Next Article in Journal
Enhancing the UV Response of All-Inorganic Perovskite Photodetectors by Introducing the Mist-CVD-Grown Gallium Oxide Layer
Previous Article in Journal
Effect of Damage on the Corrosion Performance of Thermal Spray Aluminium (TSA) Coating in Synthetic Seawater
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Research on Safe Following Distance on an Expressway Based on Braking Process Analysis

School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(2), 1110; https://doi.org/10.3390/app13021110
Submission received: 11 December 2022 / Revised: 6 January 2023 / Accepted: 11 January 2023 / Published: 13 January 2023
(This article belongs to the Section Transportation and Future Mobility)

Abstract

:
As an important part of the vehicle active collision avoidance system, improvement of the minimum safe following distance model has aroused the interest of researchers at home and abroad. According to the characteristics of the safe driving of vehicles on expressways, the braking process of vehicles is analyzed using the principle of kinematics. According to the requirements of the safe following distance between vehicles, the safe following distance of different braking system structures, different following states, different speeds and different slope sections on expressways is quantitatively analyzed. Three different safe following distance calculation methods based on braking deceleration under different slope conditions are established. Matlab software was used for simulation analysis. The simulation results show that the calculation method of the safe following distance meets the requirements of keeping safe distance in the Implementation Regulations of Road Traffic Safety Law of the People’s Republic of China and provides a more specific reference basis for speed limit used on expressways. This study provides a theoretical basis for the application and interpretation of the parking sight distance value stipulated in the Design Specification for Highway Alignment, and has certain practical guiding significance for improving the density and transportation efficiency of expressway vehicles and reducing the incidence of rear-end traffic accidents.

1. Introduction

As a fast, flexible and economical means of transportation, cars are generally favored by people over other forms of transportation. Although cars bring convenience to people, the following problems are also obvious: with the increase of vehicles, traffic accidents happen more frequently, causing an alarming number of casualties and property losses. Rear-end collisions caused by inaccurate spacing and improper control between vehicles traveling at high speed on expressways has become one of the main forms of traffic accidents in China in recent years. The analysis of highway rear-end collisions shows that more than 80% of the accidents are caused by the driver’s slow response, and more than 65% of the car collisions are rear-end collisions. The main reason for this kind of accident is that the speed of the driving vehicle is too high, and the driver fails to maintain the corresponding safe distance. According to the research of Mercedes-Benz on various traffic accidents, if drivers can realize the danger of accidents in advance and take appropriate measures, the vast majority of traffic accidents can still be avoided [1]. In the same direction of the highway, there are complete following, incomplete following and other driving conditions. As far as driving safety is concerned, different following states have different requirements for safe driving distance. If the distance between vehicles in longitudinal alignment on the expressway is kept completely in line with the requirements of stopping sight distance, rear-end collision accidents can be completely avoided, but road capacity will be greatly reduced, resulting in a serious waste of road capacity. As an important part of the vehicle active collision avoidance system, improvement of the minimum safe following distance model has aroused the interest of researchers at home and abroad. In the collision avoidance system, the determination of safe following distance completely depends on the safety distance model adopted. Therefore, whether the safe following distance model is reasonable is of great significance to improve the reliability and effectiveness of the collision avoidance system. In order to ensure traffic safety between vehicles on the expressway and bring the road capacity into play, it is particularly important to discuss the determination and internal relationship of the appropriate value of the safe distance between vehicles running in the same direction on the expressway in different following states, different speeds and different slope sections. Study into the highway safety following distance has far-reaching significance for improving traffic safety level and service quality.

2. Related Work

There are many reasons for causing traffic accidents, among which, the failure to maintain a safe following distance accounts for a large proportion. Peng et al. analyzed 1521 expressway traffic accidents in mountainous areas from 2012 to 2017 and found that one of the most common factors involved in serious accidents in the daytime was the failure to maintain a safe distance [2]. He et al. analyzed the causes of road traffic accidents on foggy days and believed that the main reason was that the drivers failed to allow a sufficient following distance [3].
There are many reasons for having a dangerous following distance, such as driving speed, driver conditions and road conditions. The method of solving these problems and reducing the occurrence of traffic accidents is of particular importance. In 2012, Li et al. designed a rear-end collision warning system based on Zigbee technology, which can compare the actual distance measured by radar with the safe distance and provide early warning information for drivers [4]. In 2013, Wang et al. proposed an improved safety distance model (SDM) based on vehicle-to-vehicle communication technology, which realized real-time data acquisition and real-time workshop communication and could effectively improve the efficiency of a vehicle anti-collision system [5]. In 2014, Wang et al. proposed a self-optimizing PID model based on fuzzy control which could assist a car in following another car at a safe distance, and this model was established on a wireless sensor network sharing information for perceiving different statuses of the traffic stream [6]. Cao et al. established a practical calculation model for safe gaps. In addition, they used Matlab to simulate three different safe gaps under dry pavement [7]. In 2018, Gargoum pointed out in his paper that if the available sight distance is less than the required sight distance, the possibility of the driver completing a certain action will be reduced [8]. In the article, he also proposed a process through which the sight distance of the highway can be evaluated. In 2013, He et al. designed an effective, intelligent traffic safety distance control system for foggy days [3]. In 2022, Sroczyński et al. proposed a new design method for a recommendation system which can suggest the safe speed on the road, and the system can suggest the safe speed for overtaking vehicles to prevent collisions by taking into account the distance from the previous vehicle [9].The rational design of roads is also of great significance to the safe driving of vehicles. Zhang et al. discussed the safe driving conditions on the highway and obtained the relationship among the design length of the vertical curve, the stopping sight distance and the slope algebra, and gave treatment suggestions [10]. If the conditions of a section of road cannot meet the requirements of safe sight distance, traffic engineering control measures can be taken to ensure traffic safety as much as possible. Fambro et al. established a parking sight distance calculation model based on driver and vehicle performance and recommended the parking sight distance of different models through actual data verification [11]. Hassan et al. established the line-of-sight calculation model by analyzing drivers’ characteristics and road alignment and other factors [12]. Crisman et al. for the first time, used the friction coefficient between the tire and the ground instead of the vehicle speed reduction in an emergency as the main calculation parameter in the calculation of sight distance [13]. Nehate et al. established a new parking sight distance model based on GPS data [14]. Hassan et al. proposed the mathematical model and calculation method of 3D sight distance through parametric analysis of road section [15]. According to the coordinate equation and cross section parameters of road median line, Zhang et al. put forward the highway 3D sight distance test technology [16]. Jiang et al. reclassified the braking process and established the stopping sight distance model under different road conditions [17]. Yang corrected the stopping sight distance by running speed and braking speed reduction [18]. Xun established the parking sight distance test model and used the simulation platform to test and evaluate the sight distance on the highway in mountainous areas [19]. Yang et al. revised the parking sight distance model from three aspects and put forward suggestions to ensure the parking sight distance from the angle of the height of the target [20].
According to the characteristics of the safe driving of vehicles on an expressway, this paper analyzes the braking process of vehicles with regard to the kinematic principle and establishes three different safe following distance calculation methods based on braking deceleration under different slope conditions. We used Matlab software for simulation analysis. The results of the simulation can be a specific and clear reference for the limited speeds used on expressways. This study has certain practical guiding significance for improving the vehicle density and transportation efficiency of expressways and reducing the incidence of rear-end traffic accidents.

3. Modeling

3.1. Analysis and Assessment of Braking Distance

3.1.1. Running Status of Automobiles

Let car A (the front car) and car B (the following car), which are arranged longitudinally and separated by distance “D” (unit: m), drive successively in the same direction on the same road. This is shown in Figure 1. The two cars drive at the speeds of VA and VB, respectively.
It is assumed that the driver of car A starts to brake after finding obstacles in the front of the road. When the speed of car A decreases to V’A, the distance driven by car A is DA. At the same time, the driver of car B starts to brake after observing car A’s braking, and the distance traveled by car B when its speed decreases to V’B is DB. The symbol d (m) is the actual distance between car A and car B when car A and car B reach the post-braking speed, V’A and V’B, respectively, as required by design.

3.1.2. Analysis of the Automobile Braking Process

In order to establish a reasonable minimum following distance model, it is necessary to have a comprehensive understanding of the braking process [21]. Assuming that the front car, A, suddenly appears in some emergency situation (sudden deceleration and stopping or cargo scattering), the curves of the following car’s brake-pedal force, braking deceleration and braking time are as shown in Figure 2, when the driver of the following car enacts emergency braking. In Figure 2, Fp is brake-pedal force and j is brake deceleration.
According to Figure 2, the driver needs to finish five periods in the actual braking process, and the specific analysis is as follows.
(1)
The driver’s reaction time t1: the time required for the driver to move the right foot to the brake pedal after the driver detects a dangerous situation and begins to react.
(2)
The brake coordination time t2: the time required from starting to step down on the brake pedal to eliminate the brake pedal clearance, clearance of various hinges and bearings and drum brake clearance.
(3)
The braking force growth time t3. At this stage, the braking force gradually increases from zero to the maximum; that is, the braking deceleration increases from zero to the maximum, and the car moves through variable deceleration.
(4)
The continuous braking time t4. At this stage, the braking force is basically maintained at a stable value; that is, the brake deceleration remains unchanged, and the car goes through uniform deceleration until stopping.
(5)
The braking relaxation time t5, that is, the time that is required after stopping until the automatic elimination of braking force.

3.1.3. Problem Simplification

In order to obtain the critical safe distance between two cars and carry out theoretical analysis, it is necessary to perform a specific analysis of driving on an expressway and make some simplifications [22].
(1)
The car movement in a short time on the expressway is simplified to a uniform movement. Assume that at a certain moment on the expressway, the front car (A) is moving at the speed of VA and the following car (B) is moving at the speed of VB. In this case, the distance between car A and car B is “D”. This is shown in Figure 1.
(2)
With the passage of time, the distance between the front car (A) and the following car (B) has two possible changing trends. One is that the distance between the two cars is getting larger and larger, which obviously eliminates the problem of safe following distance between the two cars, so this situation is not the process to be discussed. We are concerned about the other situation, where the distance between the two cars gets smaller and smaller, and if no action is taken, there will be a rear-end collision. Among the possible situations, the most dangerous is that either the front car brakes and the following car accelerates, or the front car brakes and the following car still moves at its original speed. However, those two scenarios are not what the article is talking about here, as, in them, the following car is likely trying to overtake the front car, the two cars are not in the same lane, or the crash is intentional. Obviously, the situation in question is that on the expressway, the front car and the following car are moving in the same direction in the same lane, and the following car does not intend to pass the car in front, but just follows the front car. At a certain moment, car A suddenly brakes. The driver of car B will discover that the distance between the two cars is decreasing, so the following car immediately brakes to avoid a collision between the two cars (assuming that there is no possibility or possible behavior of steering to avoid the crash). In this case, when the following car adopts braking, the front car and the following car will not collide. We define the minimum distance that the two cars must keep at the moment before the front car brakes as the critical safe distance.
(3)
We assume that the driving conditions of the two cars are the same and the drivers are in the same condition. That is, it is assumed that the braking parameters of the two cars are the same and the drivers’ abilities to respond are the same.

3.1.4. Braking Distance

(1)
Analysis of braking deceleration process of the following car
The driver of the following car completes the whole braking process after receiving the braking signal. According to the above braking process analysis, in the whole braking process, the actual distance traveled by the following car includes four periods: the driver’s reaction time t1, the brake braking coordination time t2, the braking force growth time t3 and the continuous braking time t4.
As the brakes do not have an effect during t1 and t2, the distance (DB1) traveled by car B in times t1 and t2 is
DB1 = VB × (t1 + t2)
The distance traveled by car B in time t3 is
D B 2 = 0 t 3 ( V B j B max 2 t 3 t 2 ) d t = V B t 3 j B max 6 t 3 2
Meanwhile, at the end of time t3, the speed V3 of car B is as follows.
V 3 = V B j B max 2 t 3
The distance traveled by car B during t4 is
D B 3 = V 3 2 V B 2 2 j B max = V B 2 V B 2 2 j B max V B t 3 2 + 1 8 j B max t 3 2
The total braking distance of car B is as follows:
D B = D B 1 + D B 2 + D B 3 = V B ( t 1 + t 2 + t 3 2 ) + V B 2 V B 2 2 j B max j B max 24 t 3 2
(2)
Braking distance analysis of the front car
Theoretically, the front car should also undergo the same deceleration process as the following car. Therefore, in theory, the braking distance D A of the front car is similar to that of the following car.
D A = D A 1 + D A 2 + D A 3 = V A ( t 1 + t 2 + t 3 2 ) + V A 2 V A 2 2 j A max j A max 24 t 3 2
During actual driving, the driver of car B is sure that the front car has implemented braking after the brake lights of car A come on. Therefore, the distance covered by car A during the braking process should only be the distance driven by the car in the braking force growth stage and the continuous braking stage. Therefore, the braking distance D A of car A during t2 and t3 can be obtained as follows:
D A = D A 2 + D A 3 = V A t 3 2 + V A 2 V A 2 2 j A max j A max 24 t 3 2

3.2. Establishment of a Mathematical Model of Minimum Safe Distance

3.2.1. Analysis of Safe Distance

If the front car and the following car do not collide, from the perspective of driving speed, the ideal situation is that the final speeds of car A and car B are equal; that is, V’B = V’A (including a requirement that the final speed of car A and car B is zero). Otherwise, the two cars will collide (if V’B > V’A) or the calculated result is not the critical distance (V’B < V’A). In addition, if the front car and the following car do not collide, it can be seen in Figure 1 that the driving distance must align with the following formula:
DA + D ≥ DB + d
Therefore, the distance “D” between the front car and the following car before braking must align with the following formula:
D ≥ DB + d − DA
Therefore, in order to avoid collision between car A and car B, the critical safe distance D* must align with the following formula:
D* = DB + d − DA
According to the above analysis, it can be seen that the required distance between car A and car B in the longitudinal arrangement varies with speed, braking performance, the value of “d” and other factors. In practice, “d” can be viewed as a certain value. Therefore, when the driving speed and braking performance are different for car A and car B, the required safe following distance will also be different.

3.2.2. Discussion of Safe Distance

According to the analysis of the braking distance of the front car, three different safety distances were obtained.
(1)
Minimum safe distance D1
Obviously, the critical safe distance is calculated by considering the response of the driver of car A and the braking time.
D 1 = D B + d D A = V B ( t 1 + t 2 + t 3 2 ) + V B 2 V B 2 2 j B max j B max 24 t 3 2 ( V A ( t 1 + t 2 + t 3 2 ) + V A 2 V A 2 2 j A max j A max 24 t 3 2 ) + d = ( t 1 + t 2 + t 3 2 ) ( V B V A ) + V B 2 V B 2 2 j B max V A 2 V A 2 2 j A max j B max 24 t 3 2 + j A max 24 t 3 2 + d
This formula reflects the minimum distance between car A and car B before the two cars brake if the two cars want to avoid a collision. It is called the minimum following distance.
(2)
Basic safe distance D2
In the actual expressway driving process, the driver of the following car (car B) will implement braking only after identifying the front car’s brake lights. Therefore, this reduces the distance that car A moves before the driver reacts. Therefore, this distance requirement is the general distance requirement. Hence, the basic following distance D2 can be obtained.
D 2 = D B + d D A = V B ( t 1 + t 2 + t 3 2 ) + V B 2 V B 2 2 j B max j B max 24 t 3 2 ( V A t 3 2 + V A 2 V A 2 2 j A max j A max 24 t 3 2 ) + d = V B ( t 1 + t 2 ) + ( V B V A ) t 3 2 + V B 2 V B 2 2 j B max V A 2 V A 2 2 j A max j B max 24 t 3 2 + j A max 24 t 3 2 + d
This formula reflects the distance between car A and car B that should be maintained when the following car can see the front car’s brake lights (assuming they function). It is called the basic following distance.
(3)
Sufficient safe distance D3
The front car’s deceleration jAmax can tend toward ∞—that is, jAmax = ∞. (This can happen, for example, if the front car crashes, or if it has very powerful brakes). As a result, the initial velocity VA of car A can be considered to become 0 instantaneously [23]. In this case, DA ≈ 0, so the safe spacing D3 can be obtained.
D 3   = V B ( t 1 + t 2 + t 3 2 ) + V B 2 V B 2 2 j B max j B max 24 t 3 2 + d
This reflects the safe distance requirement when car B must brake in the most unfavorable conditions (such as the front vehicle suddenly happening to crash). This is called the sufficient following distance.
Obviously, from the above analysis, the following formula can be obtained: D1 < D2 < D3.

3.3. Determination of Parameter Values in the Model

Before the simulation of the model, it is necessary to determine the value of each parameter in the model. In the model, the speed of the following car VB, the speed of the front car VA and the relative speed Vr are to be measured. The speed of the following car VB is measured by the vehicle speed sensor in real time. The relative speed Vr is measured by the vehicle-mounted radar in the collision avoidance system, and the speed “VA” of car A can be calculated by the formula Vr = VBVA. The value of each other parameter in the model needs to be set in advance.

3.3.1. Determination of Parameters t1, t2 and t3

According to the analysis of the braking process, the driver’s reaction time t1 is generally 0.3–1.0 s, and the value of the reaction time is related to the driver’s driving experience, proficiency and fatigue. In this paper, t1 = 1.0 s. The braking coordination time t2 is generally 0.2–0.4 s, and the value of the braking coordination time is mainly related to the structural form of the braking system. In this paper, t2 = 0.3 s. According to relevant statistical analysis, in the actual braking process, the braking force growth time t3 is generally considered to be 0.1–0.2 s. In this paper, t3 = 0.2 s [24].

3.3.2. Determination of Parameters jAmax and jBmax

During the braking process, the braking deceleration produced by a car reflects the value of the ground braking force, which is not only related to the brake force when the wheel is rolling, but is also related to the adhesion provided by the road when the anti-lock system drags or slips. Specifically, the braking deceleration is related to the type of brakes, wheel load, road adhesion coefficient and road slope. In general, the road adhesion condition is the key factor affecting the braking deceleration. Due to different road conditions and a different adhesion coefficient, the car’s ground braking force varies, so the time the car takes to achieve the maximum braking deceleration differs.
According to car theory [25], jmax = φg. Obviously, the maximum braking deceleration varies with the road adhesion coefficient. However, if the car is driving on a sloped section of road, the gravity of the car is decomposed along the longitudinal slope, and the direction of the supporting force is perpendicular to the road. In an uphill section, the gravity component along the longitudinal slope and the ground braking force jointly provide braking force for the vehicle. In a downhill section, gravity along the longitudinal slope of the component force will offset part of the vehicle’s braking force, which is not conducive to vehicle braking. According to JTG B01-2014 Highway Engineering and Technical Standards, the maximum longitudinal slope of an expressway shall not exceed 5%. Therefore, the maximum braking acceleration jmax under different slopes can be obtained by the formula jmax = (φ + i)g by simplification. When the slip rate is in the range of 15–30%, the wheel–road adhesion coefficient is at the maximum value, and the braking force can reach the maximum value. Asphalt and cement are widely used as the pavement materials in China [26]. The values of the wheel–road adhesion coefficient φ are shown in Table 1.

3.3.3. Determination of the Value of Parameter “d

There should be distance “d” between the front car and the following car. In order to ensure that the distance after stopping is not severely distressing, the value of “d” can be based on people’s individual responses. Generally, the value of “d” is 2–5 m. The value is affected by the driver’s experience, the conditions on the road and the psychological resilience the driver has that day. In this paper, it is defined that the value of “d” is 2 m for ascending roads, 3 m for flat roads and 5 m for downhill roads.

4. Simulation Analysis and Application

4.1. Calculations of Three Safe Distances for Different Road Conditions and Slopes

Since only the deceleration during braking is involved in the minimum following distance model, and the front car and the following car are driving on the same road (making the road conditions the same), the maximum deceleration during braking is taken as jAmax = jBmax in this paper. We take the braking deceleration of both cars to be the same. On the one hand, this simplifies the calculations. On the other hand, in actual scenarios, the deceleration of the following car is generally greater than that of the front car. If they are equal, greater safety is implied. At the same time, we can assume that V’A = V’B after car A and car B finish braking (including a requirement that both cars have stopped after the end of braking). If V’A is less than V’B, obviously both cars are not in the safe state, and if V’A is greater than V’B, then obviously both cars are in the safe state.
(1)
For i = 0 and d = 3, the simulations of the three safe distances are shown in Figure 3, Figure 4 and Figure 5. The results of Table 2, Table 3 and Table 4 can be obtained from Figure 3, Figure 4 and Figure 5, respectively.
(2)
For i = 0.3 and d = 2 on the uphill road, the simulations of three kinds of safe distance are shown in Figure 6, Figure 7 and Figure 8. The results of Table 5, Table 6 and Table 7 can be obtained from Figure 6, Figure 7 and Figure 8, respectively
(3)
For i = −0.3 and d = 5 on the downhill road, the simulations of three kinds of safe distance are shown in Figure 9, Figure 10 and Figure 11. The results of Table 8, Table 9 and Table 10 can be obtained from Figure 9, Figure 10 and Figure 11, respectively.
According to the implementation of Article 80 of the Road Traffic Safety Law of the People’s Republic of China, when a car is being driven on an expressway at a speed of more than 100 km per hour, the driver shall keep a distance of more than 100 m from any vehicle that is in front and in the same lane. When the vehicle speed is lower than 100 km per hour, the distance between the vehicle and the vehicle in front in the same lane may be shortened appropriately, but the minimum distance shall not be less than 50 m.
As can be seen in Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11 and Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and Table 10, the numerical simulation results meet the highway speed limit requirements stipulated in Article 80 of the Implementation Regulations of the Road Traffic Safety Law of the People’s Republic of China. According to the detailed analysis in Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11 and Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and Table 10, under the same slope section, the minimum safe distance D1 is the minimum, the basic safe distance D2 is the second and the sufficient safe distance D3 is the maximum. Under the same driving conditions, such as the same speed VB of car B, relative speed Vr and the same road adhesion, all kinds of safety distances required by the downhill section are the largest, followed by the corresponding flat section, and the smallest on the uphill section. The results of this study can provide a more specific reference value for the safe following distance at different vehicle speeds and provide a theoretical basis for the traffic management department to formulate relevant policies.

4.2. Application of Three Kinds of Safe Distance on Expressways

In practical cases, it is assumed that a selection switch is set on the dashboard of the cab. The driver can put the button in an appropriate position according to the actual road conditions during the day of driving, and the alarm system can select the road adhesion coefficient for the data processing unit to calculate.
Here, the relative speed was set as Vr = VBVA = 20 km/h, and VB ranged from 80 to 120 km/h. The basic following distance between car A and car B on different roads was simulated, and the results shown in Figure 12 were obtained. The specific parameters are shown in Table 11.
As we considered the safety distance under three different driving conditions, we can consider providing an impact factor according to the needs of practical operation and feasibility. As the minimum following distance expects perfection from the following car’s driver’s reaction time, and the sufficient following distance accounts for an extreme case, the basic following distance reflects most real scenarios. Thus, we set three factors influencing safe following distance (which can be analyzed using probability statistics according to actual driving conditions). The influencing factor of the minimum following distance D1 is ω1, the influencing factor of the basic following distance D2 is ω2, and the influencing factor of the sufficient following distance D3 is ω3; and ω1 + ω2 + ω3 = 1. Therefore, it is finally provided to the warning system as the warning critical safe distance S:
S = ω1D1 + ω2D2 + ω3D3

5. Conclusions

In this study, according to the characteristics of safe driving and rear-end accidents on expressways, the safe following distance on an expressway is studied from the angle of vehicle braking deceleration based on kinematic principles. Based on the characteristics of the vehicle braking process, three kinds of highway safe following distance models based on braking deceleration were constructed. Based on the investigation of the road section and the study of the calculation model and parameters of the safe following distance, the key parameters of the safe following distance model, such as the driving speed and the deceleration speed and different slope values, were discussed in depth, and the recommended values of the safe following distance of the vehicles on plain and longitudinal slopes were put forward. The research results show that the calculation results of the safe following distance conform to the requirements of safe following distance in the Implementation Regulations of the Road Traffic Safety Law of the People’s Republic of China. The calculation of three kinds of safe following distance can provide a theoretical basis and practical examples for the traffic police department to formulate different traffic management policies. The results of this study have practical guiding significance for improving the vehicle density and transportation efficiency of expressways and reducing the incidence of rear-end traffic accidents on expressways. The next step of this study is to carry out the following research contents:
(1)
Construct the calculation model of expressway parking sight distance based on braking deceleration and study the relationship between safe following distance and stopping sight distance.
(2)
Propose the suggested value of expressway stopping sight distance based on braking deceleration under different longitudinal slopes and study different design speeds.
(3)
Carry out the vehicle braking deceleration test, to further improve the parameter value of the model and enhance the application value of the research.

Author Contributions

Conceptualization, X.W. and S.F.; Methodology, X.W.; Software, X.W. and S.F.; Validation, X.W. and S.F.; Formal analysis, S.F.; Resources, X.W.; Data curation, X.W. and S.F.; Writing—original draft preparation, X.W. and S.F.; Writing—review and editing, S.F.; Supervision, X.W.; Project administration, X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (grant Nos. 11772277, 51305372); the Open Fund Project of Transportation Infrastructure Intelligent Management and Maintenance Engineering Technology Center of Xiamen City (grant No. TCIMI201803); and the Project of 2011 Collaborative Innovation Center of Fujian Province (grant No. 2016BJC019).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Qian, Y.; Hu, Y. Modern Automobile Safety Technology; Shanghai Jiao Tong University Press: Shanghai, China, 2006. [Google Scholar]
  2. Peng, Z.; Wang, Y.; Wang, L. A comparative analysis of factors influencing the injury severity of daytime and nighttime crashes on a mountainous expressway in China. Int. J. Inj. Control Saf. Promot. 2021, 28, 503–512. [Google Scholar] [CrossRef] [PubMed]
  3. He, Z.; Zhang, J.; Xu, P. A Sensor Network-Based Intelligent Control System for Traffic Safe Distance. In Proceedings of the 2013 Sixth International Symposium on Computational Intelligence and Design, ISCID 2013, Hangzhou, China, 28–29 October 2013; pp. 326–329. [Google Scholar]
  4. Li, W.; Feng, K.; Zhang, H. Anti-rear-end Collision Warning System Based-on Zigbee for Highway Vehicle. Appl. Mech. Mater. 2012, 130–134, 3511–3514. [Google Scholar] [CrossRef]
  5. Wang, H.; Quan, W.; Wang, Y.; Liu, X. Modeling of Safe Driving Distance on the Basis of Vehicle-to-Vehicle Communication. Transp. Res. Rec. 2013, 2381, 28–35. [Google Scholar] [CrossRef]
  6. Wang, Q.; Xu, S.; Xu, H. A Fuzzy Control Based Self-Optimizing PID Model for Autonomous Car Following on Highway. In Proceedings of the 2014 International Conference on Wireless Communication and Sensor Network, WCSN 2014, Wuhan, China, 13–14 December 2014; pp. 395–399. [Google Scholar]
  7. Cao, W.; Wu, X.; Huang, H. Anti-Rear-End Collision Safe Running and Its Warning of Vehicle on Highway. In Proceedings of the 2016 Eighth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Macau, China, 11–12 March 2016; pp. 97–101. [Google Scholar]
  8. Gargoum Suliman, A.; Karim, E.-B.; Joseph, S. Assessing Stopping and Passing Sight Distance on Highways Using Mobile LiDAR Data. J. Comput. Civ. Eng. 2018, 32, 65–85. [Google Scholar]
  9. Andrzej, S.; Adam, K.; Szymon, Z.; Andrzej, C. Examining Impact of Speed Recommendation Algorithm Operating in Autonomous Road Signs on Minimum Distance between Vehicles. Remote Sens. 2022, 14, 28–38. [Google Scholar] [CrossRef]
  10. Zhang, H.; Huang, Y.; Deng, K. Safety Analysis on Road Sight Distance. In Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA), Changsha, Hunan, 20–22 October 2008; pp. 461–465. [Google Scholar]
  11. Fambro, D.; Fitzpatrick, K.; Koppa, R. New Stopping Sight Distance Model for Use in Highway Geometric Design. Transportation Research Record. J. Transp. Res. Board 2000, 1701, 1–8. [Google Scholar] [CrossRef]
  12. Hassan, Y.; Sayed, T.; Tabernero, V. Establishing Practical Approach for Design Consistency Evaluation. J. Transp. Eng. 2001, 127, 295–302. [Google Scholar] [CrossRef]
  13. Crisman, B.; Marchionna, A.; Perco, P. Photogrammetric Surveys for the Definition of a Model for a Passing Sight Distance Computation. In Proceeding of the 20th International Symposium on Highway Geometric Design. Mainz: Road and Transportation Research Association, Mainz, Germany, 14–17 June 2000; pp. 110–118. [Google Scholar]
  14. Nehate, G.; Rys, M. 3D Calculation of Stopping-sight Distance from GPS Data. J. Transp. Eng. 2006, 132, 691–698. [Google Scholar] [CrossRef]
  15. Hassan, Y.; Sayed, T. Effect of Driver and Road Characteristics on Required Preview Sight Distance. Can. J. Civ. Eng. 2011, 29, 276–288. [Google Scholar] [CrossRef]
  16. Zhang, C.; Yang, S.; Zhao, Y.; Pan, B. Methods of Three-dimensional Sight Distance Inspection for Highway. J. Chang. Univ. Nat. Sci. Ed. 2009, 29, 54–57. [Google Scholar]
  17. Jiang, H.; Li, F. Modeling of Stopping Sight Distance and Analysis of Safe Speed on the Freeway with Different Road Conditions. J. Xi’an Technol. Univ. 2012, 32, 25–30. [Google Scholar]
  18. Yang, Y. Safety of Stop Sight Distance of Freeway Passing Lane for Passenger Car in Mountainous Area. J. Chang. Univ. Nat. Sci. Ed. 2014, 34, 42–48. [Google Scholar]
  19. Xun, S. Research on Sight Distance Inspection Technology of Mountain Highway; Kunming University of Science and Technology: Kunming, China, 2017. [Google Scholar]
  20. Yang, F.; Bai, H.; He, Y.; Wei, H. Study on Method of Evaluating Stopping Sight Distance of Median Strip in Expressway. J. Highw. Transp. Res. Dev. 2018, 35, 45–51. [Google Scholar]
  21. Luo, Y.; Zhang, F.; Zhang, Y. Model of minimum safety vehicle distance for trucks on curved slope combined section of expressway. Highw. Automob. Transp. 2019, 13, 39–43. [Google Scholar]
  22. Zhong, Y.; Yao, J. A formula of the critical safety distance between two moving vehicles. J. Hunan Univ. Nat. Sci. Ed. 2001, 28, 54–58. [Google Scholar]
  23. Zheng, A. The analysis of the motorway vehicle gap and design the device of protectiving vehicle collision. J. Wuhan Univ. Technol. 2002, 24, 62–65. [Google Scholar]
  24. Yang, P.; Tang, Y.; Ge, L.; Zhang, Y. Simulation Study on Minimum Vehicle Safety Distance Model in Following State. Heilongjiang Transp. Sci. Technol. 2013, 36, 166–168. [Google Scholar]
  25. Yu, Z. Automobile Theory; China Machine Press: Beijing, China, 2005. [Google Scholar]
  26. Li, X.; Tang, H. Numerical emulation of car braking dynamics model on superhighway. J. Syst. Simul. 2007, 2, 668–670. [Google Scholar]
Figure 1. Automobiles in a braking scenario.
Figure 1. Automobiles in a braking scenario.
Applsci 13 01110 g001
Figure 2. Change curves of brake-pedal force and braking deceleration with time in the process of automobile braking.
Figure 2. Change curves of brake-pedal force and braking deceleration with time in the process of automobile braking.
Applsci 13 01110 g002
Figure 3. Changes in the minimum safe distance D1 with various VB and Vr when i = 0 and d = 3.
Figure 3. Changes in the minimum safe distance D1 with various VB and Vr when i = 0 and d = 3.
Applsci 13 01110 g003
Figure 4. Changes in the basic safe distance D2 with various VB and Vr when i = 0 and d = 3.
Figure 4. Changes in the basic safe distance D2 with various VB and Vr when i = 0 and d = 3.
Applsci 13 01110 g004
Figure 5. Changes in the sufficient safe distance D3 with various VB and Vr when i = 0 and d = 3.
Figure 5. Changes in the sufficient safe distance D3 with various VB and Vr when i = 0 and d = 3.
Applsci 13 01110 g005
Figure 6. Changes in the minimum safe distance D1 with various VB and Vr when i = 0.3 and d = 2.
Figure 6. Changes in the minimum safe distance D1 with various VB and Vr when i = 0.3 and d = 2.
Applsci 13 01110 g006
Figure 7. Changes in the basic safe distance D2 with various VB and Vr when i = 0.3 and d = 2.
Figure 7. Changes in the basic safe distance D2 with various VB and Vr when i = 0.3 and d = 2.
Applsci 13 01110 g007
Figure 8. Changes in the sufficient safe distance D3 with various VB and Vr when i = 0.3 and d = 2.
Figure 8. Changes in the sufficient safe distance D3 with various VB and Vr when i = 0.3 and d = 2.
Applsci 13 01110 g008
Figure 9. Changes in the minimum safe distance D1 with various VB and Vr when i = −0.3 and d = 5.
Figure 9. Changes in the minimum safe distance D1 with various VB and Vr when i = −0.3 and d = 5.
Applsci 13 01110 g009
Figure 10. Changes in the basic safe distance D2 with various VB and Vr when i = −0.3 and d = 5.
Figure 10. Changes in the basic safe distance D2 with various VB and Vr when i = −0.3 and d = 5.
Applsci 13 01110 g010
Figure 11. Changes in the sufficient safe distance D3 with various VB and Vr when i = −0.3 and d = 5.
Figure 11. Changes in the sufficient safe distance D3 with various VB and Vr when i = −0.3 and d = 5.
Applsci 13 01110 g011
Figure 12. Changes in the basic following distance with VB and φ when i = 0, Vr = 20 km/h.
Figure 12. Changes in the basic following distance with VB and φ when i = 0, Vr = 20 km/h.
Applsci 13 01110 g012
Table 1. Wheel–road adhesion coefficient (φ) values.
Table 1. Wheel–road adhesion coefficient (φ) values.
Pavement Condition Asphalt, Concrete Pavement (Dry)Asphalt, Concrete Pavement (Wet)Dirt Road, Gravel Road (Snow)Ice Road
The adhesion coefficient
φ value
0.80.70.60.15
Table 2. The minimum safe distance D1 based on VB between 60 and 120 km/h when i = 0 and d = 3.
Table 2. The minimum safe distance D1 based on VB between 60 and 120 km/h when i = 0 and d = 3.
i = 0, d = 3Relative Velocity Vr (km/h) (Vr = VBVA)VB
(km/h)
0102030405060
The minimum safe
distance D1 (m)
3.0000 60
3.000014.122724.2809 80
3.000016.051728.138939.261649.4198 100
3.000017.980731.996945.048657.135868.258578.4167120
Table 3. The basic safe distance D2 based on VB between 60 and 120 km/h when i = 0 and d = 3.
Table 3. The basic safe distance D2 based on VB between 60 and 120 km/h when i = 0 and d = 3.
i = 0, d = 3Relative Velocity Vr (km/h) (Vr = VBVA)VB
(km/h)
0102030405060
The basic safe
distance D2 (m)
24.6667 60
31.888939.400545.9475 80
39.111148.551757.027864.539471.0864 100
46.333357.702968.108077.548686.024793.5363100.0833120
Table 4. The sufficient safe distance D3 based on VB between 60 and 120 km/h when i = 0 and d = 3.
Table 4. The sufficient safe distance D3 based on VB between 60 and 120 km/h when i = 0 and d = 3.
i = 0, d = 3Relative Velocity Vr (km/h) (Vr = VBVA)VB
(km/h)
6080100120
The sufficient safe
distance between cars
D3 (m)
43.6844 60
64.9653 80
90.1042 100
119.1011120
Table 5. The minimum safe distance D1 based on VB between 60 and 120 km/h when i = 0.3 and d = 2.
Table 5. The minimum safe distance D1 based on VB between 60 and 120 km/h when i = 0.3 and d = 2.
i = 0.3, d = 2Relative Velocity Vr (km/h) (Vr = VBVA)VB
(km/h)
0102030405060
The minimum safe
distance D1 (m)
2.0000 60
2.000012.861222.7928 80
2.000014.720526.511437.372647.3042 100
2.000016.579830.230042.950554.741365.602675.5341120
Table 6. The basic safe distance D2 based on VB between 60 and 120 km/h when i = 0.3 and d = 2.
Table 6. The basic safe distance D2 based on VB between 60 and 120 km/h when i = 0.3 and d = 2.
i = 0.3, d = 2Relative Velocity Vr (km/h) (Vr = VBVA)VB
(km/h)
0102030405060
The basic safe
distance D2 (m)
23.6667 60
30.888938.139044.4595 80
38.111147.220555.400362.650468.9708 100
45.333356.302066.341175.450583.630290.880397.2008120
Table 7. The sufficient safe distance D3 based on VB between 60 and 120 km/h when i = 0.3 and d = 2.
Table 7. The sufficient safe distance D3 based on VB between 60 and 120 km/h when i = 0.3 and d = 2.
i = 0.3, d = 2Relative Velocity Vr (km/h) (Vr = VBVA)VB
(km/h)
6080100120
The sufficient safe
distance between cars
D3 (m)
42.0569 60
62.8497 80
87.3611 100
115.5911120
Table 8. The minimum safe distance D1 based on VB between 60 and 120 km/h when i = −0.3 and d = 5.
Table 8. The minimum safe distance D1 based on VB between 60 and 120 km/h when i = −0.3 and d = 5.
i = −0.3, d = 5Relative Velocity Vr (km/h) (Vr = VBVA)VB
(km/h)
0102030405060
The minimum safe
distance D1 (m)
5.0000 60
5.000016.404526.8070 80
5.000018.408730.815342.219852.6223 100
5.000020.412934.823648.232360.638972.043582.4459120
Table 9. The basic safe distance D2 based on VB between 60 and 120 km/h when i = −0.3 and d = 5.
Table 9. The basic safe distance D2 based on VB between 60 and 120 km/h when i = −0.3 and d = 5.
i = −0.3, d = 5Relative Velocity Vr (km/h) (Vr = VBVA)VB
(km/h)
0102030405060
The basic safe
distance D2 (m)
26.6667 60
33.888941.682348.4736 80
41.111150.908759.704267.497674.2889 100
48.333360.135170.934780.732389.527897.3212104.1126120
Table 10. The sufficient safe distance D3 based on VB between 60 and 120 km/h when i = −0.3 and d = 5.
Table 10. The sufficient safe distance D3 based on VB between 60 and 120 km/h when i = −0.3 and d = 5.
i = −0.3, d = 5Relative Velocity Vr (km/h) (Vr = VBVA)VB
(km/h)
6080100120
The sufficient safe
distance between cars
D3 (m)
46.3609 60
68.1678 80
93.9831 100
123.8067120
Table 11. The basic following distance based on VB between 60 and 120 km/h when i = 0, Vr = 20 km/h.
Table 11. The basic following distance based on VB between 60 and 120 km/h when i = 0, Vr = 20 km/h.
Vr = VBVA = 20 km/hCoefficient of Road Adhesion φVB
(km/h)
0.50.60.70.8
The basic safety distance between cars
D2 (m)
54.536150.861648.236946.268480
68.057663.333259.958657.4276100
81.579075.804771.680268.5869120
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wu, X.; Fu, S. Research on Safe Following Distance on an Expressway Based on Braking Process Analysis. Appl. Sci. 2023, 13, 1110. https://doi.org/10.3390/app13021110

AMA Style

Wu X, Fu S. Research on Safe Following Distance on an Expressway Based on Braking Process Analysis. Applied Sciences. 2023; 13(2):1110. https://doi.org/10.3390/app13021110

Chicago/Turabian Style

Wu, Xinye, and Shude Fu. 2023. "Research on Safe Following Distance on an Expressway Based on Braking Process Analysis" Applied Sciences 13, no. 2: 1110. https://doi.org/10.3390/app13021110

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop