CN115240409A - Method for extracting dangerous scene based on driver model and traffic flow model - Google Patents
Method for extracting dangerous scene based on driver model and traffic flow model Download PDFInfo
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- CN115240409A CN115240409A CN202210684071.2A CN202210684071A CN115240409A CN 115240409 A CN115240409 A CN 115240409A CN 202210684071 A CN202210684071 A CN 202210684071A CN 115240409 A CN115240409 A CN 115240409A
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- 238000012544 monitoring process Methods 0.000 claims abstract description 7
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- 238000001514 detection method Methods 0.000 claims description 2
- 238000012360 testing method Methods 0.000 abstract description 21
- 206010039203 Road traffic accident Diseases 0.000 description 2
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- G—PHYSICS
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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Abstract
The invention discloses a method for extracting dangerous scenes based on a driver model and a traffic flow model, which solves the problems of high cost, long period, large safety risk and limited coverage working condition of the current road test for automatic driving, and the technical scheme is characterized by comprising the following steps: generating a random traffic flow in a virtual environment based on the established driver behavior model and the traffic flow model; monitoring the whole traffic flow; when the fact that vehicles collide or the distance between the vehicles is smaller than the set dangerous distance at the moment T is judged, in the time period from T-delta T to the moment T, relevant information of the corresponding vehicles and other involved traffic participants is extracted, static environment information relevant to the track is extracted, and a dangerous scene is formed; the method for extracting the dangerous scene based on the driver model and the traffic flow model can quickly and efficiently reproduce various dangerous working conditions, can greatly improve the testing efficiency, improve the testing safety and reduce the testing cost.
Description
Technical Field
The invention relates to an automatic driving automobile testing technology, in particular to a method for extracting dangerous scenes based on a driver model and a traffic flow model.
Background
At present, the research and development and the test development of the domestic automatic driving are very rapid, and in the research and development process of the automatic driving automobile, the function and the performance of an automatic driving system need to be checked through the test, so that developers can be helped to find the defects or shortcomings of the system function and carry out targeted improvement and optimization on the defects or the shortcomings. These tests need to be able to cover the natural driving scenario as well as the possible accident situation during everyday driving.
At present, automatic driving manufacturers mainly adopt a road testing method for testing, and the testing method has the disadvantages of high testing cost, long period, high safety risk, limited covered dangerous working conditions and room for improvement.
Disclosure of Invention
The invention aims to provide a method for extracting dangerous scenes based on a driver model and a traffic flow model, which can quickly and efficiently reproduce various dangerous working conditions, can greatly improve the testing efficiency, improve the testing safety and reduce the testing cost.
The technical purpose of the invention is realized by the following technical scheme:
a method for extracting dangerous scenes based on a driver model and a traffic flow model comprises the following steps:
generating a random traffic flow in a virtual environment based on the established driver behavior model and the traffic flow model;
monitoring the whole traffic flow;
judging whether vehicles collide or whether the distance between the vehicles is smaller than a set dangerous distance at the moment T; if not, continuing monitoring;
if so, in the time period from T-delta T to T, extracting traffic flow related information and driver behavior related information of corresponding vehicles with dangers and other involved traffic participants, and simultaneously extracting static environment information related to the track to form a dangerous scene.
In conclusion, the invention has the following beneficial effects:
through a driver behavior model and a traffic flow model, a traffic flow is randomly generated, the traffic condition of the real world can be reflected to the maximum extent, dangerous scenes such as traffic accidents possibly generated in the real world are simulated and covered, a dangerous scene can be correspondingly obtained through extracting and collecting data such as parameters and the like when an accident occurs in a virtual environment, even some marginal scenes and extreme scenes with extremely low occurrence probability in the real world can be generated, through the large-scale arrangement of the random traffic flow model, a large number of dangerous scenes, marginal scenes and extreme scenes can be rapidly obtained, the obtained scenes have certain authenticity, and the effectiveness of the scenes can be guaranteed. Various dangerous working conditions can be rapidly and efficiently reproduced through the simulation test method, the test efficiency can be greatly improved, the test safety is improved, and the test cost is reduced.
Drawings
FIG. 1 is a schematic block diagram of the process of the present method
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
According to one or more embodiments, a method for extracting dangerous scenes based on a driver model and a traffic flow model is disclosed, which comprises the following steps:
s1, generating a random traffic flow in a virtual environment based on the established driver behavior model and the established traffic flow model.
The driver behavior model includes a plurality of dimensions, including and not limited to:
desired speed fluctuation value-fluctuation range of response to desired speed, such as + -10%, range adjustable;
desired add/deceleration fluctuation value-a fluctuation range, e.g., ± 10%, in response to a desired add/deceleration;
the invisible speed limit degree-the invisible probability of the maximum speed given by traffic signs such as speed limit boards, etc., between 0 and 1, 0 represents complete compliance, 0.5 represents 50% of possible compliance, and 1 represents complete invisibility and noncompliance;
expected vehicle distance fluctuation value-fluctuation range of time distance between vehicles expected to be maintained, such as +/-20%, and adjustable range;
degree of trajectory/lane keeping deviation-cycle time to deviate from planned trajectory or lane line of the road and maximum deviation;
fluctuation value of the expected lane-changing transverse speed-fluctuation range of response to the expected lane-changing transverse speed, such as +/-10%, and adjustable range;
the overtaking intention: the relationship between the ratio of the speed of the front vehicle to the desired speed of the vehicle and the possibility of overtaking is similar to a two-dimensional curve, the horizontal axis represents the ratio of the speed of the front vehicle to the desired speed of the vehicle, the vertical axis represents the possibility of overtaking, if the ratio of the speed of the front vehicle to the desired speed of the vehicle is 0.8, the possibility of overtaking is 80%, the curve can be set according to the situation, if the curve is set to be a linear relationship, the greater the ratio of the speed of the front vehicle to the desired speed of the vehicle is, the less the possibility of overtaking is;
compliance with traffic light/traffic sign intentions: if 50% -100%,50% represents 50% of the possible compliance after meeting the traffic light, 100% represents the positive compliance, and the parameter range is adjustable;
willingness to turn on the turn light: the possibility of turning on the turn signal during turning, such as 50% -100%;
the driver behavior model is obtained by collecting, counting and analyzing the behavior characteristics of drivers in the real world, and the randomness of each parameter in a certain range is set.
Traffic flow models contain common parameters including, and not limited to:
average traffic flow rate: the number of vehicles passing through a certain section in a specified road section within sampling time;
density of traffic flow: the number of vehicles present on a certain time unit road length;
average vehicle speed: an average value of the travel distance of the vehicle in the detected section per unit time;
average occupancy: the time sum of all vehicles driving into the detection road section occupying the traffic flow data sensor is compared with the up-sampling time;
vehicle head spacing: the distance between the front and rear vehicle heads;
the headway is as follows: dividing the distance between the front vehicle head and the rear vehicle head by the time obtained by the rear vehicle speed;
the traffic flow model comprises the distribution conditions of the parameters related to time and space, the distribution conditions are obtained by collecting, counting and analyzing traffic data of certain roads in the real world, a certain range of randomness is set for each parameter, and each vehicle in the traffic flow model has a certain automatic driving function.
And S2, monitoring the whole traffic flow.
S3, judging whether vehicles collide or whether the distance between the vehicles is smaller than a set dangerous distance at the moment T; if not, continuing monitoring.
And S4, if the vehicle collision occurs or the distance between the vehicles is smaller than the set dangerous distance, extracting traffic flow related information and driver behavior related information of the corresponding vehicle with the danger and other involved traffic participants in a time period from T-delta T to T, and simultaneously extracting static environment information related to the track to form a dangerous scene.
For example, in the time period from the time point T- Δ T to the time point T, the traveling tracks, the speeds, the accelerated speed heading directions and other related information of the vehicles a and B and related other traffic participants are extracted, and static environment information such as road information, traffic sign information, weather information, illumination information and the like related to the tracks is extracted, wherein the road information includes but is not limited to lane line types, lane numbers, gradients, road surface conditions and the like, the traffic sign information includes traffic lights, ground marks, traffic sign information, weather information includes sunny days, rainy days, snowy days, fog, haze and the like, and the illumination information includes day, evening, night, backlight, glare and the like. The information is assembled together to form a dangerous scene.
The driver behavior model and the traffic flow model are obtained by statistics and analysis according to real driving/traffic conditions of the real world, the models have reality, and certain randomness is set at the same time, the random traffic flow generated by the mode can reflect the traffic conditions of the real world to the maximum extent, dangerous scenes such as traffic accidents possibly generated in the real world can be covered, and even some marginal scenes and extreme scenes with extremely low probability of occurrence in the real world can be generated. By arranging the random traffic flow model in a large scale, a large number of dangerous scenes, marginal scenes and extreme scenes can be rapidly acquired, the acquired scenes have certain authenticity, and the validity of the scenes can be ensured. By the simulation test method, various dangerous working conditions can be rapidly and efficiently reproduced, the test efficiency can be greatly improved, the test safety is improved, and the test cost is reduced.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications without inventive contribution to the present embodiment as required after reading the present specification, but all of them are protected by patent law within the scope of the present invention.
Claims (5)
1. A method for extracting dangerous scenes based on a driver model and a traffic flow model is characterized by comprising the following steps:
generating a random traffic flow in a virtual environment based on the established driver behavior model and the traffic flow model;
monitoring the whole traffic flow;
judging whether vehicles collide or whether the distance between the vehicles is smaller than a set dangerous distance at the moment T; if not, continuing monitoring;
if the vehicle is dangerous, in the time period from T-delta T to T, extracting traffic flow related information and driver behavior related information of corresponding vehicles with dangers and other involved traffic participants, and extracting static environment information related to the track to form a dangerous scene.
2. The method for extracting dangerous scene based on driver model and traffic flow model according to claim 1, wherein:
acquiring, counting and analyzing the behavior characteristics of a driver in the real world, setting randomness of a certain range for each dimension parameter, and generating a driver behavior model;
collecting, counting and analyzing the distribution condition of common parameter data of real traffic flow with respect to time and space, setting randomness of a certain range for each parameter, and generating a traffic flow model;
and randomly generating a traffic flow in the virtual environment according to the set parameters.
3. The method for extracting dangerous scene based on driver model and traffic flow model according to claim 2, wherein the driver behavior model comprises the following multidimensional information:
desired speed fluctuation value-fluctuation range in response to desired speed, the fluctuation range being adjusted according to the setting;
desired add/deceleration fluctuation value-fluctuation range in response to desired add/deceleration, the fluctuation range being adjusted according to the setting;
the invisible probability of the maximum speed given by the invisible speed-limiting traffic sign is set to be between 0 and 1, 0 represents complete compliance, 0.5 represents 50% of possible compliance, and 1 represents complete invisibility and noncompliance;
expected vehicle distance fluctuation value-fluctuation range of time distance between vehicles expected to be maintained, and the fluctuation range is adjusted according to setting;
degree of trajectory/lane keeping deviation-cycle time to deviate from planned trajectory or lane line of the road and maximum deviation;
fluctuation value of the desired lane-change lateral velocity-fluctuation range in response to the desired lane-change lateral velocity, the fluctuation range being adjusted according to the setting;
the overtaking intention: a relationship between a ratio between a preceding vehicle speed and a desired vehicle speed of the host vehicle and a possibility of passing;
to comply with traffic light/traffic sign wishes and with turn light wishes.
4. The method for extracting dangerous scenes based on the driver model and the traffic flow model according to claim 2, wherein the traffic flow model setting comprises the following parameter information:
average traffic flow: the number of vehicles passing through a certain section in a specified road section in sampling time;
the traffic density: the number of vehicles present on a certain time unit road length;
average vehicle speed: an average value of travel distances of the vehicle in the detected section per unit time;
average occupancy: the time sum of all vehicles driving into the detection road section occupying the traffic flow data sensor is compared with the up-sampling time;
the distance between the car heads: the distance between the front and rear vehicle heads;
the headway is as follows: and dividing the distance between the front vehicle and the rear vehicle by the time obtained by the speed of the rear vehicle.
5. The method for extracting dangerous scene based on driver model and traffic flow model according to claim 1, wherein the static environment information comprises:
traffic sign information including traffic lights, ground signs, traffic sign information;
weather information including sunny days, rainy days, snowy days, foggy days and haze days;
the illumination information comprises day illumination, evening illumination, night illumination, backlight and dazzling light.
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