CN114056425B - Vehicle steering control method and device, vehicle and storage medium - Google Patents
Vehicle steering control method and device, vehicle and storage medium Download PDFInfo
- Publication number
- CN114056425B CN114056425B CN202111395577.3A CN202111395577A CN114056425B CN 114056425 B CN114056425 B CN 114056425B CN 202111395577 A CN202111395577 A CN 202111395577A CN 114056425 B CN114056425 B CN 114056425B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- steering
- sample
- vehicles
- opposite
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D6/00—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The present disclosure relates to a vehicle steering control method, device, vehicle and storage medium, the method comprising: the method comprises the steps of obtaining detection data of the vehicle to be steered at the current intersection, wherein the detection data comprise information of the opposite vehicle, and controlling steering operation of the vehicle to be steered at the current intersection based on the detection data and a pre-trained steering decision model.
Description
Technical Field
The present disclosure relates to the field of automatically driven automobiles, and in particular, to a vehicle steering control method, device, vehicle, and storage medium.
Background
In recent years, the technology of automatic driving automobiles is rapidly developed, and in the future, the technology is also closely related to daily lives of people. In the process of automatically driving an automatic driving automobile, steering decision is one of the most difficult key problems to solve, such as left turning. How to realize efficient and safe left turn at the intersection according to some information of the opposite vehicles has important significance for the whole automatic driving system.
Disclosure of Invention
In order to achieve the above objective, the present disclosure provides a vehicle steering control method, device, vehicle and storage medium for solving the problem of steering decision when the vehicle is automatically driven.
According to a first aspect of an embodiment of the present disclosure, there is provided a vehicle steering control method including:
acquiring detection data of a vehicle to be steered at a current intersection, wherein the detection data comprises information of an opposite vehicle of the vehicle to be steered at the current intersection, and the opposite vehicle is a vehicle which runs opposite to the vehicle to be steered and is nearest to the vehicle to be steered on an opposite lane of the vehicle to be steered;
controlling steering operation of the vehicle to be steered at the current intersection based on the detection data and a pre-trained steering decision model; the steering decision model is trained based on a plurality of sets of sample data of sample vehicles at a target intersection, wherein the sample data comprises information of opposite vehicles of the sample vehicles at the target intersection.
Optionally, before the acquiring the detection data of the vehicle to be steered at the current intersection, the method includes: collecting the plurality of groups of sample data of the sample vehicles at the target intersection; training the steering decision model based on the plurality of sets of sample data.
Optionally, the vehicle information of the opposite vehicle includes vehicle speed, vehicle position and vehicle acceleration information of the opposite vehicle; the step of training the steering decision model based on the plurality of sets of sample data comprises: calculating a collision time of the oncoming vehicle to the position of the sample vehicle based on the vehicle speed, the vehicle position and the vehicle acceleration information of the oncoming vehicle; training a steering decision model based on the collision time.
Optionally, the sample data includes equal numbers of first sample data and second sample data, the first sample data is sample data in a scene in which the sample vehicle is recommended to turn in a first direction, and the second sample data is sample data in a scene in which the sample vehicle is not recommended to turn in the first direction.
Optionally, the vehicle information of the opposing vehicle further includes a vehicle number of the opposing vehicle; the step of training a steering decision model based on the sample data comprises: calculating a steering probability of the sample vehicle based on a first fault tolerance coefficient and the first sample data; the steering probability characterizes a probability that the sample vehicle can steer in the first direction; the first fault tolerance coefficient is an offset coefficient determined based on a training result of the first sample data; the calculation formula of the steering probability comprises the following steps:
wherein lambda is the first fault tolerance coefficient and n is the number of vehicles; ttc n For a collision of the oncoming vehicle n in the first sample data to the current position of the sample vehicleTime; u (u) n The average value of collision time of the opposite vehicles on an opposite lane n in the first sample data is taken as the opposite lane n where the opposite vehicles n are located; sigma (sigma) n 2 A variance of collision time on the opposite lane n in the first sample data;
calculating a non-steering probability of the sample vehicle based on a second fault tolerance coefficient and the second sample data; the non-steering probability characterizes a probability that the sample vehicle cannot steer in the first direction; the second fault tolerance coefficient is an offset coefficient determined based on a training result of the second sample data; the calculation formula of the non-steering probability comprises the following steps:
wherein lambda' is the second fault tolerance coefficient and n is the number of vehicles; ttc n ' is the collision time of the oncoming vehicle n to the current position of the sample vehicle in the second sample data; u (u) n ' is an average value of collision times of the opposing vehicles on an opposing lane n, which is an opposing lane in which the opposing vehicle n is located, in the second sample data;a variance of collision time on the opposite lane n in the second sample data; and updating the first fault-tolerant coefficient and the second fault-tolerant coefficient according to the steering probability and the non-steering probability respectively, and executing the step of calculating the steering probability of the sample vehicle based on the first fault-tolerant coefficient and the first sample data and the step of calculating the non-steering probability of the sample vehicle based on the second fault-tolerant coefficient and the second sample data again according to the updated first fault-tolerant coefficient and the updated second fault-tolerant coefficient until a preset training condition is met, so as to obtain the steering decision model.
Optionally, the vehicle information of the opposite vehicle includes the number of vehicles and the type of vehicles of the opposite vehicle; the step of training a steering decision model based on the sample data comprises: calculating the steering probability of the vehicle to be steered based on the collision risk coefficient, the first fault tolerance coefficient and the first sample data; the steering probability characterizes a probability that the sample vehicle can steer in the first direction; the collision risk coefficient is a constant determined based on the vehicle type and the vehicle speed; the first fault tolerance coefficient is an offset coefficient determined based on a training result of the first sample data; the calculation formula of the steering probability comprises the following steps:
wherein T is n Lambda is the first fault tolerance coefficient for the collision risk coefficient for the oncoming vehicle n, n being the number of vehicles; ttc n A collision time for the oncoming vehicle n to reach the current position of the sample vehicle in the first sample data; u (u) n The average value of collision time of the opposite vehicles on an opposite lane n in the first sample data is taken as the opposite lane n where the opposite vehicles n are located; sigma (sigma) n 2 The variance of collision time on the opposite lane n is the first sample data.
Optionally, the step of controlling the steering operation of the vehicle to be steered at the current intersection based on the detection data and a pre-trained steering decision model includes: acquiring information of a target opposite vehicle of the vehicle to be steered at the current intersection; the target opposite vehicle is a vehicle which runs opposite to the vehicle to be turned on an opposite lane of the vehicle to be turned, and the information of the target opposite vehicle comprises the number of vehicles, the type of the vehicles, the speed of the vehicles, the position of the vehicles and the acceleration information of the vehicles; calculating the steering probability and the non-steering probability of the vehicle to be steered in the first direction by using the steering decision model based on the information of the target opposite vehicle; and controlling the steering operation of the vehicle to be steered according to the steering probability and the non-steering probability of the vehicle to be steered.
According to a second aspect of the embodiments of the present disclosure, there is provided a vehicle steering control apparatus, the apparatus including: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring detection data of a vehicle to be steered at a current intersection, the detection data comprise information of an opposite vehicle of the vehicle to be steered at the current intersection, and the opposite vehicle is a vehicle which runs opposite to the vehicle to be steered on an opposite lane of the vehicle to be steered;
the processing module is used for controlling the steering operation of the vehicle to be steered at the current intersection based on the detection data and a pre-trained steering decision model; the steering decision model is trained based on a plurality of sets of sample data of sample vehicles at a target intersection, wherein the sample data comprises information of opposite vehicles of the sample vehicles at the target intersection.
According to a third aspect of the disclosed embodiments, there is provided a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the aforementioned vehicle steering control method.
According to a fourth aspect of embodiments of the present disclosure, there is provided a vehicle comprising: a memory having a computer program stored thereon; and a processor for executing the computer program in the memory to implement the steps of the vehicle steering control method described above.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects: according to the method and the device for controlling the steering operation of the vehicle to be steered at the current intersection, the detection data of the vehicle to be steered at the current intersection can be obtained, the detection data comprise information of the opposite vehicle, the steering operation of the vehicle to be steered at the current intersection is controlled based on the detection data and a pre-trained steering decision model, whether the steering operation of the vehicle to be steered can be effectively judged, and the driving safety of an automatic driving system is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:
fig. 1 is a flowchart of a vehicle steering control method shown in an exemplary embodiment of the present disclosure.
Fig. 2 is a schematic view of a scenario of a sample vehicle at a target intersection, as illustrated in an exemplary embodiment of the present disclosure.
Fig. 3 is a flowchart illustrating sub-steps of step S202 according to an exemplary embodiment of the present disclosure.
Fig. 4 is a flowchart illustrating sub-steps of step S2022, shown in an exemplary embodiment of the present disclosure.
Fig. 5 is a flowchart illustrating sub-steps of step S204 according to an exemplary embodiment of the present disclosure.
Fig. 6 is a block diagram of a vehicle steering control apparatus according to an exemplary embodiment of the present disclosure.
Fig. 7 is a block diagram of a vehicle shown in an exemplary embodiment of the present disclosure.
Description of the reference numerals
10-an opposing vehicle; 20-an opposing vehicle; 30-sample vehicle; 200-vehicle steering control device; 203, an acquisition module; 205-a processing module; 1900-vehicle; 1922-a processor; 1932-memory; 1926-power supply assembly; 1950-communication component; 1958-input/output interface.
Detailed Description
Specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.
When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
Referring to fig. 1, fig. 1 is a flowchart illustrating a vehicle steering control method according to an exemplary embodiment of the present disclosure, which is applicable to an electronic device, which may be an electronic device in a vehicle, which may be an autonomous vehicle. The vehicle steering control method shown in fig. 1 includes the steps of:
in step S201, a plurality of sets of sample data of sample vehicles at a target intersection are collected.
Referring to fig. 2, fig. 2 is a schematic view of a scene of a sample vehicle at a target intersection according to an exemplary embodiment of the disclosure; in fig. 2, a sample vehicle waits for left turn at an intersection, and a driver with abundant driving experience can drive the sample vehicle to collect sample data, so as to improve the accuracy of the sample data, more than one driver with abundant driving experience, such as two drivers, three drivers and the like, can be matched on the sample vehicle (automatic driving vehicle); the driver judges whether the vehicle can turn left according to the information of the opposite vehicles on the opposite lanes, wherein the opposite vehicles are vehicles which run opposite to the sample vehicle and are closest to the sample vehicle in each opposite lane, the opposite vehicles comprise opposite vehicles 10 and opposite vehicles 20 as shown in fig. 2, the opposite vehicles 10 are vehicles closest to the sample vehicle 30 on the lanes of the opposite vehicles 10, and the opposite vehicles 20 are vehicles closest to the sample vehicle 30 on the lanes of the opposite vehicles 20; only two opposing vehicles are shown in fig. 2, but in one embodiment the opposing vehicles may be one, three, etc. The sample vehicle 30 driven by the driver may acquire information of the oncoming vehicle including the number of vehicles of the oncoming vehicle, the type of the vehicle, the speed of the vehicle, the position of the vehicle, and the acceleration information of the vehicle. The information of the opposite vehicles can be obtained through network connection equipment or sensors such as radar, cameras and the like, and the present disclosure does not specifically require the information.
The sample data includes, but is not limited to, information of the opposing vehicle of the sample vehicle. The sample tag of the sample data is recorded while the sample data is acquired (the sample tag may be determined based on the driver's judgment), the sample tag including a suggestion that the sample vehicle turns in a first direction, which in one embodiment is the direction of left turn, and a non-suggestion that the sample vehicle turns in the first direction.
It should be noted that in this step, there may be multiple sample vehicles, and there may be multiple target intersections, for example, there may be multiple sets of sample data acquired by one sample vehicle from multiple target intersections, there may be multiple sets of sample data acquired by multiple sample vehicles from the same target intersection, there may be multiple sets of sample data acquired by one sample vehicle from multiple target intersections, and there may be multiple sets of sample data acquired by one sample vehicle from multiple sets of sample data acquired by any one or multiple modes as required.
In step S202, a steering decision model is trained based on a plurality of sets of sample data.
It should be noted that, since the driver determines whether the vehicle can turn left according to the information of the opposite vehicle on the opposite lane, the first sample data and the second sample data are obtained, the first sample data are sample data in a scene in which the sample vehicle is recommended to turn in the first direction, and the second sample data are sample data in a scene in which the sample vehicle is not recommended to turn in the first direction, and in one embodiment, the first direction is the direction of the left turn; in step S202, the first sample data and the second sample data with equal amounts are selected for training the steering decision model.
Step S202 includes sub-step S2021 and sub-step S2022, and the specific manner of training the steering decision model will be described in detail in the sub-step of step S202. Referring to fig. 3, fig. 3 is a flowchart illustrating sub-steps of step S202 according to an exemplary embodiment of the present disclosure.
In sub-step S2021, the collision time for the oncoming vehicle to reach the position of the sample vehicle is calculated based on the vehicle speed, the vehicle position, and the vehicle acceleration information of the oncoming vehicle.
And calculating the Time-To-Collision (TTC) of the opposite vehicle To the position of the sample vehicle according To the selected first sample data and the second sample data. Specifically, a first position where the oncoming vehicle arrives at the specimen vehicle is calculated according to the vehicle speed, the vehicle position and the vehicle acceleration information of the oncoming vehicle in the first specimen dataTime to collision ttc n Calculating a second collision time ttc when the opposite vehicle reaches the position of the sample vehicle according to the vehicle speed, the vehicle position and the vehicle acceleration information of the opposite vehicle in the second sample data n ′。
According to the first collision time ttc n Second time to collision ttc n The' turn probability of the sample vehicle, which characterizes the probability that the sample vehicle can turn in a first direction, and the non-turn probability, which characterizes the probability that the sample vehicle cannot turn in the first direction, which in one embodiment is the direction of left turn, can be calculated. For example, referring to fig. 2, the steering probability characterizes a probability that the sample vehicle 30 may make a left turn in the current state of the counter vehicle 10 and the counter vehicle 20, and the non-steering probability characterizes a probability that the sample vehicle 30 may not make a left turn in the current state of the counter vehicle 10 and the counter vehicle 20.
The above steering probability, the steering probability p, can be obtained from the first sample data 1 The calculation formula of (1) comprises:
wherein n is the number of vehicles facing the vehicle; ttc n For the collision time of the opposing vehicle n to the current position of the sample vehicle in the first sample data, for example, see fig. 2, where there are two opposing vehicles in fig. 2, ttc 1 Can represent the time of collision, ttc, of the subject vehicle 10 to the current position of the sample vehicle 2 The collision time of the subject vehicle 20 to the current position of the sample vehicle may be represented, where the value of n is 2; u (u) n The average value of collision time of the opposite vehicles on an opposite lane n in the first sample data, wherein the opposite lane n is the opposite lane where the opposite vehicle n is positioned; sigma (sigma) n 2 Is the variance of the collision time on the opposite lane n in the first sample data.
The non-steering probability p can be obtained according to the second sample data 2 The calculation formula of (1) comprises:
wherein n is the number of vehicles facing the vehicle; ttc n ' is the time of collision of the opposing vehicle n to the current position of the sample vehicle in the second sample data, for example, see FIG. 2, where there are two opposing vehicles in FIG. 2, ttc 1 ' may represent the time of collision, ttc, of the subject vehicle 10 to the current location of the sample vehicle 2 ' may represent a collision time of the subject vehicle 20 to the current position of the sample vehicle, where the value of n is 2; u's' n The average value of collision time of the opposite vehicles on an opposite lane n in the second sample data, wherein the opposite lane n is the opposite lane where the opposite vehicle n is located;the variance of the collision time on the opposite lane n in the second sample data.
In sub-step S2022, a steering decision model is trained based on the collision time.
For an automatic driving system, the driving safety must be first, so the established steering decision model can tolerate that the vehicle can turn left is erroneously judged as the vehicle cannot turn left, but cannot tolerate that the vehicle cannot turn left is erroneously judged as the vehicle can turn left, which is based on the cost sensitive classification, and the automatic driving safety is improved to solve the problem.
In one embodiment, the present disclosure introduces fault tolerance coefficients that characterize the cost for offsetting the output results of the steering decision model, e.g., offsetting the results of "vehicle may turn left" toward "vehicle may not turn left".
Step S2022 includes sub-step S20221 and sub-step S20222, and the specific manner in which the steering decision model is trained will be described in detail in the sub-step of step S2022. Referring to fig. 4, fig. 4 is a flowchart illustrating sub-steps of step S2022, shown in an exemplary embodiment of the present disclosure.
In sub-step S20221, a steering probability of the sample vehicle is calculated based on the first fault tolerance coefficient and the first sample data.
Illustratively, a first fault tolerance coefficient is introduced, the steering probability of the sample vehicle is calculated based on the first sample data, and the first fault tolerance coefficient is an offset coefficient determined based on the training result of the first sample data; at this time, the calculation formula of the steering probability includes:
wherein lambda is a first fault tolerance coefficient, and the value range is 0<λ<1，n、ttc n 、u n Sigma (sigma) n 2 The meaning of (a) is consistent with that described in the foregoing embodiments, and specific reference may be made to the foregoing embodiments, which are not repeated here.
In sub-step S20222, a non-steering probability of the sample vehicle is calculated based on the second fault tolerance coefficient and the second sample data.
Illustratively, a second fault tolerance coefficient is introduced, the steering probability of the sample vehicle is calculated based on the second sample data, and the second fault tolerance coefficient is an offset coefficient determined based on the training result of the second sample data; at this time, the calculation formula of the non-steering probability includes:
wherein lambda 'is a second fault tolerance coefficient, and the value range is lambda'>1，n、ttc n ′、u n ' and sigma n ′ 2 The meaning of (a) is consistent with that described in the foregoing embodiments, and specific reference may be made to the foregoing embodiments, which are not repeated here.
Updating the first fault-tolerant coefficient and the second fault-tolerant coefficient according to the steering probability and the non-steering probability respectively, executing the sub-step S20221 again to calculate the steering probability of the sample vehicle according to the updated first fault-tolerant coefficient and the updated second fault-tolerant coefficient, and executing the sub-step S20222 again to calculate the non-steering probability of the sample vehicle until the preset training condition is met, so as to obtain the steering decision model. The preset training condition is a preset reasonable value in the field, aims to enable the accuracy of the steering decision model to reach a preset value, and can be obtained based on experience.
It should be noted that, updating the first fault tolerance coefficient and the second fault tolerance coefficient aims at shifting the boundary of the steering decision model to the direction of "the vehicle cannot turn left", that is, decreasing the value of the steering probability and increasing the value of the non-steering probability, so as to reduce the risk of erroneously determining that the sample vehicle "the vehicle cannot turn left" is "the vehicle can turn left".
In addition, for the automatic driving system, the magnitude of risk caused by the collision of the vehicle needs to be considered, for example, in the case that the opposite vehicle is a small-sized vehicle, and the opposite vehicle is a large-sized vehicle, and the result after the collision of the automatic driving vehicle is completely different, and for this reason, the present disclosure designs another training method of the steering decision model based on the severity of the result that may be caused by the collision of the vehicle, which will be described in detail below.
In the present embodiment, a collision risk coefficient T representing the collision risk level of the vehicle is introduced n The collision risk coefficient is a constant determined based on the type of vehicle and the speed of the vehicle. Firstly, the collision risk level is classified according to the severity of the collision of the vehicle:
for example, when the oncoming vehicle is a normal small-sized vehicle and the traveling speed is lower than a predetermined threshold value, the collision risk level is 4; when the opposite vehicle is a common small vehicle and the running speed is higher than the preset threshold value, the collision risk level is 3; when the opposite vehicle is a large vehicle and the running speed is lower than the preset threshold value, the collision risk level is 2; when the oncoming vehicle is a large vehicle and the traveling speed is higher than the predetermined threshold value, the collision risk level is 1. In one embodiment, the predetermined threshold may be, but is not limited to, 50 kilometers per hour. Referring to FIG. 2, the opposing vehicle of FIG. 2 is illustrated with the opposing lane in which the opposing vehicle 10 is located being an adjacent pair of sample vehiclesToward the lane, for the opposite vehicles on the adjacent opposite lane, when the collision risk level is respectively 1 level, 2 level, 3 level, 4 level, the corresponding T n The values of (2) may be, but are not limited to, 0.9, 0.6, 0.3, 0; the opposite lane where the opposite vehicle 20 is located is the far adjacent opposite lane of the sample vehicle, because the opposite lane where the opposite vehicle 20 is located is separated from the lane where the sample vehicle is located by one lane, when the collision risk level is respectively 1 level, 2 level, 3 level and 4 level for the opposite vehicle on the far adjacent opposite lane, the corresponding T n The values of (2) may be, but are not limited to, 1.2, 0.8, 0.4, 0.
The calculation formula of the steering probability is further optimized based on the collision risk coefficient, and the calculation formula of the steering probability of the vehicle to be steered based on the collision risk coefficient, the first fault tolerance coefficient and the first sample data comprises the following steps:
wherein T is n Lambda, n, ttc, as the collision risk coefficient of the opposing vehicle n n 、u n Sigma (sigma) n 2 The meaning of (a) is consistent with that described in the foregoing embodiments, and specific reference may be made to the foregoing embodiments, which are not repeated here.
The collision risk coefficient can be obtained according to the behavior data and habits of the user driving the vehicle at ordinary times. For example, for some users who are driving very cautiously at ordinary times, the value of the collision risk coefficient may be appropriately increased.
The optimized steering decision model changes the random variable ttc n Subject to mathematically expected positions by increasing the safe time T n The safety of the personnel on the autonomous vehicle can be further ensured.
For example, before optimizing the steering decision model, when the collision risk level of the adjacent opposite lane is 1-4, assuming that the critical time to collision TTC is 5 seconds, it may be determined that the vehicle is turning left; after adjustment, when the collision level of the adjacent opposite lane is 2, the critical time to collision TTC is 5.6 seconds, the left turn of the vehicle is determined, and when the collision level of the adjacent opposite lane is 1, the critical time to collision TTC is 5.9 seconds, the left turn of the vehicle is determined. Obviously, the optimized steering decision model can be adjusted according to the severity of the consequences caused by the collision of the vehicle, so that the driving safety is improved.
In step S203, detection data of the vehicle to be steered at the current intersection is acquired.
In this step, the detection data is information of an opposing vehicle of the vehicle to be steered at the current intersection, and the information of the opposing vehicle includes the number of vehicles of the opposing vehicle, the type of the vehicle, the speed of the vehicle, the position of the vehicle, and the acceleration information of the vehicle.
In step S204, the steering operation of the vehicle to be steered at the current intersection is controlled based on the detection data and the pre-trained steering decision model.
Step S204 includes sub-steps S2041, S2042, and S2043, and the specific manner of controlling the steering operation of the vehicle to be steered at the current intersection will be described in detail in the sub-steps of step S204. Referring to fig. 5, fig. 5 is a flowchart illustrating sub-steps of step S204 according to an exemplary embodiment of the present disclosure.
In sub-step S2041, information of a target oncoming vehicle of a vehicle to be steered at a current intersection is acquired.
The target counter vehicle is a vehicle which runs opposite to the vehicle to be steered and is nearest to the vehicle to be steered on each counter lane, and the information of the target counter vehicle includes the number of vehicles of the target counter vehicle, the type of the vehicles, the speed of the vehicles, the position of the vehicles and the acceleration information of the vehicles. Information of the target counter vehicle is obtained from the detection data acquired in step S203.
In sub-step S2042, a steering probability and a non-steering probability of the vehicle to be steered in the first direction are calculated using a steering decision model based on the information of the target oncoming vehicle.
The information of the target oncoming vehicle is input to any one of the trained steering decision models in step S202 to calculate the steering probability and the non-steering probability of the vehicle to be steered in the first direction, which may be the left-turning direction in one embodiment.
In sub-step S2043, the steering operation of the vehicle to be steered is controlled according to the steering probability and the non-steering probability of the vehicle to be steered.
When the steering probability of the vehicle to be steered is greater than the non-steering probability, steering the vehicle to be steered in a first direction, which may be a left-turning direction in one embodiment.
And when the steering probability of the vehicle to be steered is smaller than or equal to the non-steering probability, controlling the vehicle to be steered not to perform steering operation.
In summary, the method and the device for detecting the steering state of the vehicle can acquire detection data of the vehicle to be steered at the current intersection, the detection data comprise information of the opposite vehicle, steering operation of the vehicle to be steered at the current intersection is controlled based on the detection data and a pre-trained steering decision model, whether the steering operation of the vehicle to be steered can be effectively judged, and driving safety of an automatic driving system is improved. The boundary of the steering decision model is adjusted through the fault tolerance coefficient, so that the fault tolerance of the automatic driving system is improved; the collision risk coefficient is adjusted based on the type of the vehicle, so that the driving safety is improved.
Fig. 6 is a block diagram of a vehicle steering control apparatus according to an exemplary embodiment of the present disclosure. Referring to fig. 6, the apparatus 20 includes an acquisition module 203 and a processing module 205.
The obtaining module 203 is configured to obtain detection data of a vehicle to be steered at a current intersection, where the detection data includes information of an opposite vehicle of the vehicle to be steered at the current intersection, and the opposite vehicle is a vehicle that runs opposite to the vehicle to be steered on an opposite lane of the vehicle to be steered.
The processing module 205 is configured to control a steering operation of the vehicle to be steered at the current intersection based on the detection data and a pre-trained steering decision model; the steering decision model is trained based on a plurality of sets of sample data of sample vehicles at a target intersection, wherein the sample data comprises information of opposite vehicles of the sample vehicles at the target intersection.
Optionally, the obtaining module 203 is further configured to collect the multiple sets of sample data of the sample vehicle at the target intersection.
Optionally, the processing module 205 is further configured to train the steering decision model based on the plurality of sets of sample data.
Optionally, the processing module 205 includes:
and the time calculation sub-module is used for calculating the collision time of the opposite vehicle to the position of the sample vehicle based on the vehicle speed, the vehicle position and the vehicle acceleration information of the opposite vehicle.
And the model training sub-module is used for training a steering decision model based on the collision time.
Optionally, the model training sub-module is further configured to calculate a steering probability of the sample vehicle based on a first fault tolerance coefficient and the first sample data; the steering probability characterizes a probability that the sample vehicle can steer in the first direction; the first fault tolerance coefficient is an offset coefficient determined based on a training result of the first sample data;
the calculation formula of the steering probability comprises the following steps:
wherein lambda is the first fault tolerance coefficient and n is the number of vehicles; ttc n A collision time for the oncoming vehicle n to reach the current position of the sample vehicle in the first sample data; u (u) n The average value of collision time of the opposite vehicles on an opposite lane n in the first sample data is taken as the opposite lane n where the opposite vehicles n are located; sigma (sigma) n 2 The variance of collision time on the opposite lane n is the first sample data.
The model training sub-module is further used for calculating the non-steering probability of the sample vehicle based on a second fault tolerance coefficient and the second sample data; the non-steering probability characterizes a probability that the sample vehicle cannot steer in the first direction; the second fault tolerance coefficient is an offset coefficient determined based on a training result of the second sample data;
the calculation formula of the non-steering probability comprises the following steps:
wherein lambda' is the second fault tolerance coefficient and n is the number of vehicles; ttc n ' is the collision time of the oncoming vehicle n to the current position of the sample vehicle in the second sample data; u (u) n ' is an average value of collision times of the opposing vehicles on an opposing lane n, which is an opposing lane in which the opposing vehicle n is located, in the second sample data;and the variance of collision time on the opposite lane n in the second sample data is obtained.
And the model training sub-module is further configured to update the first fault-tolerant coefficient and the second fault-tolerant coefficient according to the steering probability and the non-steering probability, and execute the step of calculating the steering probability of the sample vehicle based on the first fault-tolerant coefficient and the first sample data and the step of calculating the non-steering probability of the sample vehicle based on the second fault-tolerant coefficient and the second sample data again with the updated first fault-tolerant coefficient and the updated second fault-tolerant coefficient until a preset training condition is satisfied, thereby obtaining the steering decision model.
Optionally, the model training sub-module is further configured to calculate a steering probability of the vehicle to be steered based on the collision risk coefficient, the first fault tolerance coefficient, and the first sample data; the steering probability characterizes a probability that the sample vehicle can steer in the first direction; the collision risk coefficient is a constant determined based on the vehicle type and the vehicle speed; the first fault tolerance coefficient is an offset coefficient determined based on a training result of the first sample data;
the calculation formula of the steering probability comprises the following steps:
wherein T is n Lambda is the first fault tolerance coefficient for the collision risk coefficient for the oncoming vehicle n, n being the number of vehicles; ttc n A collision time for the oncoming vehicle n to reach the current position of the sample vehicle in the first sample data; u (u) n The average value of collision time of the opposite vehicles on an opposite lane n in the first sample data is taken as the opposite lane n where the opposite vehicles n are located; sigma (sigma) n 2 The variance of collision time on the opposite lane n is the first sample data.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Optionally, the processing module 205 is further configured to obtain information of a target oncoming vehicle of the vehicle to be steered at the current intersection; the target opposite vehicle is a vehicle which runs opposite to the vehicle to be turned on an opposite lane of the vehicle to be turned, and the information of the target opposite vehicle comprises the number of vehicles, the type of the vehicles, the speed of the vehicles, the position of the vehicles and the acceleration information of the vehicles.
And the steering decision model is also used for calculating the steering probability and the non-steering probability of the vehicle to be steered in the first direction based on the information of the target opposite vehicle.
And the steering control module is also used for controlling the steering operation of the vehicle to be steered according to the steering probability and the non-steering probability of the vehicle to be steered.
Fig. 7 is a block diagram of a vehicle 1900, which may be an autonomous vehicle, shown in accordance with an exemplary embodiment of the present disclosure. Referring to fig. 7, a vehicle 1900 includes a processor 1922, which may be one or more in number, and a memory 1932 for storing computer programs executable by the processor 1922. The computer program stored in memory 1932 may include one or more modules each corresponding to a set of instructions. Further, the processor 1922 may be configured to execute the computer program to perform the vehicle steering control method described above.
Additionally, vehicle 1900 may further include a power component 1926 and a communication component 1950, where power component 1926 may be configured to perform power management of vehicle 1900, and communication component 1950 may be configured to enable communication of vehicle 1900, e.g., wired or wireless communication. In addition, the vehicle 1900 may also include an input/output (I/O) interface 1958. Vehicle 1900 may operate based on an operating system stored in memory 1932, such as Windows Server TM ，Mac OS X TM ，Unix TM ，Linux TM Etc.
In another exemplary embodiment, a computer readable storage medium is also provided that includes program instructions that, when executed by a processor, implement the steps of the vehicle steering control method described above. For example, the non-transitory computer readable storage medium may be the memory 1932 including program instructions described above that are executable by the processor 1922 of the vehicle 1900 to perform the vehicle steering control method described above.
In another exemplary embodiment, a computer program product is also provided, which comprises a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-mentioned vehicle steering control method when being executed by the programmable apparatus.
The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.
In addition, the specific features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, the present disclosure does not further describe various possible combinations.
Moreover, any combination between the various embodiments of the present disclosure is possible as long as it does not depart from the spirit of the present disclosure, which should also be construed as the disclosure of the present disclosure.
Claims (9)
1. A vehicle steering control method characterized by comprising:
acquiring detection data of a vehicle to be steered at a current intersection, wherein the detection data comprises information of an opposite vehicle of the vehicle to be steered at the current intersection, and the opposite vehicle is a vehicle which runs opposite to the vehicle to be steered and is nearest to the vehicle to be steered on an opposite lane of the vehicle to be steered;
controlling steering operation of the vehicle to be steered at the current intersection based on the detection data and a pre-trained steering decision model; the steering decision model is obtained by training based on a plurality of groups of sample data of sample vehicles at a target intersection, wherein the sample data comprises information of opposite vehicles of the sample vehicles at the target intersection;
the step of controlling the steering operation of the vehicle to be steered at the current intersection based on the detection data and a pre-trained steering decision model comprises the following steps:
acquiring information of a target opposite vehicle of the vehicle to be steered at the current intersection; the target opposite vehicle is a vehicle which runs opposite to the vehicle to be turned on an opposite lane of the vehicle to be turned, and the information of the target opposite vehicle comprises the number of vehicles, the type of the vehicles, the speed of the vehicles, the position of the vehicles and the acceleration information of the vehicles;
calculating the steering probability and the non-steering probability of the vehicle to be steered in the first direction by using the steering decision model based on the information of the target opposite vehicle;
and controlling the steering operation of the vehicle to be steered according to the steering probability and the non-steering probability of the vehicle to be steered.
2. The method according to claim 1, characterized in that before the acquisition of the detection data of the vehicle to be steered at the current intersection, the method comprises:
collecting the plurality of groups of sample data of the sample vehicles at the target intersection;
training the steering decision model based on the plurality of sets of sample data.
3. The method of claim 2, wherein the vehicle information of the opposing vehicle includes vehicle speed, vehicle position, vehicle acceleration information of the opposing vehicle;
the step of training the steering decision model based on the plurality of sets of sample data comprises:
calculating a collision time of the oncoming vehicle to the position of the sample vehicle based on the vehicle speed, the vehicle position and the vehicle acceleration information of the oncoming vehicle;
training a steering decision model based on the collision time.
4. The method of claim 3, wherein the sample data includes equal numbers of first sample data and second sample data, the first sample data being sample data in a scenario in which the sample vehicle is recommended to turn in a first direction, the second sample data being sample data in a scenario in which the sample vehicle is not recommended to turn in the first direction.
5. The method of claim 4, wherein the vehicle information of the opposing vehicle further includes a number of vehicles of the opposing vehicle;
the step of training a steering decision model based on the sample data comprises:
calculating a steering probability of the sample vehicle based on a first fault tolerance coefficient and the first sample data; the steering probability characterizes a probability that the sample vehicle can steer in the first direction; the first fault tolerance coefficient is an offset coefficient determined based on a training result of the first sample data;
the calculation formula of the steering probability comprises the following steps:
wherein lambda is the first fault tolerance coefficient and n is the number of vehicles; ttc n A collision time for the oncoming vehicle n to reach the current position of the sample vehicle in the first sample data; u (u) n The average value of collision time of the opposite vehicles on an opposite lane n in the first sample data is taken as the opposite lane n where the opposite vehicles n are located; sigma (sigma) n 2 A variance of collision time on the opposite lane n in the first sample data;
calculating a non-steering probability of the sample vehicle based on a second fault tolerance coefficient and the second sample data; the non-steering probability characterizes a probability that the sample vehicle cannot steer in the first direction; the second fault tolerance coefficient is an offset coefficient determined based on a training result of the second sample data;
the calculation formula of the non-steering probability comprises the following steps:
wherein lambda' is the second fault tolerance coefficient and n is the number of vehicles; ttc n ' is the collision time of the oncoming vehicle n to the current position of the sample vehicle in the second sample data; u (u) n ' is an average value of collision times of the opposing vehicles on an opposing lane n, which is an opposing lane in which the opposing vehicle n is located, in the second sample data;a variance of collision time on the opposite lane n in the second sample data;
and updating the first fault-tolerant coefficient and the second fault-tolerant coefficient according to the steering probability and the non-steering probability respectively, and executing the step of calculating the steering probability of the sample vehicle based on the first fault-tolerant coefficient and the first sample data and the step of calculating the non-steering probability of the sample vehicle based on the second fault-tolerant coefficient and the second sample data again according to the updated first fault-tolerant coefficient and the updated second fault-tolerant coefficient until a preset training condition is met, so as to obtain the steering decision model.
6. The method of claim 4, wherein the vehicle information of the opposing vehicle includes a number of vehicles and a type of vehicles of the opposing vehicle;
the step of training a steering decision model based on the sample data comprises:
calculating the steering probability of the vehicle to be steered based on the collision risk coefficient, the first fault tolerance coefficient and the first sample data; the steering probability characterizes a probability that the sample vehicle can steer in the first direction; the collision risk coefficient is a constant determined based on the vehicle type and the vehicle speed; the first fault tolerance coefficient is an offset coefficient determined based on a training result of the first sample data;
the calculation formula of the steering probability comprises the following steps:
wherein T is n Lambda is the first fault tolerance coefficient for the collision risk coefficient for the oncoming vehicle n, n being the number of vehicles; ttc n A collision time for the oncoming vehicle n to reach the current position of the sample vehicle in the first sample data; u (u) n The average value of collision time of the opposite vehicles on an opposite lane n in the first sample data is taken as the opposite lane n where the opposite vehicles n are located; sigma (sigma) n 2 A variance of collision time on the opposite lane n in the first sample data;
and updating the collision risk coefficient according to the steering probability, and executing the step of calculating the steering probability of the sample vehicle based on the collision risk coefficient, the first fault tolerance coefficient and the first sample data again according to the updated collision risk coefficient until a preset training condition is met, so as to obtain the steering decision model.
7. A steering control device for a vehicle is characterized in that,
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring detection data of a vehicle to be steered at a current intersection, the detection data comprise information of an opposite vehicle of the vehicle to be steered at the current intersection, and the opposite vehicle is a vehicle which runs opposite to the vehicle to be steered on an opposite lane of the vehicle to be steered;
the processing module is used for controlling the steering operation of the vehicle to be steered at the current intersection based on the detection data and a pre-trained steering decision model; the steering decision model is obtained by training based on a plurality of groups of sample data of sample vehicles at a target intersection, wherein the sample data comprises information of opposite vehicles of the sample vehicles at the target intersection;
the step of controlling the steering operation of the vehicle to be steered at the current intersection based on the detection data and a pre-trained steering decision model comprises the following steps:
acquiring information of a target opposite vehicle of the vehicle to be steered at the current intersection; the target opposite vehicle is a vehicle which runs opposite to the vehicle to be turned on an opposite lane of the vehicle to be turned, and the information of the target opposite vehicle comprises the number of vehicles, the type of the vehicles, the speed of the vehicles, the position of the vehicles and the acceleration information of the vehicles;
calculating the steering probability and the non-steering probability of the vehicle to be steered in the first direction by using the steering decision model based on the information of the target opposite vehicle;
and controlling the steering operation of the vehicle to be steered according to the steering probability and the non-steering probability of the vehicle to be steered.
8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor implements the steps of the vehicle steering control method of any one of claims 1 to 6.
9. A vehicle, characterized by comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the vehicle steering control method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111395577.3A CN114056425B (en) | 2021-11-23 | 2021-11-23 | Vehicle steering control method and device, vehicle and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111395577.3A CN114056425B (en) | 2021-11-23 | 2021-11-23 | Vehicle steering control method and device, vehicle and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114056425A CN114056425A (en) | 2022-02-18 |
| CN114056425B true CN114056425B (en) | 2023-07-18 |
Family
ID=80275436
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111395577.3A Active CN114056425B (en) | 2021-11-23 | 2021-11-23 | Vehicle steering control method and device, vehicle and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114056425B (en) |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4947028B2 (en) * | 2008-10-10 | 2012-06-06 | 株式会社デンソー | Driving support apparatus and driving support method for automobile |
| JP2014241036A (en) * | 2013-06-11 | 2014-12-25 | 三菱電機株式会社 | Vehicle driving assist system |
| EP3610343A4 (en) * | 2017-04-12 | 2021-01-13 | HRL Laboratories, LLC | Cognitive behavior prediction system for autonomous systems |
| US10019011B1 (en) * | 2017-10-09 | 2018-07-10 | Uber Technologies, Inc. | Autonomous vehicles featuring machine-learned yield model |
| US11749111B2 (en) * | 2018-03-19 | 2023-09-05 | Derq Inc. | Early warning and collision avoidance |
| TWI690440B (en) * | 2018-10-17 | 2020-04-11 | 財團法人車輛研究測試中心 | Intelligent driving method for passing intersections based on support vector machine and intelligent driving system thereof |
| CN109901574B (en) * | 2019-01-28 | 2021-08-13 | 华为技术有限公司 | Automatic driving method and device |
| CN110298122B (en) * | 2019-07-03 | 2021-04-27 | 北京理工大学 | Unmanned vehicle urban intersection left-turn decision-making method based on conflict resolution |
| CN110723207B (en) * | 2019-09-23 | 2021-08-03 | 江苏大学 | Intelligent automobile model prediction steering controller based on model reconstruction and control method thereof |
| CN112365710B (en) * | 2020-10-19 | 2021-09-24 | 南京航空航天大学 | Intelligent vehicle lane change decision-making method based on probability output model |
| CN113200086A (en) * | 2021-06-21 | 2021-08-03 | 盐城工学院 | Intelligent vehicle steering control system and control method thereof |
-
2021
- 2021-11-23 CN CN202111395577.3A patent/CN114056425B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN114056425A (en) | 2022-02-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3533681B1 (en) | Method for detecting safety of driving behavior, apparatus and storage medium | |
| US11940790B2 (en) | Safe hand-off between human driver and autonomous driving system | |
| CN110214107B (en) | Autonomous vehicle providing driver education | |
| CN116342655A (en) | Apparatus and method for online real-time multi-object tracking | |
| JP2019510677A (en) | Control data creation method for driver's rule-based assistance | |
| CN108944939B (en) | Method and system for providing driving directions | |
| Shin et al. | Randomized adversarial imitation learning for autonomous driving | |
| US11242050B2 (en) | Reinforcement learning with scene decomposition for navigating complex environments | |
| CN110473310A (en) | Running car data record method, system, equipment and storage medium | |
| US20180284763A1 (en) | Assistance for an autonomous vehicle using crowd-sourced responses | |
| US20220204020A1 (en) | Toward simulation of driver behavior in driving automation | |
| CN113581209B (en) | Driving assistance mode switching method, device, equipment and storage medium | |
| JP6924629B2 (en) | Automatic control device and its control method | |
| CN111666307A (en) | Unmanned safety judgment system for intuitive inference according to scene observation | |
| EP3915851B1 (en) | System and method for estimating take-over time | |
| CN113085873A (en) | Method and device for acquiring driving strategy, computer equipment and storage medium | |
| CN114502442A (en) | Method for operating a vehicle | |
| CN116331221A (en) | Driving assistance method, driving assistance device, electronic equipment and storage medium | |
| EP3751465A1 (en) | Methods, apparatuses and computer programs for generating a reinforcement learning-based machine-learning model and for generating a control signal for operating a vehicle | |
| US20210341885A1 (en) | Device and method for training a control strategy for a control device over several iterations | |
| CN114056425B (en) | Vehicle steering control method and device, vehicle and storage medium | |
| CN110932932A (en) | Reliability determination method for automatic driving cloud service platform and related device | |
| CN111583716A (en) | Vehicle obstacle avoidance method and device, electronic equipment and storage medium | |
| WO2023230740A1 (en) | Abnormal driving behavior identification method and device and vehicle | |
| CN116767237A (en) | Fraud detection for hands-on automated driving |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |