CN108944943B - Bend following model based on risk dynamic balance theory - Google Patents
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/14—Adaptive cruise control
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
A curve car following model based on a risk dynamic balance theory is used for solving the problem of safe car following in the curve driving process. According to the technical scheme, on the basis of an expected safety margin following model, the acceptable level of danger of a driver in a following process is considered, the safety margin change rule of a curve driver is explored, curve state parameter vectors are represented, and a curve following behavior compensation model is constructed. The curve behavior compensation model enables the curve following control to have similarity with the operation of a driver, improves the acceptable degree of the driver and the comfort of vehicle passengers, and provides technical support for safety control in the fields of adaptive cruise control, automatic driving and the like of a vehicle.
Description
Technical Field
The invention relates to a method used in the technical field of traffic safety and vehicle engineering, in particular to a method for analyzing curve driving characteristics in the curve following process of a vehicle based on a risk dynamic balance theory, and provides a curve following behavior compensation model on the basis of an expected safety margin model to provide a theoretical basis for curve control.
Background
With the rapid increase of the automobile holding capacity, an increasingly serious traffic problem is caused, and a driving assistance system becomes a hot spot of current research depending on the rapid development of technologies such as communication, sensing and the like. An Adaptive Cruise Control (ACC) System is one of important components of a driving assistance System. The adaptive cruise control system can effectively reduce the working load and fatigue strength of a driver in the driving process, improve the driving comfort of the driver and obviously improve the safety performance of a vehicle, and the ACC is more and more widely applied in the market at present. The ACC system has a good application effect on a horizontal straight road, but easily generates negative effects under the working condition of a curve. When a front vehicle rapidly passes through a curved road, a rear vehicle equipped with an adaptive cruise control system can follow at a similar high speed so as to keep a following behavior under a curve working condition, and the rear vehicle is easy to fall into a dangerous condition due to the complexity of the curved road; the ACC system adopts a radar detection distance method to obtain the distance between two vehicles on a road, the distance is a straight line distance between the two vehicles and is suitable for a straight road working condition, the actual distance between the two vehicles in a curve working condition is larger than the straight line distance, errors exist between the two vehicles, the accuracy of an ACC distance control algorithm is reduced, and the following safety of the curve working condition is also influenced. For the curve working condition, the Zheng Y M et al provides a curve traffic flow model based on an optimal speed model, the model mainly considers the speed difference between the self vehicle and the front vehicle, and the characteristics of a driver are ignored. Zhang D et al proposed a driver behavior characteristic-based speed control algorithm for a curved road ACC, which provides a customized speed curve for each driver in the case of no curve in front of the vehicle, without studying the influence of the front and rear vehicles during following. Chu D et al proposes a curve speed model considering driving style and vehicle and road factors, and Deng Z et al proposes a curve safe speed model based on a driver behavior questionnaire, but the basic constraints of the two speed models are vehicle rollover and sideslip, more emphasis is placed on controlling the motion state of the vehicle, and dynamic perception adjustment of a driver in a driving environment is ignored. In 1982, Wilde proposed a theory of risk homeostasis, considering that drivers attempt to keep the risk level within a subjectively acceptable level during driving. A person has a subjective risk acceptance level that is the result of a person's desire to profit from their behaviour combined with the impact of that behaviour on health and safety. The theory of risk homeostasis considers that the driver attempts to keep the risk level within a subjectively acceptable level during driving. In driving, drivers constantly perceive and assess the degree of risk they are exposed to, compare the degree of risk with their own desired level, and try to reduce the difference between the two to zero.
Conventionally, a following vehicle model is used to describe the movement of a following vehicle following a preceding vehicle. The following model is widely applied to strategy analysis of an automatic cruise control system and an intelligent traffic system of a vehicle. The current curve following model focuses more on vehicle control, ignoring the driver's own behavior characteristics and the driver's perception of a hazardous environment. During a curve following, the magnitude of the risk perceived by the driver has a significant influence on the driver's operating behavior, or, in other words, the driver's operating decision on the vehicle movement is a function of the driver's perception of risk in a given environment. A Desired safety margin following model (Desired safety margin model) based on a risk dynamic balance theory can simulate the physiological and psychological characteristics of a driver through an acceleration and deceleration sensitivity coefficient, the reaction time of the driver and the upper and lower limits of the safety margin Desired by the driver, and can provide a new mode for revealing a following process. The DSM model is already applied to a rear-end collision prevention system of the internet vehicle as a vehicle following strategy. However, the model is applied to the situation of straight-road car following, and the special car following situation under the working condition of the curve is ignored, so that on the basis of the DSM model, the curve car following behavior compensation model is constructed, and the expected safety margin model is expanded to the curve car following.
Disclosure of Invention
Aiming at the defects of the technology, in order to better solve the problems, the invention provides a curve following model based on a risk dynamic balance theory. On the basis of the expected safety margin following model, the safety margin change rule of a curve driver is explored, and a curve following behavior compensation model is constructed. The model enables the curve following control to have similarity with the operation of a driver, and improves the acceptable degree of the driver and the comfort of vehicle passengers.
The invention is realized by the following technical scheme, and the specific steps are as follows:
(1) the traffic situation setting comprises a straight road and a curve road respectively, and the required driving situation is completed by two front vehicles and two rear vehicles which are provided with data acquisition equipment and vehicle-vehicle communication systems. The traffic flow is less, the environmental interference is less, the road surface is dry, and the weather is clear.
(2) The states of the front and rear vehicles at any time are acquired, wherein the states include time, course angle information, GPS coordinate information, speed, acceleration and the like.
(3) Selecting the parameter values of straight roads and curved roads, and selecting the values of the parameters according to the set traffic situation, wherein the values comprise the response time tau and the acceleration sensitivity coefficient α1Coefficient of sensitivity to deceleration α2Desired upper margin of safety SMnDH(t), desired safety margin lower limit SMnDLAnd (t) analyzing the difference of the straight-curve parameter values and summarizing the change rule of each parameter value under the curve working condition.
(4) According to the comparison and analysis, the curve safety margin compensation quantity is quantized, and a curve safety margin model is constructed.
(5) And (5) the application effect of the model under the curve working condition is verified through experiments.
The desired safety margin model (DSM) may be described as:
the curve following model based on the risk dynamic balance theory has the following equation:
wherein, a'n-follow(t + τ) is the desired acceleration of the trailing vehicle; τ' is the reaction time; SMnDH' (t) and SMnDL' (t) is a desired safety margin interval SM at time t for each of n vehicles as following vehiclesnD' (t) upper and lower limits, α1' and α2' the coefficients related to the behavior of the driver of the following vehicle for acceleration and deceleration, the sensitivity coefficients for acceleration and deceleration, and α1′＞0m/s2,α2′＞0m/s2。
The invention considers the behavior characteristics of a driver driving a vehicle under the working condition of a curve, and the reaction time, the upper and lower limits of the safety margin and the acceleration and deceleration sensitivity coefficient of the driver are changed due to different road environments. On the basis of the expected safety margin model, curve driving behavior compensation is added, the compensation behavior is quantized, and the effect of the compensation model is verified according to an actual vehicle experiment. The invention provides a basis for the curve following theory and the development of a following model, and is favorable for more reasonable application of the ACC in the curve following process.
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FIG. 1 is a schematic diagram of a technical route according to the present invention;
FIG. 2 is a schematic diagram of a curve vehicle following motion in an embodiment of the present invention;
FIG. 3 is a model of the stimulus response during a curve following.
FIG. 4 is a graph of SM (safety margin) variation for a driver in straight and curved road conditions.
FIG. 5(a) a graph of driver response time and safety margin for a curve condition; FIG. 5(b) is a graph showing the variation of the acceleration sensitivity and the deceleration sensitivity of the driver under the curve condition.
FIG. 6(a) is a graph of curve radius versus upper and lower limits of safety margin; fig. 6(b) is a graph showing the change of the radius of the curve and the acceleration/deceleration sensitivity coefficient, and shows the change trends of the upper and lower limits of the safety margin and the acceleration/deceleration sensitivity coefficient at different road radii.
FIG. 7 model verification diagram.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
In the vehicle-vehicle communication environment, the behavior characteristics of a driver under the working condition of a curve are analyzed, curve driving behavior compensation is added on the basis of an expected safety margin following model, and the compensation behavior is quantized, and the method specifically comprises the following steps:
(1) establishing a curve state parameter vector p':
p′＝[τ′ SMnDH′ SMnDL′ α′1α′2]T
wherein τ' is the reaction time; SMnDH' (t) and SMnDL' (t) upper and lower limits of the expected safety margin of the following vehicle at time t, α1' and α2' is the acceleration and deceleration sensitivity coefficient of the following vehicle driver,
(2) compensation quantities representing parameters in a desired safety margin model
The compensation amount of each parameter under the curve working condition can be expressed in the form of an equation system and a matrix.
The compensation quantity equation set is as follows:
a compensation quantity matrix:
wherein p ═ τ SMnDHSMnDLα1α2]TRepresenting a straight-road state parameter vector; a is a compensation coefficient; b is a behavior compensation variable, which is related to the curve road characteristics, R refers to the curve radius, θ refers to the curve corner, and 1 refers to a constant term.
(3) Establishing a curve following behavior compensation model:
(4) quantifying a curve following behavior compensation amount using data
In a curve traffic scene, under the environment of vehicle-vehicle communication, state information of a front vehicle and a rear vehicle is obtained, curve state parameter vectors are constructed, and quantized compensation quantities are digitized.
(5) Setting up new experimental scene for model verification
Designing a new curve scene, acquiring vehicle state information in the following process, fitting a known model, and verifying the effect of the model.
In fig. 2, the radius of the curve is R, the turning angle is theta, and the linear distance between the curve entering and the curve exiting is L.
Fig. 3 is a model of the stimulus response during a curve following process, in which the following vehicle is influenced by the preceding vehicle, and the speed of the following vehicle depends on the speed of the preceding vehicle, the current speed of the following vehicle, and the relative distance between the two vehicles. The driver of the rear vehicle can control the vehicle speed to adjust the distance according to the dangerous degree, and the available adjusting modes comprise three modes, namely acceleration increasing, speed reducing and original speed maintaining. When a driver follows the vehicle under the working condition of a curve, when the safety margin is higher than the upper limit of the interval, the driver accelerates to ensure that the danger level felt by the driver returns to the safety margin interval; when the safety margin sensed by the driver is lower than the lower limit of the safety margin interval, the driver takes deceleration measures, and when the sensed safety margin is lower than the lower limit of the safety margin interval, the driver takes deceleration measures to recover the subjective danger level sensed by the driver to the acceptable safety margin interval; the current motion state is maintained when the driver feels the risk level in its acceptable range.
FIG. 4 is a graph of driver safety margin variation for straight and curved road conditions. During a follow-up, the driver's safety margin has a dynamic balance characteristic that fluctuates back and forth around a fixed value. When the driver is driving under the curve working condition, the SMn has an obvious peak value, which shows that under the curve working condition, the driver dynamically adjusts the driving behavior according to the perceived risk level. As can be seen from the figure, after the vehicle enters a curve, the safety margin change rule is basically that the safety margin change rule is firstly reduced and then increased, a peak value exists, and the peak value is larger than the SM level before the vehicle enters the curve. The safety margins are distributed in (0.6,1.4) and mainly concentrated near 0.9, the safety margins under the working conditions of straight curves and curves have differences, and the safety margins under different curve types are also different.
Fig. 5(a) is a graph showing changes in the reaction time and the safety margin of the driver in the curve condition. FIG. 5(b) is a graph showing the change in the acceleration sensitivity and the deceleration sensitivity of the driver during a curve. And under the working condition of the curve, all parameters comprise reaction time, upper and lower limits of safety margin and acceleration and deceleration sensitivity coefficients. The road number is odd and straight, and the road number is even and bend, and the change rule of each parameter is: compared with straight-way driving, the reaction time of the driver under the working condition of the curve is reduced, probably because the driver considers that the danger level of the curve driving is higher than that of the straight-way driving, more energy can be put into the following process, and when the motion state of the current vehicle and the road environment are changed, the driver can more sensitively adjust the state of the current vehicle, so that the reaction time is reduced. The difference of the reaction time of the driver under different straight-curve working conditions indicates that the influence of different road conditions on the driver is different; the upper and lower limits of the safety margin of the driver under the curve working condition are obviously higher than those under the straight-way working condition, the upper and lower limits of the safety margin of the driver can be increased when the driver bends, and the upper and lower limits of the safety margin can be reduced when the driver bends, so that the danger level sensed by the driver when the driver drives on the curve is changed, the danger judgment threshold is increased, and corresponding speed adjustment can be carried out only when the upper and lower limits of the threshold are reached; the acceleration and deceleration sensitivity coefficients of the driver under different straight curve and curve working conditions are different, which shows that the influence degrees of different road conditions on the acceleration and deceleration operation of the driver have obvious difference. Compared with the straight-road driving, the acceleration and deceleration sensitivity coefficient of the driver under the curve working condition is reduced, the change of the sensitivity coefficient also reflects the level of the current driver for judging the current safety risk to a certain extent, and the driver considers that the danger degree in the curve following process is higher than that of the straight-road following, so that the driver can operate more cautiously in the driving process, the acceleration and deceleration behavior of the driver is relatively mild, and the operation of rapid acceleration and deceleration or large-amplitude acceleration and deceleration is less likely to occur, so that the curve following safety is more favorably realized.
Fig. 6(a) is a graph showing a change in the radius of a curve and the upper and lower limits of the safety margin, and fig. 6(b) is a graph showing a change in the radius of a curve and the acceleration/deceleration sensitivity coefficient, which shows the upper and lower limits of the safety margin and the change in the acceleration/deceleration sensitivity coefficient at different road radii. As can be seen from the figure, the variation amplitude of the lower safety margin limit is larger than that of the upper safety margin limit; the curve data with the number of 12 has an abnormal value, and the abnormal value can be selected to be deleted in subsequent analysis so as to ensure better fitting effect; the radius of the curve numbered 6 and 8 is larger, the number of the straight road between the two curves is 7, the variation range of the deceleration sensitivity coefficient of the driver is larger in the straight road driving process, and the influence of the radius of the curve on the deceleration sensitivity coefficient of the related straight road is larger.
Fig. 7 is a comparison graph for model verification, which shows a theoretical value and a fitting value of the following behavior compensation model in a certain curve, and the fitting error is used for judgment, and the error value is less than 0.05, so that the model is considered to have a good effect.
The curve model fitting result, the model verification method and the judgment index thereof can be changed. On the basis of the technical solution of the present invention, the improvement and equivalent exchange of the individual methods should not be excluded from the scope of protection of the present invention.
Claims (1)
1. A curve follow-up model considering a dynamic driver risk balance process is characterized by comprising a curve state parameter vector p ═ τ' SMnDH′ SMnDL′ α′1α′2]TThe compensation quantity of each parameter in the model is expressed by an equation set and a matrix form:
wherein A is a compensation coefficient; b is a behavior compensation variable, the value is related to the road characteristic of the curve, R refers to the radius of the curve, theta refers to the corner of the curve, and 1 refers to a constant term;
the motion equation of the curve behavior compensation model is as follows:
wherein, a'n-follow(t + τ ') is the desired acceleration of the vehicle after the curve, τ' is the reaction time, SMnDH' (t) and SMnDL' (t) is a desired safety margin interval SM at time t for each of n vehicles as following vehiclesnD' (t) Upper and lower limits, α1' and α2' the coefficients related to the behavior of the driver of the following vehicle for acceleration and deceleration, the sensitivity coefficients for acceleration and deceleration, and α1′＞0m/s2,α2′＞0m/s2。
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| CN112389430B (en) * | 2020-11-06 | 2024-01-19 | 北京航空航天大学 | Determination method for vehicle lane change cutting-in motorcade period based on offset rate |
| CN113611157B (en) * | 2021-08-10 | 2022-11-08 | 北京航空航天大学合肥创新研究院(北京航空航天大学合肥研究生院) | Method for estimating rear-end collision risk of vehicles on highway |
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| CN113928314B (en) * | 2021-11-17 | 2023-11-10 | 吉林大学 | Automatic driving vehicle following control method considering front and rear vehicles under ice and snow road surface condition |
| CN114613131B (en) * | 2022-03-01 | 2023-03-28 | 北京航空航天大学 | Safety margin-based personalized forward collision early warning method |
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