulated through the cellular data of the emergency road section. Therefore, planning is carried out based on the result obtained by simulation, congestion or secondary accidents of the road can be effectively avoided, and the planning effect of the accident road section is improved.

According to a third aspect of the present disclosure, there is provided an apparatus for controlling driving behavior of a vehicle, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of any one of the first aspect.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of the first or second aspects above.

According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of any one of the first or second aspects described above.

According to the method, the device, the equipment and the storage medium for controlling the driving behavior of the vehicle, provided by the disclosure, the road condition of the emergency road section can be accurately simulated through the cellular data of the emergency road section. Therefore, planning is carried out based on the result obtained by simulation, congestion or secondary accidents of the road can be effectively avoided, and the planning effect of the accident road section is improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a schematic diagram of an application scenario of an embodiment of the present disclosure;

FIG. 2 is a flow chart of a method of controlling vehicle driving behavior in accordance with an embodiment of the present disclosure;

FIG. 3 is a flow chart of another method of controlling vehicle driving behavior in accordance with an embodiment of the present disclosure;

FIG. 4 is a schematic illustration of a vehicle emergency brake according to an embodiment of the present disclosure;

FIG. 5 is a flow chart of a method of determining lane queue length data according to an embodiment of the present disclosure;

FIG. 6 is a flow chart of a method for determining road network saturation data according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram of a lane change probability for a vehicle according to an embodiment of the present disclosure;

FIG. 8 is another schematic representation of a lane change probability for a vehicle in accordance with an embodiment of the present disclosure;

FIG. 9 is a schematic representation of a further vehicle lane change probability in accordance with an embodiment of the present disclosure;

FIG. 10 is a schematic illustration of a further vehicle lane change probability according to an embodiment of the disclosure;

FIG. 11 is another schematic representation of a lane change probability for a vehicle in accordance with an embodiment of the present disclosure;

FIG. 12 is a schematic view of a lane change condition of an embodiment of the present disclosure;

FIG. 13 is a flow chart of yet another method of controlling vehicle driving behavior in accordance with an embodiment of the present disclosure;

FIG. 14 is a schematic view of a road simulation of an embodiment of the present disclosure;

FIG. 15 is a schematic flow chart of a following rule of an embodiment of the present disclosure;

FIG. 16 is a schematic illustration of an apparatus for controlling vehicle driving behavior in accordance with an embodiment of the disclosure;

fig. 17 is a schematic diagram of an apparatus for controlling driving behavior of a vehicle according to an embodiment of the disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The present disclosure is mainly applied to a scene, for example, a scene in which a vehicle travels on a road. As illustrated, for example, in fig. 1, fig. 1 illustrates a scenario 100, wherein at least one vehicle 101 is contained within the scenario 100. It is understood that a plurality of vehicles 101 are shown in fig. 1. The vehicle travels within the lane 102. And based on the driver operating the vehicle 101, the vehicle 101 may be allowed to accelerate, decelerate, uniform, or change the lane 102 of travel within the lane 102.

Assuming that the scene 100 is located on a highway section, traffic safety is important for both traffic managers and travelers in intelligent high-speed construction and management. It is understood that the traveler may include a driver driving the vehicle 101, and a passenger riding in the vehicle 101. A traffic manager may be understood as a department, manager or management platform that maintains, regulates and supervises traffic segments.

Particularly, for the emergency on the road, the traffic operation state and the accident influence range of the accident site within a period of several minutes or a certain time length in the future are predicted, so that a traffic manager can conveniently take appropriate traffic control measures and guidance measures in time to correspondingly shunt the congested road sections, and the occurrence probability of traffic congestion and secondary accidents is effectively reduced.

In some related technologies, an emergency event is used for intelligent monitoring. For example, accident information is acquired by way of road monitoring and video recognition, and then information support is provided according to real-time road conditions or video information. The method for predicting the road condition by the scheme is used for performing macroscopic traffic flow prediction only according to historical data, so that the prediction accuracy and the visualization level are both poor.

Therefore, the above method adopts the accident data provided by Artificial Intelligence (AI) video identification, and the influence range of the accident cannot be predicted and evaluated. The prediction evaluation performed by adopting a macroscopic traffic flow mode cannot truly and visually restore the road condition, and the prediction accuracy is not enough. And, vehicle information data in the high speed of actual wisdom is discerned through bayonet socket and road side equipment at present, and the accuracy and the integrity of data are all far away not enough.

Therefore, the present disclosure provides a method, an apparatus, a device and a storage medium for controlling driving behaviors of a vehicle, which can accurately perform simulation on road conditions of an emergency road section through cellular data of the emergency road section. Therefore, planning is carried out based on the result obtained by simulation, congestion or secondary accidents of the road can be effectively avoided, and the planning effect on the accident road section is improved.

The present disclosure will be described in detail below with reference to the accompanying drawings.

FIG. 2 is a flow chart of a method of controlling vehicle driving behavior in accordance with an embodiment of the disclosure.

Based on the scenario illustrated in fig. 1, the present disclosure also provides a method of controlling vehicle driving behavior. The method may be applied to a terminal device or a network device, which may be a server or a server cluster in some examples. Of course, in other examples, the terminal device may include, but is not limited to, any terminal device or portable terminal device such as a mobile phone, a wearable device, a tablet computer, a handheld computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a Personal Digital Assistant (PDA), a laptop computer (laptop), a mobile computer, an Augmented Reality (AR) device, a Virtual Reality (VR) device, an Artificial Intelligence (AI) device, and/or a vehicle mounted device, and the disclosure is not limited thereto.

It is understood that the terminal device and the network device may be collectively referred to as a device in this disclosure.

The method to which the present disclosure relates may comprise the steps of:

s201, road emergency information is acquired.

In some examples, the device may obtain road emergency information. The road emergency information may include emergency time information and emergency position information. It is understood that the emergency time information is used to describe the time when the emergency occurs, and the emergency time position information may describe the position where the emergency occurs, for example, which position of which road is located.

It is to be understood that the road emergency information may include any information describing a road emergency.

And S202, determining cellular data of the emergency road section by using historical road data related to the emergency time information and the emergency position information.

In some examples, the device may acquire historical road data related to the emergency time information and the emergency position information according to the road emergency acquired in S201. And then determining the cellular data of the emergency road section based on the acquired historical road data. The emergency road section comprises a position corresponding to the emergency position information. It is understood that the emergency section means a section where an emergency occurs, and the section should include at least a location where the emergency occurs. The cellular data may be used to describe the vehicle driving situation of the corresponding road segment.

In some examples, a cell may represent the smallest road element. For example, the relationship of the road basic data hierarchy may be, from large to small: road → road section → lane → cell. It is understood that the range of the road is the largest, and a road may be composed of a plurality of road segments, each road segment may contain a plurality of lanes, and each lane may be composed of a plurality of cells. It is to be understood that the above-mentioned layer-by-layer division may be regarded as performing a road network segmentation process on the road. In some examples, each cellular data may contain vehicle travel information within a corresponding length. For example, the unit cells may be arranged in a section having a length of 0.1 meter (m).

In some examples, cellular data for a road segment at any time in the future may be predicted using a grey prediction model (GM) (1, 1). For example, a certain road segment may be predicted from historical road data. It is understood that the historical road data may be made up of cellular history data for different locations of the historical period. The predicted sequence X can be constructed in the manner of equation 1 ⁽⁰⁾ 。

X ⁽⁰⁾ ＝(x ⁽⁰⁾ (1)，x ⁽⁰⁾ (2)，......，x ⁽⁰⁾ (n)) \ 8230; \ 8230; (formula 1)

Wherein x is ⁽⁰⁾ (n) represents nth historical road data, and n is any positive integer. It will be appreciated that different x ⁽⁰⁾ (n) may represent historical road data at different times but at the same location.

For example, the prediction sequence X ⁽⁰⁾ CollectedThe used historical road data can be statistically obtained by taking the day as a unit. The data can be divided according to smaller time granularity for each day, so that historical road data of different time periods of the day can be obtained. In some examples, the time granularity may be set to 15 minutes (min). Taking the unit of the time information of the emergency as a day as an example, the historical road data may be data of different days added with corresponding labels for distinguishing the historical road data corresponding to the different days. For example, the tags may be divided into weekdays, holidays, weekends, and the like. Wherein the weekdays can include monday through friday. Weekends may include saturday and sunday.

In some examples, when the historical road data is acquired, abnormal values in the historical road data can be eliminated. The specific manner can be implemented by referring to the existing manner, and the details of the disclosure are not repeated.

In some examples, if the acquired historical road data corresponds to a holiday, when the data of the corresponding holiday is smaller than a preset minimum data amount threshold, the data of the weekend in sequence closest to the current date may be used for completion. It will be appreciated that to ensure that the sequence of numbers X is predicted ⁽⁰⁾ The accuracy of (2) is usually preset to be the amount of historical road data required. In some examples, the minimum data amount threshold may be set to 4 bars. It will be appreciated that each piece of data may correspond to one day of historical road data.

In other examples, assume that the prediction sequence X ⁽⁰⁾ The date to be predicted is the weekend shift compensation situation, the weekend shift compensation can be regarded as one of the working days, and the historical road data corresponding to the working day nearest to the weekend shift compensation can be selected according to the time to predict the weekend shift compensation.

For example, if the sequence X is predicted ⁽⁰⁾ The date needing prediction is Saturday 13. If the sequence X is predicted ⁽⁰⁾ The date needing prediction is Monday 13. If the sequence X is predicted ⁽⁰⁾ The date needing to be predicted is holidays 13-00-13, and historical road data of the history corresponding to holidays 13. If it isPrediction sequence X ⁽⁰⁾ The date needing prediction is weekend shift 13-00-13, and the historical road data of the historical working day 13.

In some examples, the prediction sequence X shown in equation 1 may be compared to the prediction sequence X ⁽⁰⁾ Performing one-time accumulation to obtain a new sequence X ⁽¹⁾ The value of the error, for example as shown in equation 2,

X ⁽¹⁾ ＝(x ⁽¹⁾ (1)，x ⁽¹⁾ (2)，......，x ⁽¹⁾ (n)) \ 8230; \ 8230; (formula 2)

Wherein x is ⁽¹⁾ (n) can be calculated by formula 3.

Thereafter, X can be utilized ⁽¹⁾ Generating a sequence Z ⁽¹⁾ The number of bits, for example as shown in equation 4,

Z ⁽¹⁾ ＝(z ⁽¹⁾ (2)，z ⁽¹⁾ (3)，......，z ⁽¹⁾ (n)) \ 8230; \ 8230; formula 4

Wherein z is ⁽¹⁾ (n) can be calculated by equation 5.

It can be understood that k in equation 5 takes the value 1,2, \8230;, n-1.

Thereafter, matrix Y and matrix B may be constructed. Such as shown in equations 6 and 7.

Then, the parameters a, u may be output based on the two matrices shown in equation 6 and equation 7. For example, this can be obtained by equation 8.

Wherein, B ^T Denoted as the transposed matrix of B.

Then, the accumulated value x can be predicted by formula 9 ⁽¹⁾ (k + 1), i.e.

Finally, the predicted value x can be calculated by formula 10 ⁽⁰⁾ (k+1)

x ⁽⁰⁾ (k+1)＝x ⁽¹⁾ (k+1)-x ⁽¹⁾ (k) 8230the formula 10

It can be understood that the cellular data of the corresponding road section at any time in the future can be predicted through the formula 1 to the formula 10 involved in the GM (1, 1) model. Of course, the specific implementation process may be implemented by referring to the existing manner, and details of the present disclosure are not repeated.

And S203, performing road simulation on the emergency road section by using the cellular data to obtain simulated road simulation data.

In some examples, the device may perform a road simulation on the road segment between corresponding bursts based on the cellular data determined in S202 described above, thereby obtaining simulated road simulation data.

In some examples, the state of the cell data corresponding to the corresponding accident location, time, start and stop lanes may be modified to a state in which the vehicle is prohibited from entering based on the road emergency information when performing the road simulation. For example, 0 may be used to indicate entry is prohibited and 1 may be used to indicate entry is allowed. Of course, the above description is only an exemplary description, and any modification may be made according to the actual situation, and the disclosure is not limited.

It will be appreciated that the above operation may indicate that an emergency has occurred at a corresponding location in the road network.

And S204, controlling the driving behavior of the first vehicle running on the emergency road section based on the simulated road simulation data.

In some examples, the device may utilize the simulated road simulation data obtained in S203 to perform corresponding road planning and/or vehicle driving behavior planning. And controlling the driving behavior of the corresponding vehicle traveling on the emergency road segment based on the road plan and/or the vehicle driving behavior plan. Such as a first vehicle. The first vehicle may be any vehicle traveling on the emergency road segment.

It can be understood that the situation of congestion on the road section of the emergency can be effectively avoided by controlling the driving behavior of the first vehicle, and secondary accidents can be further avoided to a certain extent.

According to the method and the device, the road condition of the emergency road section can be accurately simulated through the cellular data of the emergency road section. Therefore, planning is carried out based on the result obtained by simulation, congestion or secondary accidents of the road can be effectively avoided, and the planning effect on the accident road section is improved.

In some embodiments, as shown in FIG. 3, FIG. 3 is a flow chart of another method of controlling vehicle driving behavior according to embodiments of the present disclosure. Wherein the simulated road simulation data may include vehicle-following data. In S203, performing road simulation on the emergency road segment by using the cellular data to obtain simulated road simulation data, which may include the following steps:

s301, first position information of the first vehicle and second position information of the second vehicle are determined.

In some examples, a device may determine first location information of a first vehicle, and second location information of a second vehicle. Wherein the second vehicle is a vehicle closest to the first vehicle in a traveling direction of the first vehicle.

S302, determining the distance between the first vehicle and the second vehicle according to the first position information and the second position information.

In some examples, the device may determine a distance between the first vehicle and the second vehicle according to the first position information and the second position information acquired in S301.

And S303, determining a following strategy for the first vehicle to follow the second vehicle based on the cellular data and the distance.

In some examples, the device may determine a following strategy for the first vehicle to follow the second vehicle based on the cellular data and the distance determined in S302. It is understood that following means that one vehicle follows behind another vehicle.

In some examples, if the first vehicle is a head vehicle closest to the emergency, the cell closest to the head vehicle on the lane where the emergency is located may be regarded as a second vehicle with a vehicle speed of 0 to follow.

Following S303, in some embodiments, controlling the driving behavior of the first vehicle traveling on the emergency road segment based on the simulated road simulation data in S204 may include:

and S304, controlling the first vehicle to follow the second vehicle based on the following strategy.

In some examples, the device may control the first vehicle to follow the following strategy to the second vehicle based on the following strategy determined at S303.

According to the method and the device, the car following strategy is determined by using the cellular data, and a more reasonable and correct car following strategy can be obtained based on the more accurate predicted cellular data, so that road congestion caused by an emergency is avoided, and secondary accidents can be effectively avoided.

In some embodiments, determining a following strategy for the first vehicle to follow the second vehicle based on the cellular data and the distance in S304 includes: and if the distance meets a preset safe distance threshold, determining that the following strategy is the speed of the first vehicle for accelerating or keeping constant speed. And if the distance does not meet the preset safe distance threshold, determining that the following strategy is the speed of the first vehicle for deceleration.

In some instances, when a vehicle is subject to emergency braking, at least a safe distance should be maintained between different vehicles in order to avoid collision of the current vehicle with other vehicles. The spacing canCalled safe distance threshold, denoted Gap _safe . The safety distance threshold value has a certain relation with the braking capability of the vehicle, the front vehicle and the reaction time of the driver.

As can be seen in a vehicle emergency braking schematic as shown in fig. 4, the distance between the first vehicle 401 and the second vehicle 402 at time t needs to be at least the safe distance threshold. Can ensure the moment after the vehicle is stopped by emergency braking, i.e. t ^stop The first vehicle 401 'does not collide with the second vehicle 402'. It will be appreciated that first vehicle 401 represents the first vehicle at time t, and first vehicle 401' represents t ^stop A first vehicle of a time of day. Similarly, second vehicle 402 represents the second vehicle at time t, and second vehicle 402' represents t ^stop A second vehicle of a time of day. Wherein, gap _safe，n’ Denoted as the safety distance threshold that the nth' vehicle (i.e., the first vehicle) should maintain from the preceding vehicle.

Wherein the location of the first vehicle 401 may be marked as x' _n’ (t), the position of the second vehicle 402 may be denoted as x' _n’+1 (t), the body lengths of first vehicle 401 and first vehicle 401' may be noted as l _n’ The body length of the second vehicle 402 and the second vehicle 402' can be written as l _n’+1 . The maximum deceleration of the first vehicle 401 and the first vehicle 401' is b _n’ The driver reaction time of the first vehicle 401 and the first vehicle 401' may be recorded as τ _n’ . The running speed of the first vehicle 401 is v _n’ (t) the traveling speed v of the second vehicle 402 _n’+1 (t) of (d). Then Gap _safe，n’ Can be calculated using equation 11.

Gap _safe，n’ ＝max(2×v _n’ (t)，x′ _n’+1 (t)-x′ _n’ (t)-l _n’+1 ) 8230the formula 8230that

Of course, as can be seen from FIG. 4, gap _safe，n’ It is also possible to derive the braking distance based on the braking distance of the first vehicle and the braking distance of the second vehicle, wherein the braking distance is the distance required for the vehicle to stop when the vehicle brakes suddenly. Equation 11 can also be transformed into equation 12, i.e.

Wherein, b _n’+1 Is the maximum deceleration of the second vehicle 402 and the second vehicle 402'.

In some examples, when the device determines that the distance satisfies a predetermined safe distance threshold, it may be determined that the first vehicle and the second vehicle are currently safe, and therefore the following policy may be set to control the first vehicle to accelerate, or control the first vehicle to maintain a constant speed. For example, when the distance is greater than or equal to the safe distance threshold, the distance may be considered to satisfy the safe distance threshold.

For example, the device may first determine whether a change in separation occurs, i.e., whether the separation between the first vehicle and the second vehicle changes. When the device determines that the separation is large and, in turn, that the separation meets a preset safe distance threshold, then the following strategy may be set to control the first vehicle to accelerate.

In some examples, equation 13 may be used to determine the speed of the first vehicle after acceleration.

v _n’ (t+1)＝min(v _n’ (t)+a′ _n’ ，V _max ，Gap _safe，n’ ) 8230the formula 823013

Wherein, a' _n’ Representing the normal acceleration, V, of the first vehicle _max Indicating a preset maximum vehicle speed limit.

In some examples, the headway between the first vehicle and the second vehicle should be no less than 1 second (second, s). The headway refers to the time interval between the headway ends of two continuous vehicles passing through a certain section in a vehicle queue running on the same lane, and the headway represents the time difference between the front ends of the two front and rear vehicles passing through the same place. Assuming that when a train of vehicles is in line, it takes 1s more time to wait for the departure of the preceding vehicle, starting with the second vehicle.

It can be seen from equation 13 that the accelerated vehicle speed will not exceed the maximum vehicle speed limit.

It will be appreciated that the following strategy arrangement controls the first vehicle acceleration to meet the driver's desire to travel at a higher vehicle speed.

In some examples, the following strategy may be arranged to control the first vehicle to maintain a constant speed if the apparatus determines that the separation is not large, e.g. small or unchanged, and further determines that the separation meets a predetermined safe distance threshold. For example, the vehicle speed at the next time may be determined based on equation 14.

v _n’ (t+1)＝min(v _n’ (t)，Gap _safe，n’ ) 8230; formula 14

As can be understood, the following strategy setting controls the first vehicle to keep constant speed, and the safe running of the vehicle can be ensured. No speed change measures are taken at this time.

In some examples, when the device determines that the distance does not satisfy the preset safe distance threshold, it may be deemed that the distance between the first vehicle and the second vehicle is currently too small and unsafe to drive (or dangerous to drive), and thus the following strategy may be set to control the first vehicle to decelerate. For example, when the distance is smaller than the safe distance threshold, it may be set that the distance does not satisfy the safe distance threshold.

It will be appreciated that the following strategy is arranged to control deceleration of the first vehicle when the spacing is changed, whether the spacing is changed to a greater spacing or a lesser spacing.

In some examples, the determination of which deceleration method to decelerate may be further based on whether the second vehicle is stationary. For example, if the second vehicle is stationary, i.e. v _n’+1 (t) =0, the vehicle may be decelerated using safe deceleration rules based on safety considerations. That is, it is necessary to ensure that the distance between the first vehicle and the second vehicle is not less than 1m. Such as may be determined by equation 15.

v _n’ (t+1)＝max{min(v _n’ (t)-b _n’ ，(Gap _safe，n’ -1)/2)，0}8230the formula 15

In other examples, v is non-stationary if the second vehicle is not stationary _n’+1 (t) ≠ 0, then the deterministic deceleration rules can be employed to decelerate the vehicle. Such as can be determined by equation 16.

v _n’ (t+1)＝max{min(v _n’ (t)-b _n’ ，Gap _safe，n’ /2), 0} \8230 \ 8230; formula 16

According to the method and the device, based on the relation between the distance and the safety distance threshold value, the following strategy of the vehicle can be determined in a targeted mode, and therefore the driving of the vehicle running on the emergency road section is guaranteed to be safer.

In some embodiments, the following strategy may further comprise: the first vehicle is controlled to decelerate using a preconfigured regular deceleration. In some examples, a random slowdown probability R is introduced into the following rules to account for uncertainty in the driver's driving behavior during driving _p . In some examples, R _p May be taken to be 0.11. Vehicles travelling on the road may be according to R _p A slowing down in speed is performed. The slowed vehicle speed may be calculated, for example, by equation 17.

v _n’ (t+1)＝max{v _n’ (t)-b′ _n’ 0} \8230 \ 8230; formula 17

Wherein, b' _n’ Indicated as the regular deceleration of the first vehicle. It can be seen that the random moderation can be according to b _n’ Deceleration is performed.

The method introduces the random slowing process, can accurately simulate the uncertainty of the driver when driving, and therefore can more accurately control the driving behavior of the first vehicle.

In some embodiments, the simulated road simulation data may include vehicle lane change data. In S203, performing road simulation on the emergency road segment by using the cellular data to obtain simulated road simulation data, which may include: and if the first position information of the first vehicle is positioned at the upstream of the lane of the emergency position information, determining a first target lane of the first vehicle according to the cellular data. The first target lane is used for the first vehicle to avoid the road accident corresponding to the road emergency information.

In some examples, the first location information being upstream in the lane of the emergency location information may be considered that the first vehicle has not traveled the emergency road segment or that the first vehicle has arrived on the emergency road segment. At this time, a strategy of vehicle lane changing can be provided for the first vehicle according to the cellular data. And in some cases controls the first vehicle to make a corresponding lane change.

For example, a road emergency may cause closure of multiple lanes, and the device may determine, based on the cellular data, the number of lanes on the currently closed road that support avoidance traffic. For example, lane 1, lane 2, lane 3, lane 4, etc. may be written from left to right. The device may determine a lane number closest to a lane in which the first vehicle is located and treat the lane as the first target lane. When there are a plurality of lanes at the same pitch, a lane satisfying the lane change condition may be selected as the first target lane according to the priority. The lane change condition may be preset, for example, according to the queuing length of the lane, the vehicle density, and the like, and may be set arbitrarily according to the actual situation, which is not limited in this disclosure.

In some examples, after determining the first target lane, if the road segment on which the vehicle is traveling changes, for example, enters another road segment, the above process is repeated to re-determine the first target lane.

According to the method and the device, a lane changing strategy is provided before the vehicle enters the emergency road section, so that the vehicle is helped to effectively avoid obstacles, and further congestion is prevented from being aggravated or secondary accidents are avoided.

In some embodiments, the cell data may also include lane queuing length data. Determining a first target lane of the first vehicle from the cellular data, may further include: a first target lane is determined based on the lane queuing length data. As shown in fig. 5, fig. 5 is a flowchart of a method for determining lane queuing length data according to an embodiment of the present disclosure. The lane queuing length data can be obtained by the following method:

s501, determining the number of unit cells with vehicles in the lane aiming at each lane.

In some examples, the device may determine lane queue length data for each lane, and thus, may determine, on a per lane basis, the number of cells having vehicles within the respective lane.

In some examples, the device may first determine the location of a first vehicle of a lane from a start location of the lane in a direction opposite the direction of travel of the vehicle. It is understood that the opposite direction may also be considered as the upstream direction of the lane.

In some examples, the fixed length L may be set to 5m in advance, and the device determines that the scanning ends if there is no vehicle in the cells scanned by the length L in the upstream direction by the first vehicle. Otherwise, scanning in the upstream direction will continue until the queuing condition is not satisfied. Where the queuing condition is not met, e.g., the cell does not contain a vehicle. Of course, other conditions can be equivalently replaced according to actual conditions, and the disclosure is not limited. Meanwhile, it can be understood that the fixed length L may be arbitrarily adjusted according to actual situations, and the disclosure is not limited thereto.

And S502, obtaining lane queuing length data corresponding to the lanes according to the number of the cells.

In some examples, the device may determine lane queue length data corresponding to the corresponding lane according to the number of cells including the vehicle counted in S501, in combination with a preset cell length.

In some examples, the device may also determine average queue length data for the respective road segment based on the lane queue length data for each lane.

According to the method and the device, the lane queuing length is determined by using the cellular data, so that the first target lane which is more in line with the situation can be planned based on the lane queuing length, the accuracy of avoiding obstacles by vehicles is improved, and the road is further prevented from being jammed.

In some embodiments, the cellular data may also include road network saturation data. Determining a first target lane of the first vehicle from the cellular data, may further include: and determining a first target lane according to the road network saturation data. As shown in fig. 6, fig. 6 is a flowchart of a method for determining road network saturation data according to an embodiment of the present disclosure. Road network saturation data can be obtained by adopting the following modes:

s601, determining the maximum number of the vehicles bearing the emergency road section.

In some examples, the device may determine a maximum number of host vehicles for the emergency road segment.

For example, it may be assumed that the designed traffic capacity of each lane is C =2000veh/h. Indicating 2000 vehicles can pass per hour. Of course, the specific data of C may be arbitrarily adjusted according to actual situations, for example, the data may be changed to different degrees according to different terrains, and the disclosure is not limited.

The average headway h corresponding to the traffic capacity can be calculated by formula 18 based on C,

then, the device may obtain the maximum number of loaded vehicles Cap for the corresponding road section using formula 19 using h obtained from formula 18.

Where v' represents the average speed of the road, l _i’ The lane length of the i ' th lane of the road section is shown, i ' is 1,2, \8230;, n '. l _car Representing the average vehicle length, and 5 is the preset minimum headway. Of course, 5 may be substituted with any other possible value in other examples, and the disclosure is not limited thereto.

And S602, determining road network saturation data of the emergency road section according to the maximum number of the bearing vehicles.

In some examples, the device may determine road network saturation data for the emergency road segment from the Cap determined in S601. For example, may be derived based on equation 20.

Wherein x' represents road network saturation, num _veh Representing the total number of vehicles predicted on the corresponding road segment at a certain moment.

In the related technology, the road network saturation evaluation is measured and calculated through actual flow and design traffic capacity, if a road in a large range is blocked due to an emergency, the actual flow of a road network area where an accident is located is difficult to find, the flow is measured and calculated to be 0, the road network saturation is 0, and the road network in the actual accident area is saturated. Therefore, road network density calculation is introduced into the method, and the method is high in applicability.

According to the method and the device, the cellular data are utilized to determine the road network saturation, so that the first target lane which is more consistent with the situation can be planned based on the road network saturation, the accuracy rate of the vehicle for avoiding obstacles is improved, and the road is further prevented from being blocked.

In some embodiments, the method may further comprise: and if the first position information is positioned at the downstream of the lane of the emergency position information, determining a second target lane of the first vehicle based on the attribute information of the first vehicle, the preset driver style information and the lane information of the first vehicle.

In some examples, the first location information being downstream of the lane of the emergency location information may be considered that the first vehicle has passed the emergency road segment, or that the first vehicle has arrived on the emergency road segment. At this time, a strategy of vehicle lane changing can be provided for the first vehicle based on the attribute information of the first vehicle, the preset driver style information and the lane information where the first vehicle is located. And in some cases controls the first vehicle to make a corresponding lane change.

For example, a second target lane is determined for the first vehicle based on the attribute information of the first vehicle, the preconfigured driver style information, and the lane information in which the first vehicle is located.

In some examples, the vehicle attribute information may describe, for example, what type of vehicle the first vehicle is, such as a small vehicle, a large vehicle, and so on. The cart is not allowed to run on the innermost lane, and if the lanes are sequentially ordered from left to right, the cart is not allowed to run on the innermost lane, namely lane 1. And the trolley runs on any lane. In other examples, the driver style may be preconfigured with different styles, such as aggressive, steady, and conservative. Of course, the above description is only an exemplary description, and any setting and adjustment may be performed according to the actual situation, and the disclosure is not limited.

In some examples, the vehicle type is a cart or a trolley, the driver style is aggressive type, steady type and conservative type, and the lane is 3 lanes. For example, the above information corresponding to a certain vehicle may be used as input, and the output is obtained, that is, the device determines the selection probability of each lane of the vehicle at the next time based on the input information. A second target lane is then determined based on the probability. And controlling the first vehicle to change the lane, namely, to drive on the second target lane under some conditions.

Fig. 7 is a schematic diagram of a lane change probability of a vehicle according to an embodiment of the disclosure.

As shown in fig. 7, if the vehicle type is a car, assuming that the car is traveling on the leftmost lane, it can be seen that the probabilities of selecting the second target lane are different according to different driver types. The driver style, referred to above, can be understood. For example, when the driver type is aggressive, since the first vehicle itself is located in the leftmost lane, the lane change to the left cannot be selected, i.e., the lane change probability of the lane change to the left is 0%. Since the driver type is aggressive, it is clear that the driver is more inclined to faster vehicle speeds, since the first vehicle is already in the leftmost lane at this time, and therefore the lane change probability of keeping the original lane is approximately 90%. It will be appreciated that the vehicle may generally travel faster on the road on the left hand lane than on the right hand lane, with the vehicle permitted to travel the fastest on the left-most lane. Obviously, since changing lanes to the right will reduce the vehicle speed in some cases, a typical aggressive driver will only change lanes to the right with a lane change probability of 10%.

If the driver type is a steady-driving type, the first vehicle is located in the leftmost lane, so that the lane changing to the left cannot be selected, namely the lane changing probability of the lane changing to the left is 0%. Since the driver type is a steady type, it is obvious that the driver usually does not tend to a certain lane, and therefore, the lane change probability of keeping the original lane and the lane change probability of changing the lane to the right can respectively account for 50%.

If the driver type is conservative, the first vehicle is located in the leftmost lane, so that the lane changing to the left cannot be selected, namely the lane changing probability of the lane changing to the left is 0%. Since the driver type is conservative, it is clear that the driver is more inclined to a relatively slow vehicle speed in order to seek smoother driving. Since the first vehicle is now in the leftmost lane and the speed of the vehicle traveling in the leftmost lane is relatively fast, the lane change probability for keeping the original lane is only approximately 10%. And the driver is more inclined to switch to a lane where the vehicle speed is relatively slow. Thus, a conservative driver typically has about a 90% lane change probability to the right.

FIG. 8 is another schematic diagram of lane change probability for a vehicle according to an embodiment of the present disclosure.

As shown in fig. 8, if the vehicle type is a car, the driver can choose to change lanes to either side or keep the original lane, assuming that the car is traveling on the middle lane. When the driver type is aggressive, it is clear that the driver is more inclined to the faster vehicle speed, and therefore the lane change probability to the left is approximately 90%. Because the right-hand lane tends to correspond to a slower vehicle speed lane, aggressive drivers typically do not switch lanes to the right, i.e., the lane-change probability for a lane-change-to-the-right is 0%. And aggressive drivers generally keep the lane change probability of the original lane only 10%.

If the driver type is the steady-driving type, the first vehicle is located in the middle lane at the moment. Drivers tend to keep driving smooth, and the lane change probability of the original lane is about 80%. And the lane change probability of changing lanes to the left and the lane change probability of changing lanes to the right can each account for 10%.

If the driver type is conservative, the first vehicle is located in the middle lane. The lane change probability of the driver for keeping the original lane and the lane change probability of changing lanes to other lanes can respectively account for 50 percent. And since the driver is a conservative driver, the driver is more inclined to the lane with slower vehicle speed. Therefore, the lane change probability of the right lane change is often higher than that of the left lane change, for example, the lane change probability of the right lane change can be 40%, and the lane change probability of the left lane change is only 10%.

FIG. 9 is a schematic diagram of a lane change probability for another vehicle according to an embodiment of the disclosure.

As shown in fig. 9, if the vehicle type is a car, it is assumed that the car is traveling on the rightmost lane. When the driver type is aggressive, it is clear that the driver is more inclined to the faster vehicle speed, and the lane change probability to the left is almost 100% because the vehicle speed of the rightmost lane is the slowest. Aggressive drivers generally do not keep the original lane, i.e., the lane change probability of keeping the original lane is 0%. And because the first vehicle is already in the rightmost lane at the moment, the lane changing to the right cannot be performed, namely the lane changing probability of the lane changing to the right is also 0%.

If the driver type is the steady type, and the first vehicle is positioned at the rightmost lane at the moment. The driver will typically select a lane with a moderate vehicle speed, such as a center lane, in order to keep the driving smooth. Therefore, the lane change probability to the left is about 80%. And the lane change probability for keeping the original lane is about 20%. Since the first vehicle is already in the rightmost lane at this time, the lane change to the right is not possible, i.e., the lane change probability to the right is still 0%.

If the driver type is conservative, and the first vehicle is located at the rightmost lane at the moment. Since the vehicle speeds of the middle lane and the rightmost lane are not the highest vehicle speed, the lane change probability of the driver keeping the original lane and the lane change probability of the driver changing the lane to the left can respectively account for 50%. Since the first vehicle is already in the rightmost lane at this time, the lane change to the right is not possible, i.e., the lane change probability to the right is still 0%.

Fig. 10 is a schematic diagram of a lane change probability of another vehicle according to an embodiment of the disclosure.

As shown in fig. 10, if the vehicle type is a large vehicle, the driver may choose to change lanes to the right or keep the original lane, assuming that the large vehicle is traveling on the middle lane. When the driver type is aggressive, the cart is not allowed to run on the leftmost lane, and the lane is only 3 lanes. Therefore, the lane change probability for keeping the original lane is almost 100%. Aggressive drivers often do not choose to change lanes, i.e., the lane change probability to the right is 0%. And because the cart is usually not allowed to run on the leftmost lane, the driver cannot change the lane to the left, namely the lane change probability of changing the lane to the left is also 0%.

If the driver type is the steady type, and the first vehicle is located in the middle lane at the moment. The driver tends to keep driving smooth, and the lane change probability of keeping the original lane is about 90%. And the lane change probability to the right may be 10%. Since the large vehicle is not allowed to run on the leftmost lane, the driver does not change lanes to the left, that is, the lane change probability of changing lanes to the left is still 0%.

If the driver type is conservative, and the first vehicle is located in the middle lane at the moment. The lane change probability of the driver normally keeping the original lane and the lane change probability of the driver changing the lane to the right can respectively account for 50 percent. Since the large vehicle is not allowed to run on the leftmost lane, the driver does not change lanes to the left, that is, the lane change probability of changing lanes to the left is still 0%.

FIG. 11 is another schematic diagram of lane change probability for a vehicle according to an embodiment of the present disclosure.

As shown in fig. 11, if the vehicle type is a large vehicle, it is assumed that the large vehicle travels on the rightmost lane. When the driver type is aggressive, it is clear that the driver is more inclined to the faster vehicle speed, and the lane change probability to the left is almost 100% because the vehicle speed of the rightmost lane is the slowest. Aggressive drivers usually do not keep the original lane, i.e. the lane change probability of keeping the original lane is 0%. And because the first vehicle is already in the rightmost lane at the moment, the lane changing to the right cannot be performed, namely the lane changing probability of the lane changing to the right is also 0%.

If the driver type is the steady type, and the first vehicle is positioned at the rightmost lane at the moment. In order to keep the driving smooth, the driver usually selects a lane with a moderate vehicle speed, such as a center lane. Therefore, the lane change probability to the left is about 80%. And the lane change probability for keeping the original lane is about 20%. Since the first vehicle is already in the rightmost lane at this time, the lane change to the right is not possible, i.e., the lane change probability to the right is still 0%.

If the driver type is conservative, and the first vehicle is located at the rightmost lane at the moment. The lane change probability for the driver to keep the original lane and the lane change probability for the lane change to the left can each account for 50%. Since the first vehicle is already in the rightmost lane at this time, the lane change to the right is impossible, i.e., the lane change probability to the right is still 0%.

It is understood that fig. 7 to fig. 11 are only exemplary descriptions, and the specific probability may be adaptively adjusted and modified according to actual situations, and the disclosure is not limited thereto.

In some examples, after the device determines the second target lane, it may determine whether to switch lanes according to the condition of the vehicle in the second target lane, such as shown in fig. 12, which shows a vehicle lane-changing scene 1200. In this scenario, vehicle 1201 may be a first vehicle and vehicle 1202 may be a second vehicle. Vehicle 1203 is a vehicle behind the first vehicle in the second target lane and vehicle 1204 is a vehicle ahead of the first vehicle in the second target lane.

It can be seen that at this point in time, the spacing between vehicle 1201 and vehicle 1202 still needs to be maintained at least at the safe distance threshold Gap _safe，n’ . And the distance between vehicle 1201 and vehicle 1203 may be denoted as d _n’，back . And the distance between the vehicle 1201 and the vehicle 1204 can be recorded as Gap _n’，other 。

At this time, if the vehicle 1201 wants to change the second target lane, it is necessary to determine whether the conditions of formula 21 and formula 22 are satisfied. For example, in the case of a liquid,

d _n’，back ＞Gap _safe，n’ 8230the formula 8230

Gap _n’，other ＞Gap _safe，n’ 8230A formula 823022

When the conditions of equation 21 and equation 22 are satisfied, it may be determined that the lane change is performed for the first vehicle, i.e., the second target lane is changed.

Of course, it is understood that the conditions of equations 21 and 22 may be equally applicable for the first vehicle to switch to the first target lane.

Of course, in some examples, when the forced lane change is performed by the driver according to actual conditions, assuming that the active lane change (such as the first target lane or the second target lane) is also triggered at this time, the forced lane change of the driver often has a higher execution priority. I.e. a forced lane change is preferably performed.

It can be understood that the purpose of the vehicle lane changing related to the present disclosure is to solve the problem that when a vehicle ramp merges into a vehicle ramp or passes through a closed road section of a lane, the vehicle tends to gather on one lane, and other vacant lanes are not fully utilized.

In some embodiments, as shown in fig. 13, fig. 13 is a flowchart of yet another method of controlling vehicle driving behavior in accordance with embodiments of the present disclosure. The method may further comprise the steps of:

s1301, vehicle running information of the first vehicle is obtained.

In some examples, a device may obtain vehicle travel information for a first vehicle. Such as current first vehicle position information, vehicle speed, etc. For example, the location information of the first vehicle may be x' _n’ (t), the vehicle speed may be v _n’ (t)。

S1302, the first position information is updated based on the vehicle travel information and the first position information of the first vehicle.

In some examples, the device may update the first position information based on the vehicle travel information acquired in S1301 and the first position information of the first vehicle.

For example, if' _n’ (t)+v _n’ When t ≦ L, the updated first position information x 'may be determined using equation 23' _n’ (t + 1). Wherein L represents the lane length of the lane in which the first vehicle is located.

x′ _n’ (t+1)→x′ _n’ (t)+v _n’ (t) \8230; (formula 23)

X' _n’ (t)+v _n’ If t > L, the updated first position information x 'may be determined according to the number of downstream lanes connected to the current lane' _n’ (t + 1). For example, if the number of the downstream lanes is only 1, and the tail position of the downstream laneThe number w of empty cells between the first vehicle and one cell of the entrance lane is greater than the length of the first vehicle body. The position of the leading end of the first vehicle corresponding to the downstream lane can be obtained by equation 24, for example,

min{x′ _n’ (t)+v _n’ (t) -L-1, w-1}. 8230A formula 24

If the number of the downstream lanes is multiple, the lanes same as the current lane can be preferentially selected, and the number w of the empty cells between the tail position of the downstream lane and one cell of the entrance lane is greater than the length of the first vehicle body. The position of the head of the first vehicle corresponding to the downstream lane can still be found by equation 24.

It is to be appreciated that when the vehicle location is updated, the following maneuver and/or the lane-change maneuver may be re-determined based on the updated first location information.

The method and the device can also update the first position information, so that continuous prediction can be realized, a simulation result in a period of time in the future can be obtained, and a proper control measure can be adopted in time to avoid congestion or secondary accidents.

In some embodiments, the method may further comprise: and updating the cell data according to the updated first position information.

In some examples, after the first location information is updated, a lane to be replaced may be determined and the attribute of the first vehicle may be switched to the lane to be replaced. And then, determining corresponding cellular data on the replaced lane, for example, determining cellular data which is parallel to the cellular data corresponding to the vehicle and is positioned on the lane after replacement.

It is understood that, when updating the cell data, the vehicle speed before lane change may be updated to the cell data after lane change. And the vehicle speed can be updated by referring to the position updating manner provided by the above equation 24, which is not described in detail in this disclosure.

The method and the device can also update the cell data, thereby being beneficial to carrying out simulation again by utilizing the cell data after being thinned and providing a corresponding strategy. Therefore, continuous simulation in a period of time in the future is realized, and a manager can take appropriate control measures in time.

Fig. 14 is a schematic diagram of a road simulation according to an embodiment of the disclosure.

As shown in fig. 14, it can be seen that, by using the methods shown in fig. 2 to fig. 13, road simulation can be performed at a future time or a certain period of time, so as to simulate a possible congestion situation and a queuing situation of each vehicle in advance. Therefore, the road manager or the driver can reasonably plan the emergency road section and make corresponding preparation in advance.

Fig. 15 is a schematic diagram of a following rule flow of an embodiment of the disclosure.

As shown in fig. 15, the present disclosure further provides a schematic flow chart of a car-following rule, where the flow chart may include the following steps:

s1501, the vehicle follows.

In some examples, the device may determine that a first vehicle is following a second vehicle.

S1502, it is determined whether the pitch becomes large.

In some examples, the device may determine whether a separation between the first vehicle and the second vehicle is large. If the pitch is increased, executing S1503; if the pitch is not increased, e.g., decreased or maintained, S1506 is performed.

And S1503, whether the spacing is small or not and a safety distance.

In some examples, if the spacing is determined to be large, the device may also determine whether the spacing is less than a preset safe distance threshold. If the distance is smaller than the safety distance threshold value, executing S1504; if the pitch is not less than the safe distance threshold, e.g., greater than or equal to the safe distance threshold, S1505 is performed.

S1504, controlling the first vehicle to decelerate.

In some examples, the device may control the first vehicle to slow down if the separation is less than the safe distance threshold.

S1505, controlling the first vehicle to accelerate.

In some examples, the device may control the first vehicle to accelerate if the separation is greater than or equal to the safe distance threshold.

And S1506, whether the spacing is small or not and the safety distance.

In some examples, if the spacing is determined to be small or constant, the device may also determine whether the spacing is less than a preset safe distance threshold. If the distance is smaller than the safety distance threshold value, executing S1507; if the distance is not less than the safety distance threshold, for example, greater than or equal to the safety distance threshold, S1508 is executed.

And S1507, controlling the first vehicle to decelerate.

And S1508, controlling the first vehicle to keep constant speed.

In some examples, the device may control the first vehicle to maintain a constant speed if the separation is greater than or equal to the safe distance threshold.

It can be understood that, the specific implementation process may refer to the corresponding descriptions in fig. 3 and fig. 4, and the details of the present disclosure are not repeated herein.

The solutions described in fig. 2 to fig. 15 in the present disclosure can make up for the lack of the event impact range and the road network evaluation in the conventional event identification technology. And the track changing rule of car following and lane closing is provided, and more refined car data can be provided for the research of sudden accidents.

Meanwhile, the evaluation method of the road network is optimized aiming at the scene of the emergency, and can be applied to the relevant decision of a high-speed management department.

Based on the same concept, the embodiment of the disclosure also provides a device for controlling the driving behavior of the vehicle.

It is understood that the device for controlling the driving behavior of the vehicle provided by the embodiment of the disclosure includes hardware structures and/or software modules for executing the respective functions in order to realize the functions. The disclosed embodiments can be implemented in hardware or a combination of hardware and computer software, in combination with the exemplary elements and algorithm steps disclosed in the disclosed embodiments. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the subject matter of the embodiments of the present disclosure.

Fig. 16 is a schematic diagram of an apparatus for controlling driving behavior of a vehicle according to an exemplary embodiment of the present disclosure. Referring to fig. 16, an apparatus 1600 for controlling driving behavior of a vehicle is provided, where the apparatus 1600 may implement any of the methods described above with reference to fig. 1-15. The apparatus 1600 may include: the acquiring module 1601 is configured to acquire road emergency information, where the road emergency information includes emergency time information and emergency position information; a determining module 1602, configured to determine cellular data of an emergency road segment by using historical road data related to the emergency time information and the emergency position information, where the emergency road segment includes a position corresponding to the emergency position information, and the cellular data is used to describe a vehicle driving condition of the corresponding road segment; the simulation module 1603 is used for performing road simulation on the emergency road section by using the cellular data to obtain simulated road simulation data; the control module 1604 is configured to control a driving behavior of a first vehicle traveling on the emergency road segment based on the simulated road simulation data.

In some possible embodiments, the simulated road simulation data includes vehicle-following data; the simulation module 1603 is further configured to determine first position information of a first vehicle and second position information of a second vehicle, wherein the second vehicle is a vehicle closest to the first vehicle in a traveling direction of the first vehicle; determining a distance between the first vehicle and the second vehicle according to the first position information and the second position information; determining a following strategy for the first vehicle to follow the second vehicle based on the cellular data and the distance; the control module 1604 is also configured to control the first vehicle to follow the second vehicle based on a follow-up strategy.

According to the method and the device, the following strategy of the vehicle is determined by using the cell data, and the more reasonable and correct following strategy can be obtained based on the more accurate predicted cell data, so that road congestion caused by an emergency is avoided, and secondary accidents can be effectively avoided.

In some possible embodiments, simulation module 1603 is further configured to: if the distance meets a preset safe distance threshold, determining a following strategy to control the first vehicle to accelerate or keep a constant speed; and if the distance does not meet the preset safe distance threshold, determining the following strategy as controlling the first vehicle to decelerate.

In some possible implementations, the control module 1604 is further configured to: the first vehicle is controlled to decelerate using a preconfigured regular deceleration.

In some possible embodiments, the simulated road simulation data includes vehicle lane change data; simulation module 1603 is also for: and if the first position information of the first vehicle is located at the upstream of the lane of the emergency position information, determining a first target lane of the first vehicle according to the cellular data, wherein the first target lane is used for the first vehicle to avoid the road accident corresponding to the emergency information.

According to the lane changing strategy provided by the invention, the vehicle can effectively avoid obstacles before entering the emergency road section, so that the condition that the congestion is aggravated or a secondary accident occurs can be avoided.

In some possible embodiments, the cell data includes lane queuing length data; simulation module 1603 is also for: determining a first target lane according to the lane queuing length data; the lane queuing length data is obtained by adopting the following method: determining the number of cells with vehicles in the lane for each lane; and obtaining the lane queuing length data corresponding to the lanes according to the number of the cells.

In some possible embodiments, the cellular data further comprises road network saturation data; simulation module 1603 is also for: determining a first target lane according to road network saturation data; the road network saturation data is obtained by adopting the following method: determining the maximum number of the vehicles bearing the emergency road section; and determining road network saturation data of the emergency road section according to the maximum number of the bearing vehicles.

In some possible embodiments, simulation module 1603 is further configured to: and if the first position information is positioned at the downstream of the lane of the emergency position information, determining a second target lane of the first vehicle based on the attribute information of the first vehicle, the preset driver style information and the lane information of the first vehicle.

In some possible embodiments, the apparatus 1600 further comprises: the obtaining module 1601 is further configured to obtain vehicle driving information of the first vehicle; an updating module 1605 for updating the first location information based on the vehicle travel information and the first location information of the first vehicle.

In some possible implementations, the update module 1605 is further configured to: and updating the cell data according to the updated first position information.

The apparatus of fig. 16 mentioned above in connection with the present disclosure has been described in detail in relation to the embodiment of the method, and the detailed description will not be provided herein.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.

The present disclosure also provides an apparatus for controlling a driving behavior of a vehicle, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

Fig. 16 shows a schematic block diagram of an apparatus 1700 for controlling vehicle driving behavior that may be used to implement embodiments of the present disclosure. It is to be appreciated that the device 1700 can be a network device or a terminal device. The apparatus 1700 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, server clusters, and other appropriate computers. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 17, the apparatus 1700 includes a computing unit 1701 that may perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM) 1702 or a computer program loaded from a storage unit 1708 into a Random Access Memory (RAM) 1703. In the RAM 1703, various programs and data required for the operation of the device 1700 can also be stored. The calculation unit 1701, the ROM 1702, and the RAM 1703 are connected to each other through a bus 1704. An input/output (I/O) interface 1705 is also connected to bus 1704.

Various components in the device 1700 are connected to the I/O interface 1705, including: an input unit 1706 such as a keyboard, a mouse, and the like; an output unit 1707 such as various types of displays, speakers, and the like; a storage unit 1708 such as a magnetic disk, optical disk, or the like; and a communication unit 1709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 1709 allows the device 1700 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 1701 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 1701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The computing unit 1701 executes various methods and processes described above, such as any one of the methods described in fig. 1 through 15. For example, in some embodiments, any of the methods described in fig. 1-15 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 1708. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 1700 via ROM 1702 and/or communications unit 1709. When the computer program is loaded into RAM 1703 and executed by computing unit 1701, one or more steps of any of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 1701 may be configured in any other suitable manner (e.g., by way of firmware) to perform any of the methods described above with respect to fig. 1-15.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain. Of course, in some examples, a server may also refer to a cluster of servers.

According to the method, the influence of the emergency on traffic in the future time is really restored in advance through vehicle visualization and flow prediction, the defect that the influence range of the emergency is difficult to predict by the traditional AI video recognition technology is overcome, a manager can conveniently take appropriate control measures in time, and congestion or secondary accidents are avoided.

The method also introduces the road network density as the analysis of the road network saturation, and solves the problem that the section traffic flow is difficult to measure and calculate along with the dynamic increase of the congestion length in the actual accident scene.

The method is further based on the safe distance and the cellular automata model, and vehicle following and track changing rules under the scene of the emergency are designed.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method of controlling driving behavior of a vehicle, the method comprising:

acquiring road emergency information, wherein the road emergency information comprises emergency time information and emergency position information;

determining cellular data of an emergency road section by using historical road data related to the emergency time information and the emergency position information, wherein the emergency road section comprises a position corresponding to the emergency position information, and the cellular data is used for describing the vehicle driving condition of the corresponding road section;

performing road simulation on the emergency road section by using the cellular data to obtain simulated road simulation data;

and controlling the driving behavior of the first vehicle running on the emergency road section based on the simulated road simulation data.

2. The method of claim 1, wherein the simulated road simulation data comprises vehicle-following data;

the road simulation of the emergency road section by using the cellular data to obtain simulation road simulation data comprises the following steps:

determining first position information of the first vehicle and second position information of a second vehicle, wherein the second vehicle is a vehicle which is closest to the first vehicle in a driving direction of the first vehicle;

determining a distance between the first vehicle and the second vehicle according to the first position information and the second position information;

determining a following strategy for the first vehicle to follow the second vehicle based on the cellular data and the distance;

the controlling the driving behavior of the first vehicle traveling on the emergency road segment based on the simulated road simulation data comprises:

controlling the first vehicle to follow the second vehicle based on the following strategy.

3. The method of claim 2, wherein the determining a following strategy for the first vehicle to follow the second vehicle based on the cellular data and the distance comprises:

if the distance meets a preset safety distance threshold, determining that the following strategy is to control the first vehicle to accelerate or keep the first vehicle at a constant speed;

and if the distance does not meet the preset safe distance threshold, determining that the following strategy is to control the first vehicle to decelerate.

4. The method of claim 2 or 3, wherein the following strategy further comprises:

controlling the first vehicle to decelerate using a preconfigured regular deceleration.

5. The method of any of claims 1-4, wherein the simulated road simulation data comprises vehicle lane change data;

the road simulation of the emergency road section by using the cellular data to obtain simulated road simulation data comprises the following steps:

and if the first position information of the first vehicle is located at the upstream of the lane of the emergency position information, determining a first target lane of the first vehicle according to the cellular data, wherein the first target lane is used for the first vehicle to avoid a road accident corresponding to the road emergency information.

6. The method of claim 5, wherein the cell data includes lane queuing length data;

the determining a first target lane of the first vehicle from the cellular data includes:

determining the first target lane according to the lane queuing length data;

the lane queuing length data is obtained by adopting the following method:

for each lane, determining the number of cells having vehicles within the lane;

and obtaining the lane queuing length data corresponding to the lane according to the number of the cells.

7. The method of claim 5 or 6, wherein said cellular data further comprises road network saturation data;

determining the first target lane according to the road network saturation data;

the road network saturation data is obtained by adopting the following method:

determining the maximum number of the vehicles bearing the emergency road section;

and determining the road network saturation data of the emergency road section according to the maximum number of the bearing vehicles.

8. The method according to any one of claims 5-7, wherein the method further comprises:

and if the first position information is located at the downstream of the lane of the emergency position information, determining a second target lane of the first vehicle based on the attribute information of the first vehicle, the preset driver style information and the lane information of the first vehicle.

9. The method according to any one of claims 1-8, wherein the method further comprises:

acquiring vehicle running information of the first vehicle;

updating the first location information based on the vehicle travel information and first location information of the first vehicle.

10. The method of claim 9, wherein the method further comprises:

and updating the cell data according to the updated first position information.

11. An apparatus for controlling driving behavior of a vehicle, the apparatus comprising:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring road emergency information, and the road emergency information comprises emergency time information and emergency position information;

the determining module is used for determining cellular data of an emergency road section by utilizing historical road data related to the emergency time information and the emergency position information, wherein the emergency road section comprises a position corresponding to the emergency position information, and the cellular data is used for describing the vehicle driving condition of the corresponding road section;

the simulation module is used for performing road simulation on the emergency road section by using the cellular data to obtain simulated road simulation data;

and the control module is used for controlling the driving behavior of the first vehicle running on the emergency road section based on the simulation road simulation data.

12. The apparatus of claim 11, wherein the simulated road simulation data includes vehicle-following data;

the simulation module is further configured to determine first position information of the first vehicle and second position information of a second vehicle, where the second vehicle is a vehicle closest to the first vehicle in a traveling direction of the first vehicle; determining a distance between the first vehicle and the second vehicle according to the first position information and the second position information; determining a following strategy for the first vehicle to follow the second vehicle based on the cellular data and the distance;

the control module is further configured to control the first vehicle to follow the second vehicle based on the following policy.

13. The apparatus of claim 12, wherein the simulation module is further to:

14. The apparatus of claim 12 or 13, wherein the control module is further configured to:

15. The apparatus of any one of claims 11-14, wherein the simulated road simulation data includes vehicle lane change data;

the simulation module is further configured to:

and if the first position information of the first vehicle is located at the upstream of the lane of the emergency position information, determining a first target lane of the first vehicle according to the cellular data, wherein the first target lane is used for enabling the first vehicle to avoid a road accident corresponding to the road emergency information.

16. The apparatus of claim 15, wherein the cell data includes lane queuing length data;

the simulation module is further configured to:

determining the first target lane according to the lane queuing length data;

the lane queuing length data is obtained by adopting the following method:

for each lane, determining the number of cells having vehicles within the lane;

17. The apparatus of claim 15 or 16, wherein said cellular data further comprises road network saturation data;

the simulation module is further configured to:

the road network saturation data is obtained by adopting the following method:

18. The apparatus of any of claims 15-17, wherein the simulation module is further configured to:

19. The apparatus of any one of claims 11-18, wherein the apparatus further comprises:

the obtaining module is further used for obtaining vehicle running information of the first vehicle;

and the updating module is used for updating the first position information based on the vehicle running information and the first position information of the first vehicle.

20. The apparatus of claim 19, wherein the update module is further configured to:

and updating the cellular data according to the updated first position information.

21. An apparatus for controlling driving behavior of a vehicle, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.

22. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-10.

23. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-10.