CN116653944A - Method and system for controlling braking distance of expressway automatic driving vehicle - Google Patents
Method and system for controlling braking distance of expressway automatic driving vehicle Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- 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
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- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
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- B60T7/12—Brake-action initiating means for automatic initiation; for initiation not subject to will of driver or passenger
- B60T7/22—Brake-action initiating means for automatic initiation; for initiation not subject to will of driver or passenger initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle, or by means of contactless obstacle detectors mounted on the vehicle
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- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
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- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
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- B60W60/0018—Planning or execution of driving tasks specially adapted for safety by employing degraded modes, e.g. reducing speed, in response to suboptimal conditions
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Abstract
The invention discloses a method and a system for controlling braking distance of an automatic driving vehicle on a highway, wherein the method comprises the following steps: s1, constructing a braking model according to highway road traffic scene related data; s2, calculating the stopping sight distance of the automatic driving vehicle according to the braking model; s3, performing braking interval control according to the stopping sight distance of the automatic driving vehicle. According to the invention, based on two following models of Krause and ACC, the braking distance control of the automatic driving vehicle on the expressway is constructed, and the parking sight distance requirement of the automatic driving vehicle can be determined more accurately and quantitatively. Under the condition that the parking sight distance can ensure traffic safety, the traffic efficiency is greatly improved. The invention can provide technical guidance for the influence research of the current automatic driving technology on the key design indexes of the expressway, and provides technical support for the linear index design of the novel expressway after the future automatic driving technology is comprehensively popularized, so that the invention has wide popularization and application prospects.
Description
Technical Field
The invention belongs to the technical field of automatic driving, and particularly relates to a method and a system for controlling braking distance of an automatic driving vehicle on a highway.
Background
The expressway is used as the most main mode of long-distance driving at present, is the road with the highest grade, and has high design speed, high linear design index of the road and high running speed of the vehicle. When an emergency occurs, the decision time of a driver is short, and if the emergency is processed improperly, serious traffic accidents are easy to cause, so that huge life and property losses are caused. Thus, the highway traffic safety problem is a major issue of traffic safety. The factors influencing the driving safety of the expressway are numerous, and the road alignment index is one of the most important factors. The driving sight distance is used as the most basic road alignment design index, and has important influence on driving safety. In the linear design of expressways, ensuring enough driving vision distance is an important measure for ensuring driving safety and comfort. If the sight distance is poor, the driver may not make a correct decision, and traffic accidents may occur.
In recent years, with the continuous improvement of intelligent and informatization technologies, automobile automatic driving technologies are continuously developed and gradually become the main development direction in the future. And due to the formulation of automatic driving technology and national policy, the future automatic driving vehicles gradually infiltrate into the existing traffic flow in a certain proportion to form heterogeneous traffic flow with certain permeability. The addition of the automatic driving vehicle can cause the reaction and braking performance of the vehicle to be greatly different from those of the traditional manual driving vehicle, the parking sight distance is used as an important road alignment design index for guaranteeing driving safety, the adaptability of the heterogeneous traffic flow and the parking sight distance is required to be developed and researched, and the braking distance of the automatic driving vehicle is controlled according to the parking sight distance so as to guarantee traffic safety of expressways.
For the research of the parking sight distance, domestic researchers mainly focus on the improvement of the parking sight distance model theory and the inspection method of road alignment. The scholars build a parking sight distance calculation model by repartitioning the automobile braking process, and some scholars correct the parking sight distance through the running speed and the braking deceleration. The above studies have conducted intensive studies on the parking sight distance, and it is considered that the parking sight distance calculation mainly includes two parts of a reaction distance and a braking distance, and is widely accepted, but they are focused on manual driving vehicles, and no parking sight distance study is conducted on automatic driving vehicles. In addition, in the study of important calculation parameter-reaction time related to the parking sight distance, the current calculation is carried out through an empirical value, and quantitative calculation is lacked.
In the running process of the vehicle, the vehicle is driven by following the vehicle at a certain interval according to the following model and the leading vehicle, and the ACC following model is mainly used for controlling in the following interval of automatic driving at present. The safety parking space parameter is still referred to an empirical value in the ACC following model, and is not analyzed for a special scene that the leading vehicle has a state abrupt change, and a certain braking safety space is required to be kept between vehicles when the vehicles run following. When the actual vehicle is running, the automatic driving vehicle is used as a following vehicle, and if the control of the braking safety distance with the front vehicle is performed according to the stopping sight distance, the control is too conservative. Therefore, there is an urgent need to develop a brake interval control method and system thereof that can adapt to an automatic driving environment.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and improves a method and a system for controlling the braking distance of an automatic driving vehicle on a highway.
In order to achieve the expected effect, the invention adopts the following technical scheme:
the invention discloses a method for controlling braking distance of an automatic driving vehicle on a highway, which comprises the following steps:
s1, constructing a braking model according to highway road traffic scene related data;
s2, calculating the stopping sight distance of the automatic driving vehicle according to the braking model;
s3, performing braking interval control according to the stopping sight distance of the automatic driving vehicle.
Further, the highway road traffic scene related data includes the direction, number and hourly traffic volume of each lane.
Further, the step S2 specifically includes:
s2.1, constructing a following scene;
s2.2, calibrating the reaction time of the automatic driving vehicle in a following scene;
s2.3, calculating a reaction distance according to the reaction time;
S2.4, calculating the parking sight distance according to the reaction distance.
Further, the following scene includes a following vehicle following a leading vehicle, the following vehicle including an autonomous vehicle and a natural person driving vehicle, the leading vehicle including an autonomous vehicle and a natural person driving vehicle.
Further, the S2.2 specifically includes: based on a following model, collecting experimental parameters in a traffic flow simulation mode, screening vehicle data with specific numbers from the experimental parameters to serve as following vehicle data, determining a leading vehicle and a lane where the leading vehicle is located according to the following vehicle data, extracting relative speed data of the following vehicle and the leading vehicle and acceleration data of the following vehicle to perform correlation analysis, and when the cross correlation coefficient is maximum, obtaining the relative speed of the following vehicle and the leading vehicle/the acceleration of the following vehicle = the reaction time of an automatic driving vehicle.
Further, the rule of collecting experimental parameters is as follows: during data acquisition, the following vehicle and the leading vehicle are both positioned on the same lane, and the relation between following and being followed is always kept, the shortest time of the following vehicle to follow the leading vehicle is larger than a first preset value, and the number of data records of the following vehicle to follow the leading vehicle is larger than a second preset value.
Further, the parking line of sight = reaction distance + braking distance.
Further, the braking distance=braking force rising stage distance+full braking stage distance.
Further, the step S3 specifically includes:
the following car monitors the acceleration of the leading car;
when the acceleration of the front guided vehicle is smaller than the braking acceleration of the following vehicle, the following vehicle continues to run;
when the acceleration of the leading vehicle is larger than the braking acceleration of the following vehicle, the following vehicle keeps a corresponding safe braking distance with the leading vehicle according to the vehicle attribute of the leading vehicle.
The invention also discloses a system for controlling the braking interval of the expressway automatic driving vehicle, which comprises the following steps:
the acquisition module is used for acquiring highway road traffic scene related data;
the braking interval control module is used for constructing a braking model according to the related data of the expressway road traffic scene; calculating the stopping sight distance of the automatic driving vehicle according to the braking model; and performing brake interval control according to the parking sight distance of the automatic driving vehicle.
Compared with the prior art, the invention has the beneficial effects that: the invention discloses a method and a system for controlling braking distance of an automatic driving vehicle on a highway, wherein the method comprises the following steps: s1, constructing a braking model according to highway road traffic scene related data; s2, calculating the stopping sight distance of the automatic driving vehicle according to the braking model; s3, performing braking interval control according to the stopping sight distance of the automatic driving vehicle. According to the invention, based on two following models of Krause and ACC, the braking distance control of the automatic driving vehicle on the expressway is constructed, and the parking sight distance requirement of the automatic driving vehicle can be determined more accurately and quantitatively. The invention is mainly applied to expressway driving safety control scenes, combines the limitation of automatic driving real vehicle test policy and uncertain additional factors brought by real vehicle test, considers the influence of the introduction of the automatic driving vehicle on the existing traffic flow, analyzes the different following combination conditions of the vehicle, carries out the calibration of response time according to the simulated following data, builds the braking models of the automatic driving vehicle and the manual driving vehicle, calculates the parking sight distance of the automatic driving vehicle, and carries out the braking distance control according to the automatic driving parking sight distance. Compared with the prior art that only the parking sight distance index value taking the manual driving vehicle as a main body is considered in the following model, the safety parking distance parameter refers to an empirical value, and research and analysis of a special scene of the condition of sudden change of the leading vehicle state are lacking. Under the condition that the parking sight distance can ensure traffic safety, the traffic efficiency is greatly improved. With the continuous development and maturity of the automatic driving technology, mass production and actual business of the automatic driving vehicle will become the necessary result of the development of the novel expressway. By means of the technical advancement of the expressway field, the method can provide technical guidance for the influence research of the current automatic driving technology on the key design indexes of the expressway, and simultaneously provides technical support for the line-shaped index design of the novel expressway after the future automatic driving technology is comprehensively popularized, so that the method has wide popularization and application prospects.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings described below are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for controlling a braking distance of an automatic driving vehicle on a highway according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of different following situations of vehicles under heterogeneous traffic of a highway automatic driving vehicle braking distance control method according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of an automatic driving vehicle braking process of a method for controlling a braking distance of an automatic driving vehicle on a highway according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a cross correlation coefficient of reaction time of a method for controlling a braking distance of an automatic driving vehicle on a highway according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of heterogeneous traffic flow simulation of a method for controlling a braking distance of an automatic driving vehicle on a highway according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 to 5, the invention discloses a method for controlling the braking distance of an automatic driving vehicle on a highway, which comprises the following specific steps:
s1, constructing a braking model according to highway road traffic scene related data;
s2, calculating the stopping sight distance of the automatic driving vehicle according to the braking model;
s3, performing braking interval control according to the stopping sight distance of the automatic driving vehicle.
In one embodiment, the method integrates the limitation of the automatic driving real vehicle test policy and the uncertain additional factors brought by the real vehicle test, considers the influence of the introduction of the automatic driving vehicle on the existing traffic flow, analyzes the different following combination conditions of the vehicle, performs the calibration of the response time according to the simulated following data, builds the braking models of the automatic driving vehicle and the manual driving vehicle, calculates the parking sight distance of the automatic driving vehicle, and performs the braking interval control according to the automatic driving parking sight distance.
Preferably, the highway road traffic scene related data includes the direction, number and hourly traffic volume of each lane. For example, the main line is a bidirectional six-lane (unidirectional three-lane), and the design speed is 120km/h; the ramp is a single lane, and the design speed is 70km/h; road design hour traffic is 2200veh/h. The related data of the expressway road traffic scene can be extracted through the existing traffic system, and can also be obtained by carrying out real-time survey according to different application scenes.
Further, the step S2 specifically includes:
s2.1, constructing a following scene;
s2.2, calibrating the reaction time of the automatic driving vehicle in a following scene;
s2.3, calculating a reaction distance according to the reaction time;
s2.4, calculating the parking sight distance according to the reaction distance.
In one aspect, the following scene includes a following vehicle following a lead vehicle, the following vehicle including an autonomous vehicle and a natural person driving vehicle, the lead vehicle including an autonomous vehicle and a natural person driving vehicle. Specifically, for heterogeneous traffic flows in which an autonomous vehicle and a natural human-driven vehicle are mixed, the respective driving behaviors of the autonomous vehicle and the autonomous vehicle will have mutual influences, and the influence degrees are different, so that the influence between the autonomous vehicle and the autonomous vehicle needs to be discussed in different cases. From a combined analysis of two types of vehicles, there are mainly four following situations on the same lane, as shown in fig. 2.
On the other hand, the S2.2 specifically includes: based on a following model, collecting experimental parameters in a traffic flow simulation mode, screening vehicle data with specific numbers from the experimental parameters to serve as following vehicle data, determining a leading vehicle and a lane where the leading vehicle is located according to the following vehicle data, extracting relative speed data of the following vehicle and the leading vehicle and acceleration data of the following vehicle to perform correlation analysis, and when the cross correlation coefficient is maximum, obtaining the relative speed of the following vehicle and the leading vehicle/the acceleration of the following vehicle = the reaction time of an automatic driving vehicle.
Preferably, the rule of collecting experimental parameters is as follows: during data acquisition, the following vehicle and the leading vehicle are both positioned on the same lane, and the relation between following and being followed is always kept, the shortest time of the following vehicle to follow the leading vehicle is larger than a first preset value, and the number of data records of the following vehicle to follow the leading vehicle is larger than a second preset value.
In one embodiment, two following models of the Krause and ACC adaptive cruise control systems are respectively used as following models of a manual driving vehicle and an automatic driving vehicle, experimental parameters are obtained in a traffic flow simulation mode, and the following criteria are applied when research data are selected: from the data acquisition starting time to the data acquisition ending time, the following vehicle and the leading vehicle are always positioned on the same lane, and always keep the relation between following and followed, so as to avoid the influence of the lane changing behavior of the vehicle on the following behavior; the time of the following vehicle following the preceding vehicle is 30 seconds at the shortest, namely at least 300 data records of the following vehicle following the preceding vehicle exist in the data. According to the invention, based on two following models of Krause and ACC, the braking distance control of the automatic driving vehicle on the expressway is constructed, and the parking sight distance requirement of the automatic driving vehicle can be determined more accurately and quantitatively.
Taking a specific number of vehicles as an example, screening the vehicle in simulation data as following vehicle data, searching a leading vehicle in front of the vehicle and a lane where the vehicle is positioned according to the data, and extracting relative speed data Deltav (t) of the two vehicles and acceleration data a of the following vehicle n+1 (t) cross-correlation analysis was performed using data analysis software EVIEWS 5.1. When the cross-correlation coefficient is maximum, then Deltav (t)/a is considered as n+1 And (t) is the reaction time of the specific number vehicle. According to the method, the reaction time of different vehicles under different following conditions can be determined.
In one embodiment, the vehicles in the database are taken as following vehicles and have 450 pieces of data, the data show that the vehicles follow the vehicles with the number X all the time in the data acquisition time and travel on the lanes with the number Y. Relative speed data { Δv (t) } of the vehicle and acceleration data { a (t) } of the following vehicle were extracted, and cross-correlation analysis was performed using EVIEWS5.1, the result of which is shown in FIG. 4. As can be seen from fig. 4, when the reaction time is 0.2 seconds, the cross correlation coefficient of the autonomous vehicle is the largest, and similarly, when the reaction time is 0.8 seconds, the cross correlation coefficient of the natural human driving vehicle is the largest, and then, 0.8 seconds is the natural human driving vehicle reaction time.
Preferably, the calculating the reaction distance according to the reaction time specifically includes:
it is assumed that the automatic driving vehicle and the manual driving vehicle keep the original running speed to run at a constant speed in the process of sensing the information of the front road, the vehicle and the like and making relevant judgment and decision, and according to the analysis of different following conditions, the i table is usedIndicating the ith following case, the reaction distance under different following conditions is: s is S r,i ＝v i T s,i (i＝1,2,3,4)；
Wherein S is r,i The reaction distance of the following car under the ith following condition is expressed as m; v i The following speed of the following vehicle under the ith following condition is expressed in m/s; t (T) s,i The reaction time of the following car in the ith following case is expressed as s.
In a preferred embodiment, the stopping viewing distance = reaction distance + braking distance. Further, the braking distance=braking force rising stage distance+full braking stage distance. Specifically, the braking process of an autonomous vehicle is mainly divided into the following 3 processes: a) The automatic driving vehicle senses the front danger and obstacle and processes the information, the time required by the process is called system reaction time, and the vehicle runs at a constant speed within the reaction time to obtain the reaction distance; b) The vehicle makes a deceleration decision, the braking deceleration is generated and rises to the maximum, the time required by the process is called the braking force rising time, and the braking force rising time is used for decelerating and driving to obtain the distance of the braking force rising stage; c) The vehicle is braked by adopting the maximum deceleration, the time required by the process is called full braking time, and the vehicle is decelerated and driven within the full braking time to obtain the full braking stage distance. Fig. 3 is a schematic diagram of an automatic driving vehicle braking process according to an embodiment of the present invention.
Preferably, the braking force rising stage distance S 2-1 The calculation steps of (1) comprise:
in the brake force rising time, the brake deceleration calculation formula at any moment is as follows:
wherein a is max For maximum braking deceleration in m/s 2 ；τ 3 The unit of the braking force rising time is s, and t is any time in the braking force rising time period.
The vehicle speed calculation formula at any time t in the braking force rising period is as follows:
in the formula, v 0 The initial speed of the vehicle before braking is given in m/s.
In the braking force rising time period, the running distance calculation formula of the vehicle is as follows:
preferably, the full brake stage distance S 2-2 The calculation steps of (1) comprise:
the vehicle speed calculation formula at the start point of the full braking period (i.e., the end point of the braking force rising period) is:
wherein a is max For maximum braking deceleration in m/s 2 ；τ 3 The unit of the time of the rising of the braking force is s, t is any time in the rising period of the braking force, v 0 The initial speed of the vehicle before braking is given in m/s.
The vehicle speed calculation formula at any time t in the full braking phase is:
v(t)＝v s -a max t。
during the full braking period, the vehicle is braked at a maximum deceleration a max Take the velocity from v s Reduced to 0 for a period of time τ 4 ＝v s /a max . Distance S travelled by the vehicle during full braking period 2-2 Calculation of (2)The formula is:
thus, the braking distance S 2 The method comprises the following steps:
in the formula, v 0 The unit is m/s, tau, the initial speed of the vehicle before braking 3 The unit of the time of rising of the braking force is s, a max For maximum braking deceleration in m/s 2 ，v s The unit is m/s, tau, the starting point vehicle speed of the full braking time period 4 For the vehicle to run at maximum braking deceleration a during the full braking period max Take the velocity from v s The time taken to decrease to 0 is in s.
Preferably, the calculation formula of the stopping sight distance of the automatic driving vehicle under different following conditions in the heterogeneous traffic flow is as follows:
in the formula, v 0 The unit is m/s for the initial speed of the vehicle before braking; t (T) s The reaction time is s; τ 3 The unit is s, which is the time for the braking force to rise; a, a max For maximum braking deceleration in m/s 2 ；v i The starting point vehicle speed of the automatic driving vehicle in the full braking time period under different following conditions is m/s; τ 4 For the vehicle to run at maximum braking deceleration a during the full braking period max Take the velocity from v i The time taken to decrease to 0 is in s.
Specifically, at different initial vehicle speeds, the calculation results of the stopping line of sight of the automatically driven vehicle and the manually driven vehicle are shown in table 1.
TABLE 1
| Initial speed (km/m) | 80 | 90 | 100 | 110 | 120 |
| Value of "Specification | 110 | - | 160 | - | 210 |
| Manual driving vehicle parking sight distance (m) | 99.32 | 103.54 | 132.22 | 144.78 | 195.23 |
| Automatic driving vehicle parking sight distance (m) | 83.74 | 98.67 | 127.36 | 137.68 | 180.07 |
Further, the step S3 specifically includes:
the following car monitors the acceleration of the leading car;
when the acceleration of the front guided vehicle is smaller than the braking acceleration of the following vehicle, the following vehicle continues to run;
when the acceleration of the leading vehicle is larger than the braking acceleration of the following vehicle, the following vehicle keeps a corresponding safe braking distance with the leading vehicle according to the vehicle attribute of the leading vehicle.
Specifically, as shown in fig. 1, the brake pitch control is divided into two parts, that is, the leading vehicle running state determination and the control of the brake distance. During the running process of the vehicle, the following vehicle judges the running state by monitoring the acceleration of the leading vehicle, and when the acceleration is smaller than the braking acceleration, the speed of the leading vehicle is regulated in a normal state, the leading vehicle is in a normal running state, and the following vehicle can continue to run; when the acceleration is larger than the braking acceleration, the leading vehicle is in a braking state, and the following vehicle needs to keep a safe braking distance from the leading vehicle. When the following car judges that the leading car is in a braking state, firstly judging the vehicle attribute of the leading car, namely judging whether the leading car is an automatic driving vehicle or a manual driving vehicle, and then keeping a corresponding safe braking distance with the leading car according to the attribute of the leading car. In practical applications, the vehicle attribute of the lead vehicle may be identified by: differentiation is made by driving behavior or lane lines, or identification is made by image identification (e.g., vehicle, tag) on the vehicle, etc.
According to the parking sight distances of different vehicle attributes, the formula for calculating the safety braking distance is as follows:
wherein d is a safety braking distance which is required to be kept between the following car and the leading car, and the unit is m; v C The unit is km/h for following the speed of the car; v A The speed of the lead vehicle is km/h; d (D) max For stopping vision distance of automatic driving vehicle or manual driving vehicle, unitM.
As shown in fig. 5, in simulation software SUMO, a heterogeneous traffic flow simulation platform is built according to road traffic environment, vehicle parking sight distance is calibrated by a standard value and a model calculation value in a route design standard, and safety distance control is performed on an automatic driving vehicle according to the parking sight distance. Under the control method, the simulation software can output data such as vehicle speed, acceleration, collision event TTC (Time-To-Collision) and the like. And secondly, comparing the traffic efficiency and traffic safety under two traffic environments by using the obtained vehicle information data, and verifying the practicability and effectiveness of the control method. Exemplary traffic simulation results versus conditions are shown in table 2.
TABLE 2
| Distance of vision for parking | q | v | E | N TC | R S |
| According to Specification | 1278 | 110 | 140580 | 84 | 0.0164 |
| According to the invention | 1578 | 118 | 183327 | 144 | 0.0231 |
| Difference value | - | - | 42792 | - | 0.0067 |
According to the calculation results in table 2, compared with the parking sight distance in the specification, the traffic flow power of the parking sight distance determined by the invention is increased by 42792km, namely, by nearly 30.44%. Therefore, the parking sight distance determined by the invention can greatly improve the traffic efficiency under the condition of ensuring traffic safety. Wherein q is the number of vehicles passing under the actual traffic condition; v is the average running speed in actual traffic conditions, km/h; e is traffic flow power, and the unit is km; n (N) TC The number of serious traffic conflicts in 1 hour is twice/h; r is R S For severe traffic collision rate, sub/(pcu km).
The invention is mainly applied to expressway driving safety control scenes, combines the limitation of automatic driving real vehicle test policy and uncertain additional factors brought by real vehicle test, considers the influence of the introduction of the automatic driving vehicle on the existing traffic flow, analyzes the different following combination conditions of the vehicle, carries out the calibration of response time according to the simulated following data, builds the braking models of the automatic driving vehicle and the manual driving vehicle, calculates the parking sight distance of the automatic driving vehicle, and carries out the braking distance control according to the automatic driving parking sight distance. Compared with the prior art that only the parking sight distance index value taking the manual driving vehicle as a main body is considered in the following model, the safety parking distance parameter refers to an empirical value, and research and analysis of a special scene of the condition of sudden change of the leading vehicle state are lacking. Under the condition that the parking sight distance can ensure traffic safety, the traffic efficiency is greatly improved. With the continuous development and maturity of the automatic driving technology, mass production and actual business of the automatic driving vehicle will become the necessary result of the development of the novel expressway. By means of the technical advancement of the expressway field, the method can provide technical guidance for the influence research of the current automatic driving technology on the key design indexes of the expressway, and simultaneously provides technical support for the line-shaped index design of the novel expressway after the future automatic driving technology is comprehensively popularized, so that the method has wide popularization and application prospects.
The invention also discloses a system for controlling the braking interval of the expressway automatic driving vehicle, which comprises the following steps:
the acquisition module is used for acquiring highway road traffic scene related data;
the braking interval control module is used for constructing a braking model according to the related data of the expressway road traffic scene; calculating the stopping sight distance of the automatic driving vehicle according to the braking model; and performing brake interval control according to the parking sight distance of the automatic driving vehicle.
In one embodiment, the system integrates the limitation of the automatic driving real vehicle test policy and the uncertain additional factors brought by the real vehicle test, considers the influence of the introduction of the automatic driving vehicle on the existing traffic flow, analyzes the different following combination conditions of the vehicle, performs the calibration of the response time according to the simulated following data, builds the braking models of the automatic driving vehicle and the manual driving vehicle, calculates the parking sight distance of the automatic driving vehicle, and performs the braking interval control according to the automatic driving parking sight distance.
Preferably, the acquisition module acquires highway road traffic scene related data including the direction, the number and the hourly traffic volume of each lane. For example, the main line is a bidirectional six-lane (unidirectional three-lane), and the design speed is 120km/h; the ramp is a single lane, and the design speed is 70km/h; road design hour traffic is 2200veh/h.
Further, the braking distance control module calculates a stopping sight distance of the automatic driving vehicle according to the braking model, and specifically includes:
s2.1, constructing a following scene;
s2.2, calibrating the reaction time of the automatic driving vehicle in a following scene;
s2.3, calculating a reaction distance according to the reaction time;
s2.4, calculating the parking sight distance according to the reaction distance.
In one aspect, the following scene includes a following vehicle following a lead vehicle, the following vehicle including an autonomous vehicle and a natural person driving vehicle, the lead vehicle including an autonomous vehicle and a natural person driving vehicle. Specifically, for heterogeneous traffic flows in which an autonomous vehicle and a natural human-driven vehicle are mixed, the respective driving behaviors of the autonomous vehicle and the autonomous vehicle will have mutual influences, and the influence degrees are different, so that the influence between the autonomous vehicle and the autonomous vehicle needs to be discussed in different cases. From a combined analysis of two types of vehicles, there are mainly four following situations on the same lane, as shown in fig. 2.
On the other hand, the S2.2 specifically includes: based on a following model, collecting experimental parameters in a traffic flow simulation mode, screening vehicle data with specific numbers from the experimental parameters to serve as following vehicle data, determining a leading vehicle and a lane where the leading vehicle is located according to the following vehicle data, extracting relative speed data of the following vehicle and the leading vehicle and acceleration data of the following vehicle to perform correlation analysis, and when the cross correlation coefficient is maximum, obtaining the relative speed of the following vehicle and the leading vehicle/the acceleration of the following vehicle = the reaction time of an automatic driving vehicle.
Preferably, the rule of collecting experimental parameters is as follows: during data acquisition, the following vehicle and the leading vehicle are both positioned on the same lane, and the relation between following and being followed is always kept, the shortest time of the following vehicle to follow the leading vehicle is larger than a first preset value, and the number of data records of the following vehicle to follow the leading vehicle is larger than a second preset value.
In one embodiment, two following models of the Krause and ACC adaptive cruise control systems are respectively used as following models of a manual driving vehicle and an automatic driving vehicle, experimental parameters are obtained in a traffic flow simulation mode, and the following criteria are applied when research data are selected: from the data acquisition starting time to the data acquisition ending time, the following vehicle and the leading vehicle are always positioned on the same lane, and always keep the relation between following and followed, so as to avoid the influence of the lane changing behavior of the vehicle on the following behavior; the time of the following vehicle following the preceding vehicle is 30 seconds at the shortest, namely at least 300 data records of the following vehicle following the preceding vehicle exist in the data. According to the invention, based on two following models of Krause and ACC, the braking distance control of the automatic driving vehicle on the expressway is constructed, and the parking sight distance requirement of the automatic driving vehicle can be determined more accurately and quantitatively.
Taking a specific number of vehicles as an example, screening the vehicle in simulation data as following vehicle data, searching a leading vehicle in front of the vehicle and a lane where the vehicle is positioned according to the data, and extracting relative speed data Deltav (t) of the two vehicles and acceleration data a of the following vehicle n+1 (t) cross-correlation analysis was performed using data analysis software EVIEWS 5.1. When the cross-correlation coefficient is maximum, then Deltav (t)/a is considered as n+1 And (t) is the reaction time of the specific number vehicle. According to the method, the reaction time of different vehicles under different following conditions can be determined.
In one embodiment, the vehicles in the database are taken as following vehicles and have 450 pieces of data, the data show that the vehicles follow the vehicles with the number X all the time in the data acquisition time and travel on the lanes with the number Y. Relative speed data { Δv (t) } of the vehicle and acceleration data { a (t) } of the following vehicle were extracted, and cross-correlation analysis was performed using EVIEWS5.1, the result of which is shown in FIG. 4. As can be seen from fig. 4, when the reaction time is 0.2 seconds, the cross correlation coefficient of the autonomous vehicle is the largest, and similarly, when the reaction time is 0.8 seconds, the cross correlation coefficient of the natural human driving vehicle is the largest, and then, 0.8 seconds is the natural human driving vehicle reaction time.
Preferably, the calculating the reaction distance according to the reaction time specifically includes:
it is assumed that the automatic driving vehicle and the manual driving vehicle both keep the original running speed to run at a constant speed in the process of sensing the information of the road in front, the vehicle and the like and making related judgment and decision, and according to the analysis of different following conditions in the above description, i is used for representing the i-th following condition, and the reaction distances under different following conditions are as follows: s is S r,i ＝v i T s,i (i＝1,2,3,4)；
Wherein S is r,i The reaction distance of the following car under the ith following condition is expressed as m; v i The following speed of the following vehicle under the ith following condition is expressed in m/s; t (T) s,i The reaction time of the following car in the ith following case is expressed as s.
In a preferred embodiment, the stopping viewing distance = reaction distance + braking distance. Further, the braking distance=braking force rising stage distance+full braking stage distance. Specifically, the braking process of an autonomous vehicle is mainly divided into the following 3 processes: a) The automatic driving vehicle senses the front danger and obstacle and processes the information, the time required by the process is called system reaction time, and the vehicle runs at a constant speed within the reaction time to obtain the reaction distance; b) The vehicle makes a deceleration decision, the braking deceleration is generated and rises to the maximum, the time required by the process is called the braking force rising time, and the braking force rising time is used for decelerating and driving to obtain the distance of the braking force rising stage; c) The vehicle is braked by adopting the maximum deceleration, the time required by the process is called full braking time, and the vehicle is decelerated and driven within the full braking time to obtain the full braking stage distance. Fig. 3 is a schematic diagram of an automatic driving vehicle braking process according to an embodiment of the present invention.
Preferably, the braking force rising stage distance S 2-1 The calculation steps of (1) comprise:
in the brake force rising time, the brake deceleration calculation formula at any moment is as follows:
wherein a is max For maximum braking deceleration in m/s 2 ；τ 3 The unit of the braking force rising time is s, and t is any time in the braking force rising time period.
The vehicle speed calculation formula at any time t in the braking force rising period is as follows:
in the formula, v 0 The initial speed of the vehicle before braking is given in m/s.
In the braking force rising time period, the running distance calculation formula of the vehicle is as follows:
preferably, the full brake stage distance S 2-2 The calculation steps of (1) comprise:
the vehicle speed calculation formula at the start point of the full braking period (i.e., the end point of the braking force rising period) is:
wherein a is max For maximum braking deceleration in m/s 2 ；τ 3 The unit of the braking force rising time is s, and t is the braking force rising timeAt any time within a segment, v 0 The initial speed of the vehicle before braking is given in m/s.
The vehicle speed calculation formula at any time t in the full braking phase is:
v(t)＝v s -a max t。
during the full braking period, the vehicle is braked at a maximum deceleration a max Take the velocity from v s Reduced to 0 for a period of time τ 4 ＝v s /a max . Distance S travelled by the vehicle during full braking period 2-2 The calculation formula of (2) is as follows:
/>
thus, the braking distance S 2 The method comprises the following steps:
in the formula, v 0 The unit is m/s, tau, the initial speed of the vehicle before braking 3 The unit of the time of rising of the braking force is s, a max For maximum braking deceleration in m/s 2 ，v s The unit is m/s, tau, the starting point vehicle speed of the full braking time period 4 For the vehicle to run at maximum braking deceleration a during the full braking period max Take the velocity from v s The time taken to decrease to 0 is in s.
Preferably, the calculation formula of the stopping sight distance of the automatic driving vehicle under different following conditions in the heterogeneous traffic flow is as follows:
in the formula, v 0 The unit is m/s for the initial speed of the vehicle before braking; t (T) s The reaction time is s; τ 3 The unit is s, which is the time for the braking force to rise; a, a max For maximum braking deceleration in m/s 2 ；v i The starting point vehicle speed of the automatic driving vehicle in the full braking time period under different following conditions is m/s; τ 4 For the vehicle to run at maximum braking deceleration a during the full braking period max Take the velocity from v i The time taken to decrease to 0 is in s.
Specifically, at different initial vehicle speeds, the calculation results of the stopping line of sight of the automatically driven vehicle and the manually driven vehicle are shown in table 1.
TABLE 1
| Initial speed (km/m) | 80 | 90 | 100 | 110 | 120 |
| Value of "Specification | 110 | - | 160 | - | 210 |
| Manual driving vehicle parking sight distance (m) | 99.32 | 103.54 | 132.22 | 144.78 | 195.23 |
| Automatic driving vehicle parking sight distance (m) | 83.74 | 98.67 | 127.36 | 137.68 | 180.07 |
Further, the step S3 specifically includes:
the following car monitors the acceleration of the leading car;
when the acceleration of the front guided vehicle is smaller than the braking acceleration of the following vehicle, the following vehicle continues to run;
when the acceleration of the leading vehicle is larger than the braking acceleration of the following vehicle, the following vehicle keeps a corresponding safe braking distance with the leading vehicle according to the vehicle attribute of the leading vehicle.
Specifically, as shown in fig. 1, the brake pitch control is divided into two parts, that is, the leading vehicle running state determination and the control of the brake distance. During the running process of the vehicle, the following vehicle judges the running state by monitoring the acceleration of the leading vehicle, and when the acceleration is smaller than the braking acceleration, the speed of the leading vehicle is regulated in a normal state, the leading vehicle is in a normal running state, and the following vehicle can continue to run; when the acceleration is larger than the braking acceleration, the leading vehicle is in a braking state, and the following vehicle needs to keep a safe braking distance from the leading vehicle. When the following car judges that the leading car is in a braking state, firstly judging the vehicle attribute of the leading car, namely judging whether the leading car is an automatic driving vehicle or a manual driving vehicle, and then keeping a corresponding safe braking distance with the leading car according to the attribute of the leading car.
According to the parking sight distances of different vehicle attributes, the formula for calculating the safety braking distance is as follows:
wherein d is a safety braking distance which is required to be kept between the following car and the leading car, and the unit is m; v C The unit is km/h for following the speed of the car; v A The speed of the lead vehicle is km/h; d (D) max The parking sight distance for automatically driving the vehicle or manually driving the vehicle is expressed as m.
As shown in fig. 5, in simulation software SUMO, a heterogeneous traffic flow simulation platform is built according to road traffic environment, vehicle parking sight distance is calibrated by a standard value and a model calculation value in a route design standard, and safety distance control is performed on an automatic driving vehicle according to the parking sight distance. Under the control method, the simulation software can output data such as vehicle speed, acceleration, collision event TTC (Time-To-Collision) and the like. And secondly, comparing the traffic efficiency and traffic safety under two traffic environments by using the obtained vehicle information data, and verifying the practicability and effectiveness of the control method. Exemplary traffic simulation results versus conditions are shown in table 2.
TABLE 2
| Distance of vision for parking | q | v | E | N TC | R S |
| According to Specification | 1278 | 110 | 140580 | 84 | 0.0164 |
| According to the invention | 1578 | 118 | 183327 | 144 | 0.0231 |
| Difference value | - | - | 42792 | - | 0.0067 |
According to the calculation results in table 2, compared with the parking sight distance in the specification, the traffic flow power of the parking sight distance determined by the invention is increased by 42792km, namely, by nearly 30.44%. Therefore, the parking sight distance determined by the invention can greatly improve the traffic efficiency under the condition of ensuring traffic safety. Wherein q is the number of vehicles passing under the actual traffic condition; v is the average running speed in actual traffic conditions, km/h; e is traffic flow power, and the unit is km; n (N) TC The number of serious traffic conflicts in 1 hour is twice/h; r is R S For severe traffic collision rate, sub/(pcu km).
The invention is mainly applied to expressway driving safety control scenes, combines the limitation of automatic driving real vehicle test policy and uncertain additional factors brought by real vehicle test, considers the influence of the introduction of the automatic driving vehicle on the existing traffic flow, analyzes the different following combination conditions of the vehicle, carries out the calibration of response time according to the simulated following data, builds the braking models of the automatic driving vehicle and the manual driving vehicle, calculates the parking sight distance of the automatic driving vehicle, and carries out the braking distance control according to the automatic driving parking sight distance. Compared with the prior art that only the parking sight distance index value taking the manual driving vehicle as a main body is considered in the following model, the safety parking distance parameter refers to an empirical value, and research and analysis of a special scene of the condition of sudden change of the leading vehicle state are lacking. Under the condition that the parking sight distance can ensure traffic safety, the traffic efficiency is greatly improved. With the continuous development and maturity of the automatic driving technology, mass production and actual business of the automatic driving vehicle will become the necessary result of the development of the novel expressway. By means of the technical advancement of the expressway field, the method can provide technical guidance for the influence research of the current automatic driving technology on the key design indexes of the expressway, and simultaneously provides technical support for the line-shaped index design of the novel expressway after the future automatic driving technology is comprehensively popularized, so that the method has wide popularization and application prospects.
Based on the same thought, the invention also discloses electronic equipment, which can comprise: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are in communication with each other through the communication bus. The processor may invoke logic instructions in the memory to perform a highway autopilot vehicle brake pitch control method comprising:
s1, constructing a braking model according to highway road traffic scene related data;
s2, calculating the stopping sight distance of the automatic driving vehicle according to the braking model;
s3, performing braking interval control according to the stopping sight distance of the automatic driving vehicle.
Further, the logic instructions in the memory described above may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, embodiments of the present invention also provide a computer program product including a computer program stored on a non-transitory computer readable storage medium, the computer program including program instructions which, when executed by a computer, enable the computer to perform a method for controlling a braking distance of an automatic highway driving vehicle according to the above-described method embodiments, the method including:
s1, constructing a braking model according to highway road traffic scene related data;
s2, calculating the stopping sight distance of the automatic driving vehicle according to the braking model;
s3, performing braking interval control according to the stopping sight distance of the automatic driving vehicle.
In yet another aspect, an embodiment of the present invention further provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform a method for controlling a braking distance of an automatic highway driving vehicle according to the above embodiments, the method comprising:
s1, constructing a braking model according to highway road traffic scene related data;
s2, calculating the stopping sight distance of the automatic driving vehicle according to the braking model;
S3, performing braking interval control according to the stopping sight distance of the automatic driving vehicle.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A method for controlling a braking interval of an automatic driving vehicle on a highway, comprising:
S1, constructing a braking model according to highway road traffic scene related data;
s2, calculating the stopping sight distance of the automatic driving vehicle according to the braking model;
s3, performing braking interval control according to the stopping sight distance of the automatic driving vehicle.
2. The method for controlling the braking distance of an automatically driven vehicle on an expressway according to claim 1, wherein said expressway road traffic scene data includes the direction, the number and the hourly traffic volume of each lane.
3. The method for controlling the braking distance of an automatic highway driving vehicle according to claim 1, wherein S2 specifically comprises:
s2.1, constructing a following scene;
s2.2, calibrating the reaction time of the automatic driving vehicle in a following scene;
s2.3, calculating a reaction distance according to the reaction time;
s2.4, calculating the parking sight distance according to the reaction distance.
4. A highway autopilot vehicle brake spacing control method in accordance with claim 3 wherein said ride following scenario includes a ride following lead vehicle, said ride following vehicle including an autopilot vehicle and a natural person driving vehicle, said lead vehicle including an autopilot vehicle and a natural person driving vehicle.
5. The method for controlling the braking distance of an expressway automatic driving vehicle according to claim 4, wherein said S2.2 specifically comprises: based on a following model, collecting experimental parameters in a traffic flow simulation mode, screening vehicle data with specific numbers from the experimental parameters to serve as following vehicle data, determining a leading vehicle and a lane where the leading vehicle is located according to the following vehicle data, extracting relative speed data of the following vehicle and the leading vehicle and acceleration data of the following vehicle to perform correlation analysis, and when the cross correlation coefficient is maximum, obtaining the relative speed of the following vehicle and the leading vehicle/the acceleration of the following vehicle = the reaction time of an automatic driving vehicle.
6. The method for controlling the braking distance of an automatic highway driving vehicle according to claim 5, wherein the rule for collecting experimental parameters is as follows: during data acquisition, the following vehicle and the leading vehicle are both positioned on the same lane, and the relation between following and being followed is always kept, the shortest time of the following vehicle to follow the leading vehicle is larger than a first preset value, and the number of data records of the following vehicle to follow the leading vehicle is larger than a second preset value.
7. The method for controlling the braking distance of an automatically driven vehicle on an expressway according to claim 6, wherein said stopping visual distance=reaction distance+braking distance.
8. The method for controlling the braking distance of an automatically driven vehicle on an expressway according to claim 7, wherein said braking distance=a braking force increase stage distance+a full braking stage distance.
9. The method for controlling the braking distance of an expressway automatic driving vehicle according to claim 4, wherein said S3 specifically comprises:
the following car monitors the acceleration of the leading car;
when the acceleration of the front guided vehicle is smaller than the braking acceleration of the following vehicle, the following vehicle continues to run;
when the acceleration of the leading vehicle is larger than the braking acceleration of the following vehicle, the following vehicle keeps a corresponding safe braking distance with the leading vehicle according to the vehicle attribute of the leading vehicle.
10. A highway autopilot vehicle brake clearance control system comprising:
the acquisition module is used for acquiring highway road traffic scene related data;
the braking interval control module is used for constructing a braking model according to the related data of the expressway road traffic scene; calculating the stopping sight distance of the automatic driving vehicle according to the braking model; and performing brake interval control according to the parking sight distance of the automatic driving vehicle.
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