CN111409648B - Driving behavior analysis method and device - Google Patents
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Abstract
The invention provides a driving behavior analysis method and a driving behavior analysis device, wherein the method comprises the following steps: acquiring driving behavior data of a driver aiming at a preset simulated traffic scene; processing the driving behavior data to obtain key index data of a simulated traffic scene; constructing a driver simulation model according to the key index data; and (4) performing anthropomorphic processing on the automatic driving algorithm to be analyzed by utilizing the driver simulation model. Based on the method disclosed by the invention, the automatic driving algorithm can be learned and the human driving behavior can be simulated as much as possible, thereby being beneficial to improving the safety of the automatic driving automobile and being popularized comprehensively.
Description
Technical Field
The invention relates to the technical field of automatic driving automobiles, in particular to a driving behavior analysis method and device.
Background
With the rapid development of automatic driving technology, the proportion of automobiles with different degrees of automation on public roads is higher and higher.
Since automotive automation is a slow, evolving process, traffic environments will be a mixture of human drivers and autonomous automobiles for a long period of time. Subtle driving behavior patterns have been developed among human drivers and can be understood by each other in the interaction. However, due to the limitation of the working mode, the way of observation, understanding and communication of human beings is not directly accepted by the computer. Therefore, how to safely adapt the automatic driving automobile to the traffic environment with the mixture of human drivers and automatic driving automobiles is a problem which needs to be solved urgently at the present stage.
Disclosure of Invention
In view of the above, the present invention provides a driving behavior analysis method and device to solve the above problems. The technical scheme is as follows:
a driving behavior analysis method, comprising:
acquiring driving behavior data of a driver aiming at a preset simulated traffic scene;
processing the driving behavior data to obtain key index data of the simulated traffic scene;
constructing a driver simulation model according to the key index data;
carrying out personification processing on an automatic driving algorithm to be analyzed by utilizing the driver simulation model; wherein,
the acquiring of the driving behavior data of the driver for the preset simulated traffic scene includes:
determining a simulated traffic scene designated by a driver based on a preset environment interactive interface;
activating a preset cockpit simulation device;
and collecting driving behavior data generated by the cockpit device.
Preferably, the constructing a driver simulation model according to the key index data includes:
determining following behavior data based on the key index data, and processing the following behavior data to obtain a following behavior model; and/or
And determining lane changing behavior data based on the key index data, and processing the lane changing behavior data to obtain a lane changing behavior model.
Preferably, the method further comprises:
and constructing a traffic environment simulation model based on the driver simulation model.
Preferably, the method further comprises:
and testing the automatic driving algorithm after anthropomorphic processing by using the traffic environment simulation model.
A driving behavior analysis apparatus comprising:
the acquisition module is used for acquiring driving behavior data of a driver aiming at a preset simulated traffic scene;
the first processing module is used for processing the driving behavior data to obtain key index data of the simulated traffic scene;
the construction module is used for constructing a driver simulation model according to the key index data;
the second processing module is used for carrying out anthropomorphic processing on the automatic driving algorithm to be analyzed by utilizing the driver simulation model; wherein,
the acquisition module is specifically configured to: determining a simulated traffic scene designated by a driver based on a preset environment interactive interface; activating a preset cockpit simulation device; and collecting driving behavior data generated by the cockpit device.
Preferably, the building block is specifically configured to:
determining following behavior data based on the key index data, and processing the following behavior data to obtain a following behavior model; and/or determining lane changing behavior data based on the key index data, and processing the lane changing behavior data to obtain a lane changing behavior model.
Preferably, the building module is further configured to:
and constructing a traffic environment simulation model based on the driver simulation model.
Preferably, the second processing module is further configured to:
and testing the automatic driving algorithm after anthropomorphic processing by using the traffic environment simulation model.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a driving behavior analysis method and a driving behavior analysis device, wherein a driver simulation model can be constructed on the basis of driving behavior data of a driver aiming at a simulated traffic scene, and then the driver simulation model is utilized to carry out anthropomorphic processing on an automatic driving calculation algorithm to be analyzed. Based on the method disclosed by the invention, the automatic driving algorithm can be learned and the human driving behavior can be simulated as much as possible, thereby being beneficial to improving the safety of the automatic driving automobile and being popularized comprehensively.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a method flow diagram of a driving behavior analysis method provided by an embodiment of the present invention;
FIG. 2 is an example of a simulated traffic scene;
FIG. 3 is a flow chart of another method of driving behavior analysis provided by an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a driving behavior analysis apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a driving behavior analysis method, wherein a flow chart of the method is shown in figure 1, and the method comprises the following steps:
and S10, acquiring driving behavior data of the driver aiming at the preset simulated traffic scene.
In the process of executing step S10, the driving behavior data of the driver for the designated simulated traffic scene may be acquired based on the preset environment interactive interface and the preset cockpit simulation device. The method specifically comprises the steps of determining a simulated traffic scene appointed by a driver on a preset environment interactive interface, further activating a cockpit simulation device, and collecting driving behavior data generated by the cockpit device. The simulated traffic scene is a virtual traffic environment which is created in advance and comprises virtual road conditions, virtual motion states of traffic participants, virtual weather conditions and the like.
All vehicles and road environments in the simulated traffic scene are virtual mathematical models, and the vehicles can be divided into two types: the first category is vehicles controlled by a human driver through a cockpit simulator (a "master" simulating brake avoidance in the traffic scene example, i.e., a right-hand vehicle, as shown in fig. 2); the second category is vehicles controlled by non-human drivers (a "leading vehicle" simulating the braking deceleration in the traffic scenario example, i.e., a left-hand vehicle, as shown in FIG. 2). Wherein,
for the first type of vehicle, the driver sends real control signals, such as actual steering actions of an accelerator, braking, steering and the like, to the vehicle model through the cockpit simulation device, so that the following, accelerating, braking and other actions of the vehicle are correspondingly controlled. The cockpit simulation device is provided with a data acquisition sensor arranged at the vehicle control mechanism besides basic vehicle control mechanisms (such as an accelerator pedal, a brake pedal, a steering wheel, a gear shift lever, various driving function related shifting pieces and the like) arranged in a real cockpit, for example, an accelerator pedal position sensor is arranged at the accelerator pedal, and can sense the accelerator opening degree controlled by a driver in real time.
For the second type of vehicles, it can simulate the driving behavior of human drivers, including the perception of the environment, such as recognizing other vehicles within a certain range, recognizing other traffic participants such as pedestrians, and judging their motion states, such as speed, direction, relative distance, etc., so as to issue control commands to reasonably control speed, acceleration, etc., thereby realizing virtual control and simulating a traffic environment close to reality.
Taking the "following" behavior in the driving behavior as an example, the driving behavior data are shown in table 1, the data in the table are all time series in the driving process, the sampling frequency is f (hz), and correspondingly, the sampling period is Δ t ═ 1/f(s).
TABLE 1
It should be noted that the driving behavior data collected in the present embodiment is only illustrated in table 1, and more other driving behavior data types can be obtained in different driving behaviors from the following vehicle, and meanwhile, the objects of data generation are not limited to the "preceding vehicle" and the "main vehicle" listed in table 2. In practical applications, it is also possible to involve a plurality of "environmental vehicles", and a plurality of "host vehicles", in which case the data type of the driving behavior data is collected, with a corresponding increase in the amount of data.
It should be further noted that the collected driving behavior data may be stored according to a multidimensional logical structure such as a time sequence, a driver number sequence, a scene sequence, and the like.
And S20, processing the driving behavior data to obtain key index data of the simulated traffic scene.
In the process of executing step S20, different key indicators may be set for different driving behaviors in different simulated traffic scenarios.
Continuing with the "following" behavior in driving behavior as an example, the key indicators shown in table 2 are calculated based on the driving behavior data, and when a specific event occurs (e.g., sudden deceleration of the preceding vehicle, and emergency braking collision avoidance of the host vehicle), the behavior characteristic parameters of the human driver of the host vehicle are controlled.
It should be noted that the driving behavior category covered by the simulated traffic scene includes various behaviors that may occur in the actual driving process, such as following, changing lanes, overtaking, turning, avoiding pedestrians, and the like.
TABLE 2
The key indicators defined in table 2 are calculated as shown in table 3. The coincidence and operator referred to in table 3 is explained as follows: time refers to the time instant, t refers to t time step, Δ t refers to the time step (sampling period). The operator | means "when the condition is satisfied", max () means to find the maximum value, min () means to find the minimum value, and | | means to take the absolute value.
TABLE 3
And S30, constructing a driver simulation model according to the key index data.
In performing step S30, the driver simulation model may be directed to following and lane changing behavior. While other behaviors are, in essence, a combination of the two decomposition behaviors. For example, overtaking refers to the situation that a vehicle firstly follows the current lane, then changes the lane and then follows the front vehicle of the target lane; for another example, the vehicle enters a ramp and exits at a high speed, and basically changes the ramp and follows the vehicle.
And aiming at the car following model corresponding to the car following behavior, processing the car following behavior data in the key index data by adopting an acceleration control method to obtain the car following behavior data. The acceleration control method specifically comprises the following steps:
wherein, the definition and unit of each parameter are shown in the following table 4:
| (symbol) | definition of | Unit of |
| x | Position of | m |
| V | Speed of rotation | m/s |
| V0 | Desired speed | m/s |
| T | Expected safe headway | s |
| a | Maximum acceleration | m/s2 |
| b | Comfortable acceleration | m/s2 |
| δ | Index of acceleration | \ |
| s0 | Stopping distance | m |
TABLE 4
The parameters in table 4 will be calibrated by the driving behavior data to obtain a model that is as close as possible to the real driving behavior of humans.
For the lane change model corresponding to the lane change behavior, the lane change behavior data in the key index data can be processed by adopting a 'condition-motivation-execution' control method to obtain the lane change behavior data.
According to the actual driving behavior characteristics of the human driver on the road, the lane changing habit of the human driver is divided into four types: the method comprises the following steps of target vehicle speed driving, vehicle speed keeping + overtaking driving, lane keeping driving and maximum vehicle speed driving.
Target vehicle speed drive, this type of driver, will select a certain desired speed and maintain this speed as the primary driving target. If the driving environment causes their actual driving speed to be lower than desired, they will grasp all the opportunities for passing as much as possible and will not consider the right-hand principle when making lane-change decisions. They will not actively switch lanes if there are no conditions that would cause them to slow down.
Vehicle speed hold + passing drive, similar to the previous type, such drivers would also set a desired speed, but with the difference that they would implement the right-hand principle, using the left-hand passing lane only when passing, and returning to the right-hand lane after passing is complete.
Lane keeping drives, which are drivers of this type tend to stay in the current lane, and they will adapt as much as possible to the speed of the vehicle ahead of the current lane. Since they are primarily aimed at "keeping the lane", they differ from the first two types of drivers primarily in that they do not have a desired vehicle speed, or in other words, they can tolerate a wider range of vehicle speeds than the first two drivers.
Maximum vehicle speed drive, which is similar to the third type in that this type of driver does not set a desired speed either, but drives at the maximum driving speed that the current driving conditions can meet as much as possible. A lane change feature for such drivers is therefore that the passing is performed to achieve the maximum vehicle speed possible, whatever condition is met or that is available to achieve speed advantage.
In the whole process of executing lane changing, firstly, a driver can continuously observe the driving environment where the driver is currently located, and compare and evaluate the driving environment with the expectation of the driver to obtain ' lane changing motivation ' with different degrees, wherein the ' lane changing motivation ' is divided into three different degree grades, the first grade is ' unnecessary ', when the driver evaluates that the driver does not need ', the driver keeps driving on the current lane, the second grade is ' general need ', when the evaluation result falls on the grade, the driver can start to search for lane changing opportunities, if appropriate lane changing opportunities which are safe enough exist, lane changing is executed, otherwise, the current lane is kept, and the ' lane changing motivation ' is evaluated again; the third level is 'strong need', under the condition of falling into the first level, the driver has urgent need to change the lane, the driver can search for the lane changing opportunity, if safe, the lane changing is executed, if not, the driver waits for the opportunity or creates the opportunity (such as deceleration or acceleration) until the lane changing condition is met, and then the lane changing is executed.
For the lane change model, another dimension, namely a road condition factor, influencing the lane change motivation needs to be considered. The conditions that the driver is confronted with to influence his "lane change motivation" are divided into 7 categories, each as follows:
preparing for turning: it is necessary to turn after a certain distance and not now on the right turning lane.
And (3) lane ending: the lane in which the vehicle is currently located will end up within a certain distance.
Borrowing lanes: for temporary reasons, it is temporarily in the wrong lane, e.g., after a right turn, it is temporarily in the right lane, but the next intersection needs to go straight.
Accidental disorders: the driver encounters unexpected obstacles in front of the lane where the driver is.
Speed advantage: the lane change can improve the vehicle speed.
Queuing advantages: when traffic jams or waiting for signal lamps, the lane change can make the user queue more ahead.
Separation from congestion dominance: when congestion is sent ahead, a lane change may take himself out of the congested road segment more quickly, e.g., when the driver notices that congestion is due to a vehicle merging on the right side of the front, he tends to change the lane to the leftmost lane ahead, since this helps to reduce his time on the congested road segment.
Wherein, the corresponding lane change requirement in the first four cases is 'strong need', and the corresponding lane change requirement in the last three cases is 'general need'.
And S40, performing anthropomorphic processing on the automatic driving algorithm to be analyzed by using the driver simulation model.
In the process of executing step S40, the personification processing is to perform statistics on the driving behavior of the human driver for a specific control parameter, such as the following distance, and the human driver is accustomed to adopt the following distance f (v) at different vehicle speeds v, so that the following distance f (v) is used as a reference to adjust the "following distance" defined in the intelligent vehicle control algorithm.
It should be noted that the above-discussed "personification processing" is only applied to "non-emergency scenarios" or is limited to scenarios that can be handled by an excellent human driver, that is, this part is only used to evaluate "comfort" and "efficiency" of the smart vehicle under the premise of ensuring "safety", while another part of scenarios is not able to be handled by the human driver, or the emergency degree of the smart vehicle cannot consider "comfort" and "efficiency", and under these extreme scenarios, it is only necessary to directly use how much collision speed is controlled as a test index in order to avoid collision in time and under the situation that cannot be avoided. In fact, the injury is reduced to the maximum extent.
It should be noted that, if the construction of the driver simulation model is completed, the iterative test may be performed on the driver simulation model according to the key index data, so as to optimize the driver simulation model, where the first-class vehicle is a vehicle controlled by the driver simulation model. Further, the first type of vehicle may be a vehicle controlled by a specified control algorithm when constructing the driver simulation model.
It should be further noted that, in the process of optimizing the driver simulation model, the driver simulation model of each lane change habit appears with a certain probability, and at this time, a traffic environment model is formed. Through the continuous iterative loop test of the human driver in the whole system, the driver simulation model is closer and closer to the driving behavior of the human driver, and the human driver is in a more and more real and complex traffic environment model. Thereby completing the learning evolution of the driver model and the traffic environment model.
In some other embodiments, in order to test and evaluate the performance of the automatic driving algorithm in real traffic environment, on the basis of the driving behavior analysis shown in fig. 1, the driving behavior analysis method further includes the following steps, and the flow chart of the method is shown in fig. 3:
and S50, constructing a traffic environment simulation model based on the driver simulation model.
In the embodiment, the traffic environment simulation model can be obtained by merging the driver simulation model into the simulated traffic scene, and a foundation is laid for subsequent simulation tests.
And S60, testing the automatic driving algorithm after anthropomorphic processing by using the traffic environment simulation model.
In executing step S60, the auto-drive vehicle algorithm may be incorporated into the traffic environment simulation model. The method partially replaces the real vehicle test on public roads under the conditions of safety and high efficiency.
The driving behavior analysis method provided by the embodiment of the invention can be used for constructing a driver simulation model aiming at the driving behavior data of the simulated traffic scene based on the driver, and further carrying out anthropomorphic processing on the automatic driving calculation algorithm to be analyzed by utilizing the driver simulation model. Based on the method disclosed by the invention, the automatic driving algorithm can be learned and the human driving behavior can be simulated as much as possible, thereby being beneficial to improving the safety of the automatic driving automobile and being popularized comprehensively.
Based on the driving behavior analysis method provided by the above embodiment, an embodiment of the present invention correspondingly provides a driving behavior analysis device, a schematic structural diagram of which is shown in fig. 4, and the driving behavior analysis device includes:
the acquisition module 10 is configured to acquire driving behavior data of a driver for a preset simulated traffic scene;
the first processing module 20 is configured to process the driving behavior data to obtain key index data of a simulated traffic scene;
the construction module 30 is used for constructing a driver simulation model according to the key index data;
the second processing module 40 is used for performing anthropomorphic processing on the automatic driving algorithm to be analyzed by utilizing the driver simulation model; wherein,
the obtaining module 10 is specifically configured to: determining a simulated traffic scene designated by a driver based on a preset environment interactive interface; activating a preset cockpit simulation device; driving behavior data generated by the cockpit device is collected.
Preferably, the building block 30 is specifically configured to:
determining following behavior data based on the key index data, and processing the following behavior data to obtain a following behavior model; and/or determining lane changing behavior data based on the key index data, and processing the lane changing behavior data to obtain a lane changing behavior model.
Preferably, module 30 is also configured to:
and constructing a traffic environment simulation model based on the driver simulation model.
Preferably, the second processing module 40 is further configured to:
and testing the automatic driving algorithm after anthropomorphic processing by using a traffic environment simulation model.
The driving behavior analysis device provided by the embodiment of the invention can construct a driver simulation model aiming at the driving behavior data of the simulated traffic scene based on the driver, and further perform anthropomorphic processing on the automatic driving calculation algorithm to be analyzed by utilizing the driver simulation model. Based on the device disclosed by the invention, the automatic driving algorithm can be learned and the human driving behavior can be simulated as much as possible, so that the safety of an automatic driving automobile is improved and the automatic driving automobile is popularized comprehensively.
The driving behavior analysis method and device provided by the invention are described in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include or include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A driving behavior analysis method, characterized by comprising:
acquiring driving behavior data of a driver aiming at a preset simulated traffic scene; wherein, the vehicles in the simulated traffic scene are divided into two types: the first type is a vehicle controlled by a human driver through a cockpit simulation device, and the second type is a vehicle controlled by a non-human driver;
processing the driving behavior data to obtain key index data of the simulated traffic scene;
constructing a driver simulation model according to the key index data;
carrying out personification processing on an automatic driving algorithm to be analyzed by utilizing the driver simulation model; wherein,
the acquiring of the driving behavior data of the driver for the preset simulated traffic scene includes:
determining a simulated traffic scene designated by a driver based on a preset environment interactive interface;
activating a preset cockpit simulation device;
and collecting driving behavior data generated by the cockpit simulation device.
2. The method of claim 1, wherein said building a driver simulation model from said key indicator data comprises:
determining following behavior data based on the key index data, and processing the following behavior data to obtain a following behavior model; and/or
And determining lane changing behavior data based on the key index data, and processing the lane changing behavior data to obtain a lane changing behavior model.
3. The method of claim 1, further comprising:
and constructing a traffic environment simulation model based on the driver simulation model.
4. The method of claim 3, further comprising:
and testing the automatic driving algorithm after anthropomorphic processing by using the traffic environment simulation model.
5. A driving behavior analysis device characterized by comprising:
the acquisition module is used for acquiring driving behavior data of a driver aiming at a preset simulated traffic scene; wherein, the vehicles in the simulated traffic scene are divided into two types: the first type is a vehicle controlled by a human driver through a cockpit simulation device, and the second type is a vehicle controlled by a non-human driver;
the first processing module is used for processing the driving behavior data to obtain key index data of the simulated traffic scene;
the construction module is used for constructing a driver simulation model according to the key index data;
the second processing module is used for carrying out anthropomorphic processing on the automatic driving algorithm to be analyzed by utilizing the driver simulation model; wherein,
the acquisition module is specifically configured to: determining a simulated traffic scene designated by a driver based on a preset environment interactive interface; activating a preset cockpit simulation device; and collecting driving behavior data generated by the cockpit simulation device.
6. The apparatus according to claim 5, wherein the building block is specifically configured to:
determining following behavior data based on the key index data, and processing the following behavior data to obtain a following behavior model; and/or determining lane changing behavior data based on the key index data, and processing the lane changing behavior data to obtain a lane changing behavior model.
7. The apparatus of claim 5, wherein the build module is further configured to:
and constructing a traffic environment simulation model based on the driver simulation model.
8. The apparatus of claim 7, wherein the second processing module is further configured to:
and testing the automatic driving algorithm after anthropomorphic processing by using the traffic environment simulation model.
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