Vehicle braking distance control method and system based on parking sight distance

Abstract

The application is suitable for the technical field of vehicle control, and particularly relates to a vehicle braking distance control method and system based on a parking sight distance, which comprises the following steps: acquiring traffic scene data, and constructing a heterogeneous traffic following scene model; calibrating reaction time through traffic flow simulation, wherein the step of calibrating reaction time comprises data acquisition and processing and cross-correlation result analysis; calculating reaction distance and braking distance of an automatic driving vehicle under a heterogeneous flow; and controlling the vehicle based on the reaction distance and the braking distance of the automatic driving vehicle, so that the automatic driving vehicle and a preceding vehicle maintain a corresponding safe braking distance. The application can calibrate the parking sight distance of the vehicle through traffic flow simulation, determine the braking distance, and control the automatic driving vehicle and the preceding vehicle to always maintain a safe parking distance through a control system. Since the parking sight distance is reduced and the distance between the vehicles is reduced, the traffic efficiency can be greatly improved under the condition of ensuring traffic safety.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle control, and in particular relates to a method and system for controlling a vehicle braking distance based on parking sight distance. Background Art

[0002] Expressways, currently the predominant mode of long-distance driving, are the highest-grade highways. They are designed for high speeds, have high road linearity design indicators, and therefore operate at relatively high speeds. In emergencies, drivers have limited time to make decisions, and improper handling can easily lead to serious traffic accidents, resulting in significant loss of life and property. Therefore, expressway traffic safety is of paramount importance. Numerous factors influence expressway driving safety, with road alignment being one of the most important. Among these, sight distance, as the most fundamental road alignment design indicator, has a significant impact on driving safety. Ensuring adequate sight distance in expressway alignment design is crucial for ensuring driving safety and comfort. Poor sight distance can hinder drivers' ability to make informed decisions, leading to traffic accidents.

[0003] In recent years, with the continuous advancement of intelligent and information-based technologies, autonomous driving technology has continued to develop and is gradually becoming a major development direction for the future. Furthermore, due to the development of autonomous driving technology and the formulation of national policies, autonomous vehicles will gradually infiltrate existing traffic flows at a certain rate in the future, forming a heterogeneous traffic flow with a certain penetration rate. The addition of autonomous vehicles will significantly differ from the vehicle's response and braking performance compared to traditional manually driven vehicles. As a key road alignment design indicator for ensuring driving safety, it is necessary to study the adaptability of stopping sight distance to heterogeneous traffic flows and use stopping sight distance as a basis for controlling the braking distance of autonomous vehicles to ensure traffic safety on highways.

[0004] Domestic researchers have primarily focused on improving stopping sight distance model theory and road alignment verification methods. Some have reclassified the braking process to establish a stopping sight distance calculation model, while others have modified the stopping sight distance using operating speed and braking deceleration. These studies have conducted in-depth research on stopping sight distance, arguing that stopping sight distance calculation primarily consists of reaction distance and braking distance. These studies have been widely recognized, but they have all focused on manually driven vehicles, with no research on stopping sight distance for autonomous vehicles. Furthermore, in research on reaction time, a crucial parameter involved in calculating stopping sight distance, current calculations rely on empirical values, lacking quantitative analysis.

[0005] During driving, a vehicle follows the preceding vehicle at a certain distance based on a following model. Currently, the ACC following model is primarily used to control the following distance in autonomous driving. The safe stopping distance parameter in the ACC following model still refers to empirical values. Analysis has not been conducted for the specific scenario of a sudden change in the preceding vehicle's state. Furthermore, when following a preceding vehicle, a certain braking safety distance must be maintained between vehicles. In actual driving, if an autonomous vehicle, acting as a following vehicle, controls the braking safety distance from the preceding vehicle based on the stopping sight distance, it would be overly conservative. However, no research has yet been conducted on braking distance control systems. Summary of the Invention

[0006] The purpose of the embodiments of the present invention is to provide a vehicle braking distance control method based on parking sight distance, aiming to solve the problem that the parameters of the safe parking distance in the prior art only refer to empirical values, resulting in the safety distance maintained by the autonomous driving vehicle being too conservative.

[0007] The embodiment of the present invention is implemented as follows: a vehicle braking distance control method based on stopping sight distance, the method comprising:

[0008] Acquire traffic scene data and build a heterogeneous traffic following scenario model;

[0009] Performing reaction time calibration through traffic flow simulation, wherein the reaction time calibration step includes data collection and processing and cross-correlation result analysis;

[0010] Calculate the reaction distance and braking distance of autonomous vehicles under heterogeneous flow;

[0011] The vehicle is controlled based on the reaction distance and the braking distance of the autonomous vehicle, so that the autonomous vehicle maintains a corresponding safe braking distance with the leading vehicle.

[0012] Preferably, the steps of performing data collection and processing specifically include: using the Krause following model as the control model of the manually driven vehicle and the ACC following model as the following model of the autonomous driving vehicle, performing traffic flow simulation, and obtaining experimental parameters. During the simulation, from the start time to the end time of data collection, the following vehicle and the followed vehicle are always in the same lane, and the following and followed relationship is always maintained. The time that the following vehicle follows the followed vehicle is not less than a first preset value, and the data records of the following vehicle and the followed vehicle finally collected are not less than a second preset value.

[0013] Preferably, the step of analyzing the related results specifically includes: selecting a vehicle with a specific number in the simulation data as a following vehicle, finding the leading vehicle and the lane in front of the vehicle according to the simulation data, extracting the relative speed data Δv(t) of the two vehicles, and comparing it with the acceleration data a of the following vehicle. n+1(t), and a cross-correlation analysis is performed through the software. During the cross-correlation analysis process, when the cross-correlation coefficient is the largest, it corresponds to the reaction time of the target vehicle. Based on this, the reaction time of different vehicles in different following situations can be determined.

[0014] Preferably, in the step of calculating the reaction distance, the reaction distance under different following conditions is calculated as follows:

[0015] S r,i =v i T s,i (i=1,2,3,4),

[0016] Where S r,i represents the reaction distance of the vehicle in the ith following situation, in meters; v i Indicates the following speed of the following vehicle in the ith following case, in m / s; T s,i It represents the reaction time of the following vehicle in the ith following situation, in seconds.

[0017] Preferably, the braking distance of the autonomous driving vehicle includes the braking force rising stage distance and the full braking stage distance.

[0018] Preferably, the braking force increasing stage distance is calculated by the following formula:

[0019]

[0020] Among them, τ3 is the time of braking force rise, unit is s, v0 is the initial speed of the vehicle before braking, unit is m / s 2 , a max is the maximum braking deceleration, m / s 2 .

[0021] Preferably, the full braking stage distance is calculated using the following formula:

[0022]

[0023] The braking distance S2 is expressed as:

[0024]

[0025] Preferably, the safe braking distance is calculated by the following formula:

[0026]

[0027] Where: d is the safe braking distance that the following vehicle and the leading vehicle need to maintain, in meters; v C is the speed of the following vehicle, in km / h; v A is the speed of the leading vehicle, in km / h; Dmax is the parking sight distance of the autonomous vehicle, in meters.

[0028] Another object of an embodiment of the present invention is to provide a vehicle braking distance control system based on stopping sight distance, the system comprising:

[0029] Scenario construction module, used to obtain traffic scenario data and construct heterogeneous traffic following scenario models;

[0030] A traffic flow simulation module is used to perform reaction time calibration through traffic flow simulation, wherein the reaction time calibration step includes data collection and processing and cross-correlation result analysis;

[0031] Braking distance calculation module, used to calculate the reaction distance and braking distance of the autonomous driving vehicle under heterogeneous flow;

[0032] The safety distance control module is used to control the vehicle based on the reaction distance and the braking distance of the autonomous driving vehicle, so that the autonomous driving vehicle maintains a corresponding safe braking distance with the leading vehicle.

[0033] The vehicle braking distance control method based on stopping sight distance provided by the embodiment of the present invention can calibrate the vehicle stopping sight distance and determine the braking distance through traffic flow simulation. The control system controls the autonomous driving vehicle to always maintain a safe stopping distance from the vehicle in front. Since the stopping sight distance is reduced, the distance between vehicles is reduced, which can greatly improve traffic efficiency while ensuring traffic safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of a vehicle following situation in heterogeneous traffic flow provided by an embodiment of the present invention;

[0035] Figure 2 A schematic diagram of the braking process of an autonomous driving vehicle according to an embodiment of the present invention;

[0036] Figure 3 A control principle diagram of a braking distance control system for an autonomous driving vehicle provided by an embodiment of the present invention;

[0037] Figure 4 A correlation diagram of the reaction times of autonomous driving and human driving provided in an embodiment of the present invention;

[0038] Figure 5 Screenshots of heterogeneous traffic flow simulation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0040] It is understood that the terms "first," "second," etc., used herein may be used to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script without departing from the scope of this application.

[0041] An embodiment of the present invention provides a method for controlling vehicle braking distance based on stopping sight distance, the method comprising:

[0042] Acquire traffic scene data and build a heterogeneous traffic following scenario model;

[0043] Performing reaction time calibration through traffic flow simulation, wherein the reaction time calibration step includes data collection and processing and cross-correlation result analysis;

[0044] Calculate the reaction distance and braking distance of autonomous vehicles under heterogeneous flow;

[0045] The vehicle is controlled based on the reaction distance and the braking distance of the autonomous vehicle, so that the autonomous vehicle maintains a corresponding safe braking distance with the leading vehicle.

[0046] In one embodiment of the present invention, the impact of the introduction of autonomous vehicles on existing traffic flow is considered, different car-following combinations of vehicles are analyzed, reaction times are calibrated based on simulated car-following data, braking models for autonomous vehicles and manually driven vehicles are constructed, the stopping sight distance for autonomous vehicles is calculated, and a braking distance control system is constructed based on the autonomous stopping sight distance. The specific steps are as follows:

[0047] A: Determine the relevant data of highway traffic scenarios: design speed, traffic volume and other information;

[0048] B: Calculation of stopping sight distance under heterogeneous traffic flow:

[0049] a) Construction of car-following scenarios with heterogeneous traffic flows:

[0050] For heterogeneous traffic flows where autonomous vehicles and human-driven vehicles mix, the driving behaviors of autonomous vehicles and autonomous vehicles will have mutual influences, and the degree of influence varies. Therefore, the influence between the two needs to be discussed separately. Through the analysis of the combination of the two types of vehicles, it can be seen that there are four main types of following situations in the same lane, see the attached Figure 1 .

[0051] b) Reaction time calibration:

[0052] 1. Data collection and processing:

[0053] The Krause and ACC following models are used as following models for manually driven vehicles and autonomous vehicles, respectively, and experimental parameters are obtained through traffic flow simulation. The following criteria are used when selecting research data in this paper: from the start time to the end time of data collection, the following vehicle and the followed vehicle are always in the same lane and the following and followed relationship is always maintained. This is to avoid the impact of vehicle lane changing behavior on following behavior; the shortest time that the following vehicle follows the followed vehicle is 30 seconds, that is, there are at least 300 data records of the following vehicle and the followed vehicle in the data.

[0054] 2. Cross-correlation results analysis:

[0055] Taking a specific numbered vehicle as an example, this vehicle is selected as the following vehicle data in the simulation data. Based on the data, the leading vehicle in front of it and the lane it is in are found. The relative speed data Δv(t) of the two vehicles is extracted, and the acceleration data a of the following vehicle is used to calculate the relative speed of the two vehicles. n+1 (t) and cross-correlation analysis using EVIEWS 5.1. The maximum cross-correlation coefficient is considered the target vehicle's reaction time. Using this method, the reaction times of different vehicles in different car-following situations can be determined.

[0056] c) Reaction distance calculation:

[0057] It is now assumed that the autonomous driving vehicle and the manually driven vehicle maintain their original speed while perceiving information about the road and vehicles ahead, as well as making relevant judgments and decisions. Based on the analysis of different following situations above, we can see that i represents the i-th following situation. The calculation of the reaction distance under different following situations is shown in formula (1).

[0058] S r,i =v i T s,i (i=1,2,3,4) (1)

[0059] Where S r,i represents the reaction distance of the vehicle in the i-th following situation, m; v ivi represents the car-following speed of the following vehicle under the ith car-following condition, m / s; T s,i ti represents the reaction time of the following vehicle under the ith car-following condition, s.

[0060] d) Construction of the braking process of the autonomous vehicle:

[0061] The braking process of the autonomous vehicle mainly includes the following three processes: a) the process of the autonomous vehicle perceiving the front danger and obstacle and processing the information, and the time required for this process is called the system "reaction time"; b) the process of the vehicle making a decision to decelerate, the braking deceleration being generated and rising to the maximum, and the time required for this process is called the braking force rising time; c) the process of the vehicle braking with the maximum deceleration, and the time required for this process is called the full braking time. The specific processes are shown in the attached figure. Figure 2 .

[0062] e) Calculation of the braking distance of the autonomous vehicle under heterogeneous flow:

[0063] ① Distance S in the braking force rising stage 2-1 :

[0064] The braking deceleration at any time in the braking force rising time is calculated as shown in formula (2):

[0065]

[0066] In the formula, a max is the maximum braking deceleration, m / s 2 .

[0067] The vehicle speed at any time t in this period is calculated as shown in formula (3) and (4):

[0068]

[0069]

[0070] In the formula, v0 is the initial speed of the vehicle before braking, m / s 2 .

[0071] The driving distance of the vehicle in the braking force rising stage is calculated as shown in formula (5):

[0072]

[0073] In the formula, τ3 is the time of the braking force rising, s.

[0074] ② Distance S in the full braking stage 2-2

[0075] The vehicle speed at the start of this period (i.e. the end of the braking force rising time period) is calculated as shown in formula (6):

[0076]

[0077] The vehicle speed at any time during this stage is as shown in formula (7):

[0078] v(t)=v s -a max t (7)

[0079] During the full braking time, the vehicle brakes at the maximum deceleration a max Change the speed from v s The time it takes to decrease to 0 is τ4=v s / a max During this time period, the vehicle travels a distance S 2-2 The calculation of is shown in formula (8):

[0080]

[0081] It can also be expressed as,

[0082] According to formula (8) and formula (5), the braking distance S2 can be obtained as:

[0083]

[0084] The parameters in the formula are the same as above

[0085] f) Calculation of parking sight distance for autonomous driving vehicles under heterogeneous traffic flow

[0086] Based on the above analysis, the stopping sight distance of the autonomous vehicle under different following conditions in heterogeneous traffic flow can be calculated, as shown in formula (10):

[0087]

[0088] Where v0 is the initial speed of the autonomous vehicle before braking, m / s 2 .

[0089] C: Braking distance control system

[0090] The braking distance control system consists of two parts: the leading vehicle driving state determination and the braking distance control. Figure 3 .

[0091] a) During the driving process, the following vehicle determines the driving status of the leading vehicle by monitoring its acceleration. When its acceleration is less than the braking acceleration, it is a normal speed adjustment, the leading vehicle is in normal driving state, and the following vehicle can continue to drive. When its acceleration is greater than the braking acceleration, the leading vehicle is in braking state. At this time, the following vehicle must maintain a safe braking distance with the leading vehicle and execute b).

[0092] b) After the following vehicle determines that the leading vehicle is in a braking state, it first determines the vehicle attributes of the leading vehicle, i.e., an autonomous vehicle or a manually driven vehicle, and then maintains a corresponding safe braking distance with the leading vehicle based on the attributes of the leading vehicle.

[0093] The safe braking distance is calculated based on the stopping sight distance of different vehicle attributes, as shown in the following formula:

[0094]

[0095] Where: d is the safe braking distance between the following vehicle and the leading vehicle, m; v C is the speed of the following vehicle, km / h; v A is the speed of the leading vehicle, km / h; D max is the stopping sight distance of the autonomous vehicle, m.

[0096] To illustrate the technical effects of this application, the following examples are given:

[0097] A: Road traffic scenario determination: The main line is six lanes in both directions (three lanes in one direction), with a design speed of 120 km / h; the ramp is a single lane with a design speed of 70 km / h; the road design hourly traffic volume is 2200 veh / h;

[0098] B: Stopping sight distance calculation under heterogeneous traffic flow

[0099] 1. Calibration of reaction time of different vehicles

[0100] The database contains 450 records of vehicles that follow the vehicle. The data shows that during the data collection period, this vehicle has been following vehicle number X in lane number Y. The relative velocity data {Δv(t)} of the vehicle and the acceleration data {a(t)} of the following vehicle were extracted and cross-correlated with each other using EVIEWS 5.1. The results are shown in the attached figure. Figure 4 As shown in the figure, it can be seen that when the reaction time is 0.2 seconds, the mutual correlation coefficient of the autonomous driving vehicle is the largest, so 0.2 seconds is considered to be the reaction time of the autonomous driving vehicle. Similarly, when the reaction time is 0.8 seconds, the mutual correlation coefficient of the natural person driving the vehicle is the largest, so 0.8 seconds is considered to be the reaction time of the natural person driving the vehicle.

[0101] 2. Calculation of Stopping Sight Distance on Highways with Heterogeneous Traffic Flow

[0102] According to formula (31), the stopping sight distances of autonomous vehicles and manually driven vehicles at different design speeds can be calculated. The results are shown in Table 1.

[0103] Table 1 Stopping sight distance values ​​for autonomous and manually driven vehicles

[0104]

[0105] 3. Braking distance control system:

[0106] In the simulation software SUMO, a heterogeneous traffic flow simulation platform is built according to the road traffic environment in A, as shown in the attached figure. Figure 5 As shown in the figure, the stopping sight distance of vehicles is calibrated using the standard values ​​in the route design specifications and the calculated values ​​from the model. A braking distance control system is used to control the safe distance of the autonomous vehicle. This control system outputs data such as vehicle speed, acceleration, and Time-To-Collision (TTC). The resulting vehicle information data is used to compare traffic efficiency and safety under two traffic environments, verifying the practicality and effectiveness of the control system.

[0107] Table 2 Comparison of traffic simulation results

[0108]

[0109] The calculation results in Table 2 indicate that, compared to the stopping sight distance in the Standard, the stopping sight distance determined in this paper increases traffic flow efficiency by 42,792 km, a 30.44% improvement. Due to the reduction in stopping sight distance, the headway between vehicles remains shorter, resulting in a slight increase in the traffic conflict rate of 0.0067 times / (pcu·km), a non-significant increase. Therefore, the stopping sight distance determined in this paper can significantly improve traffic efficiency while ensuring traffic safety.

[0110] An embodiment of the present invention further provides a vehicle braking distance control system based on parking sight distance, the system comprising:

[0111] Scenario construction module, used to obtain traffic scenario data and construct heterogeneous traffic following scenario models;

[0112] A traffic flow simulation module is used to perform reaction time calibration through traffic flow simulation, wherein the reaction time calibration step includes data collection and processing and cross-correlation result analysis;

[0113] Braking distance calculation module, used to calculate the reaction distance and braking distance of the autonomous driving vehicle under heterogeneous flow;

[0114] The safety distance control module is used to control the vehicle based on the reaction distance and the braking distance of the autonomous driving vehicle, so that the autonomous driving vehicle maintains a corresponding safe braking distance with the leading vehicle.

[0115] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0116] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0117] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0118] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0119] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A vehicle braking distance control method based on parking sight distance, characterized in that: The method comprises: Acquire traffic scene data and build a heterogeneous traffic following scenario model; Performing reaction time calibration through traffic flow simulation, wherein the reaction time calibration step includes data collection and processing and cross-correlation result analysis; Calculate the reaction distance and braking distance of autonomous vehicles under heterogeneous flow; The vehicle is controlled based on the reaction distance and the braking distance of the autonomous vehicle, so that the autonomous vehicle maintains a corresponding safe braking distance with the leading vehicle; The steps of performing data collection and processing specifically include: using the Krause following model as a control model for a manually driven vehicle and the ACC following model as a following model for an autonomous vehicle, performing traffic flow simulation, and obtaining experimental parameters; during the simulation, from the start time to the end time of data collection, the following vehicle and the followed vehicle are always in the same lane and maintain the following and followed relationship; the time the following vehicle follows the followed vehicle is not less than a first preset value, and the data records of the following vehicle and the followed vehicle finally collected are not less than a second preset value; The steps of cross-correlation result analysis specifically include: selecting a vehicle with a specific number in the simulation data as a following vehicle, finding the leading vehicle and the lane in front of it according to the simulation data, extracting the relative speed data Δv(t) of the two vehicles, and comparing it with the acceleration data a of the following vehicle. n+1 (t), cross-correlation analysis is performed through software. During the cross-correlation analysis process, the maximum cross-correlation coefficient corresponds to the reaction time of the target vehicle. Based on this, the reaction time of different vehicles in different following situations is determined; The braking distance of an autonomous vehicle includes the braking force increasing stage distance S 2-1 And the full braking stage distance S 2-2 ; The braking force increasing stage distance S 2-1 Calculated by the following formula: in, is the time for the braking force to rise, in seconds, and v0 is the initial speed of the vehicle before braking, in m / s 2 , a max is the maximum braking deceleration, m / s 2 ; The full braking stage distance S 2-2 Calculated by the following formula: The braking distance S2 is expressed as:

2. The vehicle braking distance control method based on stopping sight distance according to claim 1, characterized in that: In the step of calculating the reaction distance, the reaction distances under different following conditions are calculated as follows: S r,i =v i T s,i (i=1,2,3,4), Where S r,i represents the reaction distance of the vehicle in the ith following situation, in meters; v i Indicates the following speed of the following vehicle in the ith following case, in m / s; T s,i It represents the reaction time of the following vehicle in the ith following situation, in seconds.

3. The vehicle braking distance control method based on stopping sight distance according to claim 1, characterized in that: The safe braking distance is calculated using the following formula: Where: d is the safe braking distance that the following vehicle and the leading vehicle need to maintain, in meters; v c is the speed of the following vehicle, in km / h; v A is the speed of the leading vehicle, in km / h; D max is the parking sight distance of the autonomous vehicle, in meters.

4. A vehicle braking distance control system based on parking sight distance, used to implement the control method according to any one of claims 1 to 3, characterized in that: The system comprises: Scenario construction module, used to obtain traffic scenario data and construct heterogeneous traffic following scenario models; A traffic flow simulation module is used to perform reaction time calibration through traffic flow simulation, wherein the reaction time calibration step includes data collection and processing and cross-correlation result analysis; Braking distance calculation module, used to calculate the reaction distance and braking distance of the autonomous driving vehicle under heterogeneous flow; The safety distance control module is used to control the vehicle based on the reaction distance and the braking distance of the autonomous driving vehicle, so that the autonomous driving vehicle maintains a corresponding safe braking distance with the leading vehicle.

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