# Analysis of Normal Stopping Behavior of Drivers at Urban Intersections in China

Authors: Tian Yuan; Rui Liu; Xuan Zhao; Qiang Yu; Xichan Zhu; Shu Wang
Journal: Journal of Advanced Transportation
 (2022)

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## Abstract

Driving behavior analysis plays an important role in improving the human-like driving capability of the Advanced Driver Assistance System (ADAS) and autonomous driving technologies. In this study, 906 data of drivers stopping normally at urban intersections were extracted from natural driving data to study reaction characteristics and braking characteristics of drivers in real road traffic. The effects of traffic state and traffic density on approach speed and reaction distance were studied using one-way ANOVA, and the relationships between maximum deceleration, average deceleration, braking duration, and influencing factors were analyzed using hierarchical multiple regression. The main results indicate the following: (1) compare with free driving, the approach speed in the following state was lower, and following traffic also caused drivers with higher speeds to further increase reaction distance in high-density traffic. (2) Traffic density only affected the approach speed in the free-driving condition. High-density traffic caused drivers to reduce speed. (3) There was a clear correlation between approach speed and reaction distance: the greater the approach speed, the longer the reaction distance. (4) The braking characteristics were mainly affected by the self-vehicle motion state. Drivers braked to a stop with greater average deceleration and maximum deceleration in a shorter time when the approach speed was higher or the reaction distance was shorter. (5) Both traffic state and traffic density had an influence on the braking characteristics. Drivers reduced average deceleration and increased braking duration in the following state and high-density traffic. (6) When following a preceding vehicle to stop, the braking characteristics of drivers were no longer influenced by the traffic density but were related to the relative motion state with the preceding vehicle. In addition to the driving behavior analysis, the identification method of traffic participants and ranging method used in this study will also advance the development of autonomous driving technologies and driver assistance systems.


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## Body

## 1. Introduction

Advanced Driver Assistance System and autonomous driving technologies have been in the process of continuous development and implementation. However, it is still difficult to define the ideal response of intelligent systems in some complex traffic conditions [1]. Monitoring how human drivers perform driving tasks and equipping the systems with the operating characteristics of human drivers, i.e., human-like driving, is an effective solution to this problem [2]. In addition, during the transition from traditional vehicles to autonomous vehicles, the coexistence of the two in the future traffic environment will be inevitable. If there are significant differences in the perception and operation between autonomous vehicles and human drivers, it may cause serious traffic accidents [3, 4]. Besides, it can also cause discomfort to drivers when the systems control the vehicle differently from drivers’ own judgment [5]. Therefore, understanding of driving behavior and recognition of driving characteristics are of great help to improve the safety and acceptance of driver assistance systems and autonomous driving technologies.Intersection is a complex scene where traffic accidents occur frequently [6]. Researchers have conducted numerous studies on driving behavior in the intersection scene, which include drivers’ reaction characteristics to the upcoming intersection under different driving environments and driving conditions. Sato and Akamatsu [7] studied the reaction distance of right-turning drivers when following a vehicle, and the results showed that the relative distance between the two vehicles and speed influenced the position and timing of the drivers operating pedals. Thapa et al. [8] studied the reaction location of turning drivers at stop-controlled intersections, high speed, sidewalk markings, wet roads, and nighttime driving all caused drivers to react further from the intersection. Oneyear et al. [9] established the braking behavior model at rural intersections to determine the factors affecting reaction position. The results showed that the braking distance was significantly different in age, type, and direction of turning movement. Approach speed is another key aspect that reflects the reaction characteristics. Wu and Xu [4] found that approach speed was affected by signal status, vehicle type, and conflicting traffic when they studied the driving behavior of turning drivers. Berndt et al. [10] analyzed the approach speed during red-light phase, and the results showed that the speed was lower when drivers followed the preceding vehicle approaching intersections.The braking behavior of drivers approaching intersections under different driving environments and driving conditions has also been studied. The yellow interval is considered as a dilemma area for drivers [11, 12], where drivers’ wrong decisions due to sudden changes in traffic lights can cause safety problems [13], so driving behavior under this condition has received more attention. Existing studies have shown that when the yellow light was triggered, the average deceleration increased as the distance and the time to the intersection decreased and increased as approach speed increased [14, 15], while the braking response time showed the opposite trend [16]. Other studies focused on the analysis of deceleration behavior at stop-controlled intersections. Bao and Boyle [17] evaluated the maximum deceleration and brake pedal differential time of drivers approaching intersections, and the results showed that the two braking characteristics were influenced by age, gender, and intersection type. Haas et al. [18] further restricted the driving environment to rural intersections. Their research proved the strong correlation between deceleration and initial speed. Wang et al. [19] found that approach speed not only had an impact on deceleration but also had an impact on deceleration time and deceleration distance. Drivers driving at high speeds had higher deceleration, longer deceleration time, and longer deceleration distance.Driving behavior at intersections is influenced by plenty of factors, the above studies are not sufficient to fully clarify the way drivers operate vehicles under various traffic conditions, and few existing studies have focused on the normal stopping behavior of drivers at urban intersections. In addition, due to the limitations of data collection methods, these early researchers were usually unable to accurately determine when and where drivers started decelerating and braking, nor can they accurately obtain the deceleration parameters. Some key parameters were mostly obtained through modeling or indirect calculations. For example, Thapa et al. [8, 20] built segmented linear regression models based on the time-series data of speed and detected reaction position according to the change in slope and the change in the variance of the fitted linear segments. Berndt et al. [10] estimated the vehicle position using a laser scanner and Kalman filter and estimated the speed by smooth spline interpolation. Gates et al. [16] calculated the approach speed and deceleration based on the distance and time information obtained from the videos. These methods inevitably have a negative impact on the study results. Therefore, it is necessary to apply real natural driving data to analyze the normal stopping behavior of drivers at urban intersections.This study was carried out to analyze the influence of traffic state, traffic density, and vehicle motion state on the normal stopping behavior of drivers at urban intersections, including reaction characteristics and braking characteristics. The reaction characteristics were characterized by the approach speed and the reaction distance, and the braking characteristics were characterized by the average deceleration, maximum deceleration, and braking duration. With the development of deep learning and high-performance computing capability, some target detection algorithms based on convolutional neural networks have achieved remarkable results, such as R-CNN series [21–23], SSD [24], and YOLO series [25–27]. Such algorithms have been widely used in the field of intelligent transportation to identify traffic signs [28, 29], detect pedestrians and vehicles [30, 31], etc. Therefore, in order to measure the traffic density and reduce the workload, YOLOv4 was used to identify the vehicles driving in different directions and their numbers, and the relative motion state between the host vehicle and the preceding vehicle was calculated based on the position information of vehicle target and lane line.


## 2. Method

### 2.1. Natural Driving Data

The natural driving data used in this study comes from China-FOT. A total of 32 drivers participated in the project, and each driver drove the test vehicle for about 3 months. The test vehicle was Volvo S60. A data acquisition system (DAS) was installed in the test vehicle to collect vehicle movement information and operation information of pedals and steering wheel. Two front cameras were installed outside the test vehicle to record the driving environment, and two other cameras were installed in the test vehicle to capture the drivers’ facial expressions and foot movements (Figure1). In this study, videos captured by the front cameras were used for vehicle target recognition and ranging. Accelerator pedal position, brake master cylinder oil pressure, longitudinal acceleration, speed, and mileage were extracted from CAN-BUS data as observed variables to study driving behavior.Figure 1
(a) Data acquisition system; (b) camera installation position; (c) test vehicle; (d) videos.Natural driving data on urban roads of six drivers were randomly selected as the final sample for this study, and five of the six drivers were male and one was female (mean = 32.56 years, SD = 3.21 years). Each selected driver had more than three years of driving experience, with annual mileage ranging from 5,000 to 30,000 km.


### 2.2. Vehicle Targets Recognition

In order to measure traffic density, all vehicles in the traffic scene were identified using the target detection algorithm. Firstly, the vehicle targets were classified into three types according to their shapes to distinguish vehicles traveling in different directions: car_front, car_rear, and car_side. Then, target labels were established for these 3 vehicle targets. In order to automatically locate intersection scenes, crosswalks and traffic lights were also marked and recognized. In addition, since pedestrians and cyclists are important participants in traffic, corresponding labels for pedestrians and cyclists were established for further research in the future. The target labels and corresponding descriptions are shown in Table1.Table 1
Target labels and corresponding descriptions.
Target labelsFunctioncar_frontOncoming vehiclescar_rearFront vehicles traveling in the same directioncar_sideVehicles whose trajectories are perpendicular to the host vehicleCyclistBike, electric bicycle, motorbikePedestrian—LightTraffic lightsZebraCrosswalksAfter that, 3300 images were intercepted randomly from the videos collected by the front cameras, and all the targets in these images were manually labeled. Of course, these 3300 images contained the 7 targets mentioned above, and each target had a sufficient number. Then, 3000 images were randomly selected as the training set to train the target detection algorithm, and the remaining 300 images were used as the test set. The platform for training and testing is as follows: i7-10700KF CPU, Nvidia RTX 2070Super GPU, and 32GB memory.Faster-RCNN [23], SSD [24], and YOLOv4 [32] were selected as candidate algorithms for target recognition. These three algorithms are the more excellent algorithms among various target detection algorithms based on the convolutional neural network. Among them, Faster R-CNN is a two-stage algorithm based on region proposal, which needs to generate bounding box through the region proposal network (RPN) first and then classify and regress the bounding box. SSD and YOLOv4 are representative algorithms of one-stage algorithm. They have different network structures, but both perform prediction directly after feature extraction at the convolutional layer.The effectiveness of the 3 algorithms on the China-FOT data set is shown in Table2. Although Faster-RCNN is a two-stage algorithm, its mAP (mean average precision) did not show obvious advantages over YOLOv4 that had undergone several improvements. In addition, the one-stage algorithm was significantly faster in detection speed, so we choose YOLOv4 as the final target recognition algorithm, whose recognition accuracy and detection efficiency were better than SSD and Faster-RCNN. The visualization of target recognition is shown in Figure 2.Table 2
Effectiveness of 3 target detection algorithms.
AlgorithmsmAP (%)Average frames per second (fps)Faster-RCNN [23]78.863.57SSD [24]82.7120.73YOLOv4 [32]84.8823.58Figure 2
Target recognition with YOLOv4.


### 2.3. Ranging

The relative motion state between the host vehicle and the preceding vehicle was calculated based on the position information of the vehicle target and lane line. As shown in Figure3, the standard lane width of 3.5 m becomes gradually shorter on the image as the distance gets farther, so the lane width corresponding to each point on the lane line has a ratio corresponding to the true width. Since the camera was mounted in a fixed position on each test vehicle, the position of the lane line on the image was fixed when the test vehicle was driving along the lane line, so the linear function expressions of the lane lines can be obtained. YOLOv4 outputted the pixel position of the vehicle target, and the horizontal position of the vehicle target on the lane line can be located according to the vertical coordinate of the bottom edge of the bounding box, then the lane width (L) corresponding to this position and the lateral relative distance between the host vehicle and the preceding vehicle (dx), i.e., the length between the center point of the bottom edge of the bounding box and the midpoint of the lane, can be obtained, after which the true lateral relative distance (D1) can be calculated directly using the ratio method. The longitudinal relative distance (D2) was calculated using the similar triangle principle based on the actual lane width, the pixel lane width (L), and the intrinsic matrix of the calibrated camera as shown in the following equations:(1)D1=Ltrue×dxL,(2)D2=fx×LtrueL,where fx is the focal length in the x-axis direction and Ltrue is the true lane width.Figure 3
Ranging algorithm based on vehicle target and lane line information.In the process of target recognition, the position information of bounding box would generate random errors. In order to reduce the influence of target detection errors on ranging, the position information of the bounding box was firstly corrected by the Kalman filter algorithm and then was further corrected according to the identified vehicle center position when the height-width ratio of the bounding box changed.The ranging algorithm was applied to the China-FOT data set, and the relative distances between the host vehicle and each preceding vehicle were calculated through the identified vehicle target car_rear. Then, the preceding vehicle driving in the same lane with the host vehicle was screened out with the condition of the minimum lateral relative distance. The longitudinal relative motion state in the following state was finally obtained, including relative distance and relative speed. Figure4 shows some cases that the relative distances with the preceding vehicle change over time when drivers follow a car to stop at intersections.Figure 4
Ranging effect for drivers following a car to stop at intersections.


### 2.4. Samples Extraction and Features Selection

The stopping behavior in this study began when the drivers released the accelerator pedal and ended when the host vehicle came to a complete stop. In order to extract the sample data of the drivers braking to a stop at intersections, the intersection scenes were initially located according to the identified crosswalks and traffic lights. Then, the samples were further screened according to the driving speed, i.e., the host vehicle’s speed dropped to 0, and the exact time when the speed dropped to 0 was extracted from the CAN-BUS data. In the end, sample data were extracted 18 seconds forward based on the time point when the host vehicle came to a complete stop to capture the drivers’ reaction to the upcoming intersection. The obtained samples were manually checked by viewing the videos. During inspection, the cases that did not meet the requirements and the special cases where the green light suddenly turned to red light were removed. Meanwhile, the traffic state was classified into free driving and following driving according to whether there was a preceding vehicle braking to a stop before the host vehicle. Specifically, if there was a preceding vehicle also decelerating and stopping in the same lane when the driver released the accelerator pedal, and the deceleration motion state of the preceding vehicle continued for more than 3s after the driver of the host vehicle released the accelerator pedal, the host vehicle was considered to be in the following state; otherwise, the host vehicle was considered to be in the free-driving state. The traffic density was classified as low (0∼2) and high (≥3) according to the number of preceding vehicles traveling in the same direction. Finally, 906 valid samples were obtained, of which 415 stopping behaviors occurred in the following state and 491 stopping behaviors occurred in the free-driving state.The reaction characteristics reflect when and where the driver starts to slow down, and the braking characteristics reflect how the driver decelerates after starting to brake. In order to analyze the drivers’ reaction characteristics and braking characteristics, 7 relevant parameters of drivers braking to a stop at intersections were recorded and calculated (Table3):(1)
The first approach speed and first reaction distance: the speed and the distance to stop when the drivers released the accelerator pedal. The time when the accelerator pedal was released was selected as the time point when the drivers finally reacted to the upcoming intersection as shown in Figure5. The reaction distance was calculated as the mileage traveled by drivers from releasing the accelerator pedal to stopping.(2)
The second approach speed and second reaction distance: the speed and the distance to stop when the drivers started braking. The moment to start braking was selected as the time point when the drivers initially depressed the brake pedal in response to the upcoming intersection as shown in Figure5. The reaction distance was calculated as the mileage traveled by drivers from stepping on the brake pedal to stopping.(3)
The maximum deceleration during braking, characterizing the braking strength.(4)
The average deceleration during braking, characterizing the deceleration level.(5)
The braking duration: the time used by drivers from starting to brake to stopping completely.Figure 5
Method of selecting the moment when behavior events occur. (a) Example 1; (b) example 2.
(a)(b)Table 3
Parameters of reaction characteristics and braking characteristics.
Reaction characteristicsBraking characteristicsspeed_1st, distance_1st speed_2nd, distance_2ndMaximum deceleration, average deceleration, and braking durationNote: speed_1st and distance_1st mean the first approach speed and first reaction distance, and speed_2nd and distance_2nd mean the second approach speed and second reaction distance.


## 2.1. Natural Driving Data

The natural driving data used in this study comes from China-FOT. A total of 32 drivers participated in the project, and each driver drove the test vehicle for about 3 months. The test vehicle was Volvo S60. A data acquisition system (DAS) was installed in the test vehicle to collect vehicle movement information and operation information of pedals and steering wheel. Two front cameras were installed outside the test vehicle to record the driving environment, and two other cameras were installed in the test vehicle to capture the drivers’ facial expressions and foot movements (Figure1). In this study, videos captured by the front cameras were used for vehicle target recognition and ranging. Accelerator pedal position, brake master cylinder oil pressure, longitudinal acceleration, speed, and mileage were extracted from CAN-BUS data as observed variables to study driving behavior.Figure 1
(a) Data acquisition system; (b) camera installation position; (c) test vehicle; (d) videos.Natural driving data on urban roads of six drivers were randomly selected as the final sample for this study, and five of the six drivers were male and one was female (mean = 32.56 years, SD = 3.21 years). Each selected driver had more than three years of driving experience, with annual mileage ranging from 5,000 to 30,000 km.


## 2.2. Vehicle Targets Recognition

In order to measure traffic density, all vehicles in the traffic scene were identified using the target detection algorithm. Firstly, the vehicle targets were classified into three types according to their shapes to distinguish vehicles traveling in different directions: car_front, car_rear, and car_side. Then, target labels were established for these 3 vehicle targets. In order to automatically locate intersection scenes, crosswalks and traffic lights were also marked and recognized. In addition, since pedestrians and cyclists are important participants in traffic, corresponding labels for pedestrians and cyclists were established for further research in the future. The target labels and corresponding descriptions are shown in Table1.Table 1
Target labels and corresponding descriptions.
Target labelsFunctioncar_frontOncoming vehiclescar_rearFront vehicles traveling in the same directioncar_sideVehicles whose trajectories are perpendicular to the host vehicleCyclistBike, electric bicycle, motorbikePedestrian—LightTraffic lightsZebraCrosswalksAfter that, 3300 images were intercepted randomly from the videos collected by the front cameras, and all the targets in these images were manually labeled. Of course, these 3300 images contained the 7 targets mentioned above, and each target had a sufficient number. Then, 3000 images were randomly selected as the training set to train the target detection algorithm, and the remaining 300 images were used as the test set. The platform for training and testing is as follows: i7-10700KF CPU, Nvidia RTX 2070Super GPU, and 32GB memory.Faster-RCNN [23], SSD [24], and YOLOv4 [32] were selected as candidate algorithms for target recognition. These three algorithms are the more excellent algorithms among various target detection algorithms based on the convolutional neural network. Among them, Faster R-CNN is a two-stage algorithm based on region proposal, which needs to generate bounding box through the region proposal network (RPN) first and then classify and regress the bounding box. SSD and YOLOv4 are representative algorithms of one-stage algorithm. They have different network structures, but both perform prediction directly after feature extraction at the convolutional layer.The effectiveness of the 3 algorithms on the China-FOT data set is shown in Table2. Although Faster-RCNN is a two-stage algorithm, its mAP (mean average precision) did not show obvious advantages over YOLOv4 that had undergone several improvements. In addition, the one-stage algorithm was significantly faster in detection speed, so we choose YOLOv4 as the final target recognition algorithm, whose recognition accuracy and detection efficiency were better than SSD and Faster-RCNN. The visualization of target recognition is shown in Figure 2.Table 2
Effectiveness of 3 target detection algorithms.
AlgorithmsmAP (%)Average frames per second (fps)Faster-RCNN [23]78.863.57SSD [24]82.7120.73YOLOv4 [32]84.8823.58Figure 2
Target recognition with YOLOv4.


## 2.3. Ranging

The relative motion state between the host vehicle and the preceding vehicle was calculated based on the position information of the vehicle target and lane line. As shown in Figure3, the standard lane width of 3.5 m becomes gradually shorter on the image as the distance gets farther, so the lane width corresponding to each point on the lane line has a ratio corresponding to the true width. Since the camera was mounted in a fixed position on each test vehicle, the position of the lane line on the image was fixed when the test vehicle was driving along the lane line, so the linear function expressions of the lane lines can be obtained. YOLOv4 outputted the pixel position of the vehicle target, and the horizontal position of the vehicle target on the lane line can be located according to the vertical coordinate of the bottom edge of the bounding box, then the lane width (L) corresponding to this position and the lateral relative distance between the host vehicle and the preceding vehicle (dx), i.e., the length between the center point of the bottom edge of the bounding box and the midpoint of the lane, can be obtained, after which the true lateral relative distance (D1) can be calculated directly using the ratio method. The longitudinal relative distance (D2) was calculated using the similar triangle principle based on the actual lane width, the pixel lane width (L), and the intrinsic matrix of the calibrated camera as shown in the following equations:(1)D1=Ltrue×dxL,(2)D2=fx×LtrueL,where fx is the focal length in the x-axis direction and Ltrue is the true lane width.Figure 3
Ranging algorithm based on vehicle target and lane line information.In the process of target recognition, the position information of bounding box would generate random errors. In order to reduce the influence of target detection errors on ranging, the position information of the bounding box was firstly corrected by the Kalman filter algorithm and then was further corrected according to the identified vehicle center position when the height-width ratio of the bounding box changed.The ranging algorithm was applied to the China-FOT data set, and the relative distances between the host vehicle and each preceding vehicle were calculated through the identified vehicle target car_rear. Then, the preceding vehicle driving in the same lane with the host vehicle was screened out with the condition of the minimum lateral relative distance. The longitudinal relative motion state in the following state was finally obtained, including relative distance and relative speed. Figure4 shows some cases that the relative distances with the preceding vehicle change over time when drivers follow a car to stop at intersections.Figure 4
Ranging effect for drivers following a car to stop at intersections.


## 2.4. Samples Extraction and Features Selection

The stopping behavior in this study began when the drivers released the accelerator pedal and ended when the host vehicle came to a complete stop. In order to extract the sample data of the drivers braking to a stop at intersections, the intersection scenes were initially located according to the identified crosswalks and traffic lights. Then, the samples were further screened according to the driving speed, i.e., the host vehicle’s speed dropped to 0, and the exact time when the speed dropped to 0 was extracted from the CAN-BUS data. In the end, sample data were extracted 18 seconds forward based on the time point when the host vehicle came to a complete stop to capture the drivers’ reaction to the upcoming intersection. The obtained samples were manually checked by viewing the videos. During inspection, the cases that did not meet the requirements and the special cases where the green light suddenly turned to red light were removed. Meanwhile, the traffic state was classified into free driving and following driving according to whether there was a preceding vehicle braking to a stop before the host vehicle. Specifically, if there was a preceding vehicle also decelerating and stopping in the same lane when the driver released the accelerator pedal, and the deceleration motion state of the preceding vehicle continued for more than 3s after the driver of the host vehicle released the accelerator pedal, the host vehicle was considered to be in the following state; otherwise, the host vehicle was considered to be in the free-driving state. The traffic density was classified as low (0∼2) and high (≥3) according to the number of preceding vehicles traveling in the same direction. Finally, 906 valid samples were obtained, of which 415 stopping behaviors occurred in the following state and 491 stopping behaviors occurred in the free-driving state.The reaction characteristics reflect when and where the driver starts to slow down, and the braking characteristics reflect how the driver decelerates after starting to brake. In order to analyze the drivers’ reaction characteristics and braking characteristics, 7 relevant parameters of drivers braking to a stop at intersections were recorded and calculated (Table3):(1)
The first approach speed and first reaction distance: the speed and the distance to stop when the drivers released the accelerator pedal. The time when the accelerator pedal was released was selected as the time point when the drivers finally reacted to the upcoming intersection as shown in Figure5. The reaction distance was calculated as the mileage traveled by drivers from releasing the accelerator pedal to stopping.(2)
The second approach speed and second reaction distance: the speed and the distance to stop when the drivers started braking. The moment to start braking was selected as the time point when the drivers initially depressed the brake pedal in response to the upcoming intersection as shown in Figure5. The reaction distance was calculated as the mileage traveled by drivers from stepping on the brake pedal to stopping.(3)
The maximum deceleration during braking, characterizing the braking strength.(4)
The average deceleration during braking, characterizing the deceleration level.(5)
The braking duration: the time used by drivers from starting to brake to stopping completely.Figure 5
Method of selecting the moment when behavior events occur. (a) Example 1; (b) example 2.
(a)(b)Table 3
Parameters of reaction characteristics and braking characteristics.
Reaction characteristicsBraking characteristicsspeed_1st, distance_1st speed_2nd, distance_2ndMaximum deceleration, average deceleration, and braking durationNote: speed_1st and distance_1st mean the first approach speed and first reaction distance, and speed_2nd and distance_2nd mean the second approach speed and second reaction distance.


## 3. Analysis of Reaction Characteristics

The distribution characteristics of reaction characteristics in different traffic states and traffic densities were analyzed by plotting cumulative percentage curves. One-way ANOVA was used to analyze the influence of traffic state and traffic density on reaction characteristics. In each one-way ANOVA, all other unrelated variables were controlled at the same level to ensure accurate and reliable results. Before performing one-way ANOVA, boxplot was drawn to test outlier, Kolmogorov–Smirnov test was used to test the normality, and Levene’s test was used to test the homogeneity of variance. Welch’s test was used to approximate the result of one-way ANOVA when the sample did not meet equal variance.


### 3.1. Descriptive Statistical Analysis

The descriptive statistics of the reaction characteristics are shown in Table4, and the cumulative percentage curves are shown in Figures 6 and 7. The cumulative percentages of approach speed and reaction distance in the following state were higher than those in the free-driving state, and the cumulative percentages in the high-density traffic were also higher than those in the low-density traffic, which indicated that drivers had lower approach speeds and shorter reaction distances under the traffic conditions of following and high density (Table 4). In addition, it can be seen from the gaps between the curves that traffic state had a more significant impact on reaction characteristics than traffic density.Table 4
Descriptive statistics of reaction characteristics.
StatisticsCharacteristicsTraffic stateTraffic densityFollowingFreedomHighLowSample size—415491466440Minspeed_1st17.1928.4917.1925.36speed_2nd11.1725.8711.1722.13distance_1st18.3234.9418.3231.65distance_2nd5.9418.595.949.73Meanspeed_1st47.1165.7951.7363.05speed_2nd43.2760.8347.4158.48distance_1st108.62158.41122.12149.89distance_2nd67.4596.8174.9492.29Maxspeed_1st95.62106.56102.63106.56speed_2nd87.88104.4895.60104.48distance_1st323.26344.44323.26344.44distance_2nd232.01310.15266.22310.15Std. Dev.speed_1st12.6214.2514.5316.30speed_2nd12.0114.6714.1716.07distance_1st53.7259.3957.5163.47distance_2nd37.0844.7640.2545.86Figure 6
Approach speed and reaction distance by traffic state.
(a)(b)Figure 7
Approach speed and reaction distance by traffic density.
(a)(b)


### 3.2. Analysis of the Effect of Traffic State on Reaction Characteristics

The effect of traffic state on the reaction characteristics in different traffic densities and driving states was analyzed in this section. The specific results of the one-way ANOVA are shown in Supplementary materials (AppendixA), including sample size, mean, and the values of P and F. The significance test results of reaction characteristics when the drivers release the accelerator pedal are shown in Figures 8 and 9. In the low-density traffic, the traffic state had no effect on the reaction distance but had a significant effect on the approach speed on all distance intervals, with the approach speed in the free-driving state being higher than that in the following state. In high-density traffic, the traffic state influenced the approach speed over longer reaction distance. When the reaction distance was very short, there was no difference in the approach speed between following and free-driving states. In addition, the traffic state also affected the reaction distance over higher speed, and the reaction distance was longer when drivers followed a car to stop.Figure 8
ANOVA results for traffic state on the first approach speed.Note: (1) error bar is 95% C.I.; (2) ∗means significant, ×means insignificant. (a) Low density; (b) high density.
(a)(b)Figure 9
ANOVA results for traffic state on the first reaction distance. (a) Low density; (b) high density.
(a)(b)The significance test results of reaction characteristics when drivers start braking are shown in Figures10 and 11. The effect of traffic state on the second approach speed was the same as the effect on the first approach speed, i.e., drivers usually had higher approach speeds in the free-driving state. Compared with the first reaction distance, the second reaction distance when the drivers started braking was influenced to a greater extent by the traffic state. Specifically, in high-density traffic, the second reaction distance began to show a significant difference at a lower speed interval compared to the first reaction distance, and in low-density traffic, the second reaction distance of drivers traveling at high speed was also influenced by the traffic state. In addition, the effect of traffic state on reaction distance became stronger as traffic density became high. The reaction distance began to show difference at lower speed interval when traffic density was high.Figure 10
ANOVA results for traffic state on the second approach speed. (a) Low density; (b) high density.
(a)(b)Figure 11
ANOVA results for traffic state on the second reaction distance. (a) Low density; (b) high density.
(a)(b)


### 3.3. Analysis of the Effect of Traffic Density on Reaction Characteristics

The effect of traffic density on the reaction characteristics in different traffic states and driving states was analyzed in this section. The specific results of the one-way ANOVA are shown in Supplementary materials (AppendixB), including sample size, mean, and the values of P and F. The significance test results of reaction characteristics when the drivers release the accelerator pedal are shown in Figures 12 and 13. The traffic density significantly influenced the approach speed on all distance intervals in the free-driving state; however, in the following state, the traffic density only had an impact on the approach speed over the extreme long distance, and high-density traffic caused drivers to reduce approach speed. Traffic density did not have any effect on the reaction distance in both following and free-driving states. The significance test results of reaction characteristics when the drivers start braking are shown in Figures 14 and 15. Traffic density significantly affected the approach speed in free-driving state without affecting the approach speed in the following state; furthermore, traffic density still did not have an effect on the reaction distance.Figure 12
ANOVA results for traffic density on the first approach speed. (a) Freedom; (b) follow.
(a)(b)Figure 13
AVOVA results for traffic density on the first reaction distance. (a) Freedom; (b) follow.
(a)(b)Figure 14
AVOVA results for traffic density on the second approach speed. (a) Freedom. (b) follow.
(a)(b)Figure 15
AVOVA results for traffic density on the second reaction distance. (a) Freedom; (b) follow.
(a)(b)


### 3.4. Discussion on Reaction Characteristics

As can be seen from the above figures that there is an obvious correlation between approach speed and reaction distance, with the greater the approach speed, the longer the reaction distance, which is consistent with the results of previous studies [8, 19].Traffic state had an impact on reaction characteristics. Firstly, the approach speed showed significant differences across traffic state. Both in low-density traffic and high-density traffic, drivers approached the intersection at a lower speed when they followed the preceding vehicle to stop, which is consistent with the result of Sato and Akamatsu [33]. They investigated drivers’ preparatory behavior before making a right turn, and their results showed that the approach speed with a lead vehicle was lower than that without a lead vehicle. The impact of traffic state on the reaction distance was mainly manifested in high-density traffic, and drivers with high speed had a longer reaction distance when following a vehicle to stop. This result suggests that the combined effect of high speed, high density, and following driving further increases the perceived risk of drivers, which causes drivers to start decelerating and braking further away from the intersection after adjusting the approach speed. However, the descriptive statistics in 3.1 showed that drivers had shorter reaction distances in the following state without considering driving speed and traffic density. The reason for this result is that the approach speed in the following state is lower, thus resulting in a shorter reaction distance, which also suggests that the impact of driving speed on the reaction distance is more significant than the traffic state. In addition, the effect of traffic state on the reaction distance is stronger when drivers start to brake, with the second reaction distance starting to show significant differences at lower speed interval compared to the first reaction distance, which indicates that drivers become more cautious as the distance to the intersection gets shorter.Traffic density is an important factor influencing driving behavior, and the increase in the number of vehicles in the traffic scene has a negative impact on situation awareness and hazard avoidance [34]. Most studies have shown that high-density traffic such as more vehicles parked on the side of the road and denser oncoming traffic caused drivers to reduce driving speed [34, 35]. In this study, considering the traffic state, it was found that the traffic density mainly affected the approach speed in the free-driving state but did not have a large effect on the approach speed in the following state, which may be attributed to the fact that the approach speed in the following state is already relatively low. The high-density traffic made drivers approach intersections at lower speeds, which is consistent with the conclusions of Edquist et al. [34] and Trick et al. [35]. This result also indicates that the approach speed is significantly higher when there is no influence from the preceding vehicle and surrounding vehicles. In addition, the descriptive statistics in 3.1 showed that the reaction distance was shorter in high-density traffic, which is mainly attributed to the low approach speed in high-density traffic, because the traffic density had no significant effect on the reaction distance when the approach speed controlled at the same level. Combining the effect of traffic state on reaction distance, it can be concluded that driving speed is the primary factor affecting reaction distance. Furthermore, it can be also concluded that when drivers follow the preceding vehicle to stop at intersections, their reaction characteristics are not affected by traffic density.


## 3.1. Descriptive Statistical Analysis

The descriptive statistics of the reaction characteristics are shown in Table4, and the cumulative percentage curves are shown in Figures 6 and 7. The cumulative percentages of approach speed and reaction distance in the following state were higher than those in the free-driving state, and the cumulative percentages in the high-density traffic were also higher than those in the low-density traffic, which indicated that drivers had lower approach speeds and shorter reaction distances under the traffic conditions of following and high density (Table 4). In addition, it can be seen from the gaps between the curves that traffic state had a more significant impact on reaction characteristics than traffic density.Table 4
Descriptive statistics of reaction characteristics.
StatisticsCharacteristicsTraffic stateTraffic densityFollowingFreedomHighLowSample size—415491466440Minspeed_1st17.1928.4917.1925.36speed_2nd11.1725.8711.1722.13distance_1st18.3234.9418.3231.65distance_2nd5.9418.595.949.73Meanspeed_1st47.1165.7951.7363.05speed_2nd43.2760.8347.4158.48distance_1st108.62158.41122.12149.89distance_2nd67.4596.8174.9492.29Maxspeed_1st95.62106.56102.63106.56speed_2nd87.88104.4895.60104.48distance_1st323.26344.44323.26344.44distance_2nd232.01310.15266.22310.15Std. Dev.speed_1st12.6214.2514.5316.30speed_2nd12.0114.6714.1716.07distance_1st53.7259.3957.5163.47distance_2nd37.0844.7640.2545.86Figure 6
Approach speed and reaction distance by traffic state.
(a)(b)Figure 7
Approach speed and reaction distance by traffic density.
(a)(b)


## 3.2. Analysis of the Effect of Traffic State on Reaction Characteristics

The effect of traffic state on the reaction characteristics in different traffic densities and driving states was analyzed in this section. The specific results of the one-way ANOVA are shown in Supplementary materials (AppendixA), including sample size, mean, and the values of P and F. The significance test results of reaction characteristics when the drivers release the accelerator pedal are shown in Figures 8 and 9. In the low-density traffic, the traffic state had no effect on the reaction distance but had a significant effect on the approach speed on all distance intervals, with the approach speed in the free-driving state being higher than that in the following state. In high-density traffic, the traffic state influenced the approach speed over longer reaction distance. When the reaction distance was very short, there was no difference in the approach speed between following and free-driving states. In addition, the traffic state also affected the reaction distance over higher speed, and the reaction distance was longer when drivers followed a car to stop.Figure 8
ANOVA results for traffic state on the first approach speed.Note: (1) error bar is 95% C.I.; (2) ∗means significant, ×means insignificant. (a) Low density; (b) high density.
(a)(b)Figure 9
ANOVA results for traffic state on the first reaction distance. (a) Low density; (b) high density.
(a)(b)The significance test results of reaction characteristics when drivers start braking are shown in Figures10 and 11. The effect of traffic state on the second approach speed was the same as the effect on the first approach speed, i.e., drivers usually had higher approach speeds in the free-driving state. Compared with the first reaction distance, the second reaction distance when the drivers started braking was influenced to a greater extent by the traffic state. Specifically, in high-density traffic, the second reaction distance began to show a significant difference at a lower speed interval compared to the first reaction distance, and in low-density traffic, the second reaction distance of drivers traveling at high speed was also influenced by the traffic state. In addition, the effect of traffic state on reaction distance became stronger as traffic density became high. The reaction distance began to show difference at lower speed interval when traffic density was high.Figure 10
ANOVA results for traffic state on the second approach speed. (a) Low density; (b) high density.
(a)(b)Figure 11
ANOVA results for traffic state on the second reaction distance. (a) Low density; (b) high density.
(a)(b)


## 3.3. Analysis of the Effect of Traffic Density on Reaction Characteristics

The effect of traffic density on the reaction characteristics in different traffic states and driving states was analyzed in this section. The specific results of the one-way ANOVA are shown in Supplementary materials (AppendixB), including sample size, mean, and the values of P and F. The significance test results of reaction characteristics when the drivers release the accelerator pedal are shown in Figures 12 and 13. The traffic density significantly influenced the approach speed on all distance intervals in the free-driving state; however, in the following state, the traffic density only had an impact on the approach speed over the extreme long distance, and high-density traffic caused drivers to reduce approach speed. Traffic density did not have any effect on the reaction distance in both following and free-driving states. The significance test results of reaction characteristics when the drivers start braking are shown in Figures 14 and 15. Traffic density significantly affected the approach speed in free-driving state without affecting the approach speed in the following state; furthermore, traffic density still did not have an effect on the reaction distance.Figure 12
ANOVA results for traffic density on the first approach speed. (a) Freedom; (b) follow.
(a)(b)Figure 13
AVOVA results for traffic density on the first reaction distance. (a) Freedom; (b) follow.
(a)(b)Figure 14
AVOVA results for traffic density on the second approach speed. (a) Freedom. (b) follow.
(a)(b)Figure 15
AVOVA results for traffic density on the second reaction distance. (a) Freedom; (b) follow.
(a)(b)


## 3.4. Discussion on Reaction Characteristics

As can be seen from the above figures that there is an obvious correlation between approach speed and reaction distance, with the greater the approach speed, the longer the reaction distance, which is consistent with the results of previous studies [8, 19].Traffic state had an impact on reaction characteristics. Firstly, the approach speed showed significant differences across traffic state. Both in low-density traffic and high-density traffic, drivers approached the intersection at a lower speed when they followed the preceding vehicle to stop, which is consistent with the result of Sato and Akamatsu [33]. They investigated drivers’ preparatory behavior before making a right turn, and their results showed that the approach speed with a lead vehicle was lower than that without a lead vehicle. The impact of traffic state on the reaction distance was mainly manifested in high-density traffic, and drivers with high speed had a longer reaction distance when following a vehicle to stop. This result suggests that the combined effect of high speed, high density, and following driving further increases the perceived risk of drivers, which causes drivers to start decelerating and braking further away from the intersection after adjusting the approach speed. However, the descriptive statistics in 3.1 showed that drivers had shorter reaction distances in the following state without considering driving speed and traffic density. The reason for this result is that the approach speed in the following state is lower, thus resulting in a shorter reaction distance, which also suggests that the impact of driving speed on the reaction distance is more significant than the traffic state. In addition, the effect of traffic state on the reaction distance is stronger when drivers start to brake, with the second reaction distance starting to show significant differences at lower speed interval compared to the first reaction distance, which indicates that drivers become more cautious as the distance to the intersection gets shorter.Traffic density is an important factor influencing driving behavior, and the increase in the number of vehicles in the traffic scene has a negative impact on situation awareness and hazard avoidance [34]. Most studies have shown that high-density traffic such as more vehicles parked on the side of the road and denser oncoming traffic caused drivers to reduce driving speed [34, 35]. In this study, considering the traffic state, it was found that the traffic density mainly affected the approach speed in the free-driving state but did not have a large effect on the approach speed in the following state, which may be attributed to the fact that the approach speed in the following state is already relatively low. The high-density traffic made drivers approach intersections at lower speeds, which is consistent with the conclusions of Edquist et al. [34] and Trick et al. [35]. This result also indicates that the approach speed is significantly higher when there is no influence from the preceding vehicle and surrounding vehicles. In addition, the descriptive statistics in 3.1 showed that the reaction distance was shorter in high-density traffic, which is mainly attributed to the low approach speed in high-density traffic, because the traffic density had no significant effect on the reaction distance when the approach speed controlled at the same level. Combining the effect of traffic state on reaction distance, it can be concluded that driving speed is the primary factor affecting reaction distance. Furthermore, it can be also concluded that when drivers follow the preceding vehicle to stop at intersections, their reaction characteristics are not affected by traffic density.


## 4. Analysis of Braking Characteristics

Hierarchical linear regression was used to analyze the effects of traffic state, traffic density, and vehicle motion state on braking characteristics. Durbin–Watson test was used to verify the independence between observed variables. Regression tolerance (Tolerance) and variance inflation factor (VIF) were used to test whether there was multicollinearity among the independent variables. Casewise diagnostics test was used to detect outliers. Scatter plots of unstandardized predicted values and student residuals were plotted to verify equal variances. Normal Q-Q plots were plotted to verify whether the residuals met the normal distribution.


### 4.1. Analysis of Braking Characteristics considering Self-Vehicle Motion State and Traffic Conditions

Firstly, the hierarchical linear regression models of average deceleration, maximum deceleration, and braking duration were established using the self-vehicle motion state, traffic state, and traffic density as the first-, second-, and third-level independent variables, respectively.The regression analysis results of the average deceleration are shown in Table5. Model 1 with the self-vehicle motion state as the independent variable was significant (P<0.001), and the goodness of fit R2 = 0.807. After adding the traffic state variable (Model 2), R2 increased by 0.005 and F increased by 21.583, which was statistically significant (P<0.001). After adding the traffic density variable (Model 3), R2 increased by 0.004 and F increased by 19.214, which was also statistically significant (P<0.001), indicating that the increase of environmental factors, namely, traffic state and traffic density, significantly improved the prediction level of average deceleration. The variables included in the final model were all significant at the 95% confidence level. The higher the speed when the drivers started braking, the greater the average deceleration; the longer the reaction distance, the smaller the average deceleration. Compared with free driving, the average deceleration in the following state was smaller. In addition, the average deceleration was negatively correlated with traffic density, and the average deceleration was smaller in high-density traffic.Table 5
Regression analysis results of the average deceleration.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.038∗∗∗−1.593−0.036∗∗∗−1.523−0.035∗∗∗−1.500distance_2nd0.010∗∗∗1.1910.010∗∗∗1.1610.010∗∗∗1.150traffic state0.063∗∗∗0.0830.049∗∗∗0.065traffic density0.053∗∗∗0.070R20.8070.8120.816ΔR20.807∗∗∗0.005∗∗∗0.004∗∗∗ΔF1871.705∗∗∗21.583∗∗∗19.214∗∗∗Note. ∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001. The bold values mean the factor is statistically significant.The regression analysis results of maximum deceleration are shown in Table6. Model 1 with the self-vehicle motion state as the independent variable was significant (P<0.001), R2 = 0.594. The goodness of fit and F value of the model were not significantly improved by increasing the traffic state variable and the traffic density variable (P>0.05), indicating that the environmental factors had no effect on the maximum deceleration, and the only variable related to the maximum deceleration was the self-vehicle motion state. Higher approach speed and shorter reaction distance led to higher maximum deceleration.Table 6
Regression analysis results of the maximum deceleration.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.057∗∗∗−1.343−0.059∗∗∗−1.384−0.059∗∗∗−1.383distance_2nd0.015∗∗∗0.9370.015∗∗∗0.9550.015∗∗∗0.954traffic state−0.066−0.048−0.067−0.048traffic density0.0040.003R20.5940.5950.595ΔR20.594∗∗∗0.0020.000ΔF653.688∗∗∗3.3290.015The bold values mean the factor is statistically significant.The regression analysis results of braking duration are shown in Table7. Model 1 was significant (P<0.001), R2 = 0.777. The goodness of fit and F value of the model were significantly improved after adding the traffic state variable and traffic density variable (P<0.001), indicating that both self-vehicle motion state and environmental factors had an effect on the braking duration. The greater the approach speed, the shorter the braking duration; the longer the reaction distance, the longer the braking duration. Furthermore, the braking duration was longer in the following state and in the high-density traffic.Table 7
Regression analysis results of the braking duration.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.116∗∗∗−0.578−0.098∗∗∗−0.493−0.095∗∗∗−0.473distance_2nd0.095∗∗∗1.3000.092∗∗∗1.2640.092∗∗∗1.254traffic state0.650∗∗∗0.1010.549∗∗∗0.085traffic density0.385∗∗∗0.060R20.7770.7840.787ΔR20.777∗∗∗0.007∗∗∗0.003∗∗∗ΔF1563.248∗∗∗28.048∗∗∗12.376∗∗∗The bold values mean the factor is statistically significant.


### 4.2. Analysis of Braking Characteristics Based on Following State

In order to study the influence of the relative motion state with the preceding vehicle on the braking characteristics, hierarchical linear regression models based on the following state were established with the self-vehicle motion state, relative motion state, and traffic density as the first-, second-, and third-level independent variables, respectively.The regression analysis results of the average deceleration are shown in Table8. Model 1 was significant (P<0.001), and R2 = 0.720. The changes in F and R2 of model 2 were significant, while the changes in F and R2 of model 3 were not significant, indicating that the average deceleration in the following state was not related to the traffic density, but was related to the relative motion state and the self-vehicle motion state. In addition to the high approach speed and short reaction distance, the short relative distance between the two vehicles also increased the average deceleration of drivers. The regression analysis results of the maximum deceleration are shown in Table 9. Only the independent variables added in model 2 significantly increased the F value and R2, which indicated that the relative motion state affected the maximum deceleration, and the larger the relative speed or the shorter the relative distance, the larger the maximum deceleration. The regression analysis results of braking duration are shown in Table 10. The changes of F and R2 both in model 2 and model 3 were not significant, which indicated that the braking duration in the following state was only influenced by the self-vehicle motion state.Table 8
Results of the average deceleration based on the following state.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.037∗∗∗−1.552−0.039∗∗∗−1.612−0.038∗∗∗−1.601distance_2nd0.012∗∗∗1.4270.012∗∗∗1.4400.012∗∗∗1.432relative speed_2nd0.0100.0500.0100.050relative distance_2nd0.005∗∗0.0920.005∗∗0.090traffic density0.0260.041R20.7200.7270.728ΔR20.720∗∗∗0.007∗0.002ΔF452.027∗∗∗4.377∗2.165Note. (1) ∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001. (2) relative speed_2nd and relative distance_2nd mean the longitudinal relative speed and relative distance when the driver starts braking. The bold values mean the factor is statistically significant.Table 9
Results of the maximum deceleration based on the following state.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.063∗∗∗−1.239−0.068∗∗∗−1.333−0.069∗∗∗−1.345distance_2nd0.019∗∗∗1.0360.019∗∗∗1.0530.019∗∗∗1.062relative speed_2nd0.042∗0.0950.042∗0.095relative distance_2nd0.019∗∗0.1500.019∗∗0.151traffic density−0.058−0.044R20.4490.4690.471ΔR20.449∗∗∗0.020∗∗0.002ΔF143.359∗∗∗6.554∗∗1.246The bold values mean the factor is statistically significant.Table 10
Results of the braking duration based on the following state.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.126∗∗∗−0.443−0.131∗∗∗−0.459−0.129∗∗∗−0.453distance_2nd0.127∗∗∗1.2470.127∗∗∗1.2430.126∗∗∗1.238relative speed_2nd0.116∗0.0480.116∗0.048relative distance_2nd0.0240.0340.0230.034traffic density0.1790.024R20.8220.8250.825ΔR20.822∗∗∗0.0020.001ΔF813.612∗∗∗2.4891.171The bold values mean the factor is statistically significant.


### 4.3. Analysis of Braking Characteristics Based on Free-Driving State

Hierarchical linear regression models based on the free-driving state were established with the self-vehicle motion state and traffic density as the first and second independent variables. The regression analysis results of the average deceleration, maximum deceleration, and braking duration are shown in Tables11–13. The average deceleration and braking duration were affected by both the self-vehicle motion state and traffic density, while the maximum deceleration only depended on the self-vehicle motion state.Table 11
Results of the average deceleration based on free-driving state.
ParametersModel 1Model 2BBetaBBetaspeed_2nd−0.037∗∗∗−1.480−0.036∗∗∗−1.447distance_2nd0.010∗∗∗1.2050.10∗∗∗1.187traffic density0.066∗∗∗0.086R20.7710.778ΔR20.771∗∗∗0.007∗∗∗ΔF809.823∗∗∗14.877∗∗∗Note.∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001. The bold values mean the factor is statistically significant.Table 12
Results of the maximum deceleration based on free-driving state.
ParametersModel 1Model 2BBetaBBetaspeed_2nd−0.060∗∗∗−1.275−0.059∗∗∗−1.271distance_2nd0.014∗∗∗0.9340.014∗∗∗0.932traffic density0.0130.009R20.5800.580ΔR20.580∗∗∗0.000ΔF331.985∗∗∗0.089The bold values mean the factor is statistically significant.Table 13
Results of the braking duration based on free-driving state.
ParametersModel 1Model 2BBetaBBetaspeed_2nd−0.091∗∗∗−0.464−0.086∗∗∗−0.439distance_2nd0.079∗∗∗1.2300.078∗∗∗1.217traffic density0.392∗∗0.065R20.8100.814ΔR20.810∗∗∗0.004∗∗ΔF1020.824∗∗∗10.135∗∗


### 4.4. Discussion on Braking Characteristics

In summary, the braking characteristics of drivers stopping at urban intersections were mainly influenced by the self-vehicle motion state. Whether in the following state or in the free-driving state, drivers braked to a stop in a shorter time with greater average deceleration and maximum deceleration when the approach speed was higher or the reaction distance was shorter, which is consistent with the results of several previous studies. Gates et al. [16] found that the average deceleration increased with the increase of approach speed and decreased with the increase of the distance to the intersection. Deligianni et al. [36] showed that the increase in the available distance made drivers decrease the maximum deceleration and increase the braking duration. Different from the results of this study, Wang et al. [19] reported that drivers approaching intersections at high speed had longer deceleration time, which is attributed to the fact that their study aimed to analyze the distribution characteristics of average deceleration time over different speed intervals. The analysis ignored the effect of deceleration distance, while drivers with high speed usually had longer deceleration distance, which resulted in longer deceleration time.The results of both 4.1 and 4.3 showed that traffic density had a significant effect on the average deceleration and braking duration, while the results of 4.2 showed that all braking characteristics were independent of traffic density, but the decelerations were significantly influenced by the relative motion state, which indicates that the effect of traffic density on braking characteristics is mainly manifested in the free-driving state, and when drivers follow the preceding vehicle to stop at intersections, the braking characteristics are not influenced by the surrounding vehicles, i.e., traffic density, but are significantly related to the relative motion state with the preceding vehicle.Compared with free driving, drivers had a lower average deceleration and longer braking duration when following the preceding vehicle to stop. In addition, in the free-driving state, the high-density traffic also caused drivers to reduce the average deceleration and increase the braking duration. This suggests that both following and high density increase the perceived risk of drivers so that they start braking earlier with lower deceleration level, and therefore, the deceleration process is smoother in these conditions. The maximum deceleration did not show significant differences in the above two cases; however, when the drivers were in the following state, the smaller relative distance or larger relative speed between the two vehicles caused them to increase the maximum deceleration, and the smaller relative distance also caused them to increase the average deceleration. This is reasonable because the violent braking itself is a compensatory measure taken by drivers to decelerate in dangerous situations [37], and the relative distance and relative speed are the direct sources of drivers’ perception of collision risk.


## 4.1. Analysis of Braking Characteristics considering Self-Vehicle Motion State and Traffic Conditions

Firstly, the hierarchical linear regression models of average deceleration, maximum deceleration, and braking duration were established using the self-vehicle motion state, traffic state, and traffic density as the first-, second-, and third-level independent variables, respectively.The regression analysis results of the average deceleration are shown in Table5. Model 1 with the self-vehicle motion state as the independent variable was significant (P<0.001), and the goodness of fit R2 = 0.807. After adding the traffic state variable (Model 2), R2 increased by 0.005 and F increased by 21.583, which was statistically significant (P<0.001). After adding the traffic density variable (Model 3), R2 increased by 0.004 and F increased by 19.214, which was also statistically significant (P<0.001), indicating that the increase of environmental factors, namely, traffic state and traffic density, significantly improved the prediction level of average deceleration. The variables included in the final model were all significant at the 95% confidence level. The higher the speed when the drivers started braking, the greater the average deceleration; the longer the reaction distance, the smaller the average deceleration. Compared with free driving, the average deceleration in the following state was smaller. In addition, the average deceleration was negatively correlated with traffic density, and the average deceleration was smaller in high-density traffic.Table 5
Regression analysis results of the average deceleration.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.038∗∗∗−1.593−0.036∗∗∗−1.523−0.035∗∗∗−1.500distance_2nd0.010∗∗∗1.1910.010∗∗∗1.1610.010∗∗∗1.150traffic state0.063∗∗∗0.0830.049∗∗∗0.065traffic density0.053∗∗∗0.070R20.8070.8120.816ΔR20.807∗∗∗0.005∗∗∗0.004∗∗∗ΔF1871.705∗∗∗21.583∗∗∗19.214∗∗∗Note. ∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001. The bold values mean the factor is statistically significant.The regression analysis results of maximum deceleration are shown in Table6. Model 1 with the self-vehicle motion state as the independent variable was significant (P<0.001), R2 = 0.594. The goodness of fit and F value of the model were not significantly improved by increasing the traffic state variable and the traffic density variable (P>0.05), indicating that the environmental factors had no effect on the maximum deceleration, and the only variable related to the maximum deceleration was the self-vehicle motion state. Higher approach speed and shorter reaction distance led to higher maximum deceleration.Table 6
Regression analysis results of the maximum deceleration.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.057∗∗∗−1.343−0.059∗∗∗−1.384−0.059∗∗∗−1.383distance_2nd0.015∗∗∗0.9370.015∗∗∗0.9550.015∗∗∗0.954traffic state−0.066−0.048−0.067−0.048traffic density0.0040.003R20.5940.5950.595ΔR20.594∗∗∗0.0020.000ΔF653.688∗∗∗3.3290.015The bold values mean the factor is statistically significant.The regression analysis results of braking duration are shown in Table7. Model 1 was significant (P<0.001), R2 = 0.777. The goodness of fit and F value of the model were significantly improved after adding the traffic state variable and traffic density variable (P<0.001), indicating that both self-vehicle motion state and environmental factors had an effect on the braking duration. The greater the approach speed, the shorter the braking duration; the longer the reaction distance, the longer the braking duration. Furthermore, the braking duration was longer in the following state and in the high-density traffic.Table 7
Regression analysis results of the braking duration.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.116∗∗∗−0.578−0.098∗∗∗−0.493−0.095∗∗∗−0.473distance_2nd0.095∗∗∗1.3000.092∗∗∗1.2640.092∗∗∗1.254traffic state0.650∗∗∗0.1010.549∗∗∗0.085traffic density0.385∗∗∗0.060R20.7770.7840.787ΔR20.777∗∗∗0.007∗∗∗0.003∗∗∗ΔF1563.248∗∗∗28.048∗∗∗12.376∗∗∗The bold values mean the factor is statistically significant.


## 4.2. Analysis of Braking Characteristics Based on Following State

In order to study the influence of the relative motion state with the preceding vehicle on the braking characteristics, hierarchical linear regression models based on the following state were established with the self-vehicle motion state, relative motion state, and traffic density as the first-, second-, and third-level independent variables, respectively.The regression analysis results of the average deceleration are shown in Table8. Model 1 was significant (P<0.001), and R2 = 0.720. The changes in F and R2 of model 2 were significant, while the changes in F and R2 of model 3 were not significant, indicating that the average deceleration in the following state was not related to the traffic density, but was related to the relative motion state and the self-vehicle motion state. In addition to the high approach speed and short reaction distance, the short relative distance between the two vehicles also increased the average deceleration of drivers. The regression analysis results of the maximum deceleration are shown in Table 9. Only the independent variables added in model 2 significantly increased the F value and R2, which indicated that the relative motion state affected the maximum deceleration, and the larger the relative speed or the shorter the relative distance, the larger the maximum deceleration. The regression analysis results of braking duration are shown in Table 10. The changes of F and R2 both in model 2 and model 3 were not significant, which indicated that the braking duration in the following state was only influenced by the self-vehicle motion state.Table 8
Results of the average deceleration based on the following state.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.037∗∗∗−1.552−0.039∗∗∗−1.612−0.038∗∗∗−1.601distance_2nd0.012∗∗∗1.4270.012∗∗∗1.4400.012∗∗∗1.432relative speed_2nd0.0100.0500.0100.050relative distance_2nd0.005∗∗0.0920.005∗∗0.090traffic density0.0260.041R20.7200.7270.728ΔR20.720∗∗∗0.007∗0.002ΔF452.027∗∗∗4.377∗2.165Note. (1) ∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001. (2) relative speed_2nd and relative distance_2nd mean the longitudinal relative speed and relative distance when the driver starts braking. The bold values mean the factor is statistically significant.Table 9
Results of the maximum deceleration based on the following state.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.063∗∗∗−1.239−0.068∗∗∗−1.333−0.069∗∗∗−1.345distance_2nd0.019∗∗∗1.0360.019∗∗∗1.0530.019∗∗∗1.062relative speed_2nd0.042∗0.0950.042∗0.095relative distance_2nd0.019∗∗0.1500.019∗∗0.151traffic density−0.058−0.044R20.4490.4690.471ΔR20.449∗∗∗0.020∗∗0.002ΔF143.359∗∗∗6.554∗∗1.246The bold values mean the factor is statistically significant.Table 10
Results of the braking duration based on the following state.
ParametersModel 1Model 2Model 3BBetaBBetaBBetaspeed_2nd−0.126∗∗∗−0.443−0.131∗∗∗−0.459−0.129∗∗∗−0.453distance_2nd0.127∗∗∗1.2470.127∗∗∗1.2430.126∗∗∗1.238relative speed_2nd0.116∗0.0480.116∗0.048relative distance_2nd0.0240.0340.0230.034traffic density0.1790.024R20.8220.8250.825ΔR20.822∗∗∗0.0020.001ΔF813.612∗∗∗2.4891.171The bold values mean the factor is statistically significant.


## 4.3. Analysis of Braking Characteristics Based on Free-Driving State

Hierarchical linear regression models based on the free-driving state were established with the self-vehicle motion state and traffic density as the first and second independent variables. The regression analysis results of the average deceleration, maximum deceleration, and braking duration are shown in Tables11–13. The average deceleration and braking duration were affected by both the self-vehicle motion state and traffic density, while the maximum deceleration only depended on the self-vehicle motion state.Table 11
Results of the average deceleration based on free-driving state.
ParametersModel 1Model 2BBetaBBetaspeed_2nd−0.037∗∗∗−1.480−0.036∗∗∗−1.447distance_2nd0.010∗∗∗1.2050.10∗∗∗1.187traffic density0.066∗∗∗0.086R20.7710.778ΔR20.771∗∗∗0.007∗∗∗ΔF809.823∗∗∗14.877∗∗∗Note.∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001. The bold values mean the factor is statistically significant.Table 12
Results of the maximum deceleration based on free-driving state.
ParametersModel 1Model 2BBetaBBetaspeed_2nd−0.060∗∗∗−1.275−0.059∗∗∗−1.271distance_2nd0.014∗∗∗0.9340.014∗∗∗0.932traffic density0.0130.009R20.5800.580ΔR20.580∗∗∗0.000ΔF331.985∗∗∗0.089The bold values mean the factor is statistically significant.Table 13
Results of the braking duration based on free-driving state.
ParametersModel 1Model 2BBetaBBetaspeed_2nd−0.091∗∗∗−0.464−0.086∗∗∗−0.439distance_2nd0.079∗∗∗1.2300.078∗∗∗1.217traffic density0.392∗∗0.065R20.8100.814ΔR20.810∗∗∗0.004∗∗ΔF1020.824∗∗∗10.135∗∗


## 4.4. Discussion on Braking Characteristics

In summary, the braking characteristics of drivers stopping at urban intersections were mainly influenced by the self-vehicle motion state. Whether in the following state or in the free-driving state, drivers braked to a stop in a shorter time with greater average deceleration and maximum deceleration when the approach speed was higher or the reaction distance was shorter, which is consistent with the results of several previous studies. Gates et al. [16] found that the average deceleration increased with the increase of approach speed and decreased with the increase of the distance to the intersection. Deligianni et al. [36] showed that the increase in the available distance made drivers decrease the maximum deceleration and increase the braking duration. Different from the results of this study, Wang et al. [19] reported that drivers approaching intersections at high speed had longer deceleration time, which is attributed to the fact that their study aimed to analyze the distribution characteristics of average deceleration time over different speed intervals. The analysis ignored the effect of deceleration distance, while drivers with high speed usually had longer deceleration distance, which resulted in longer deceleration time.The results of both 4.1 and 4.3 showed that traffic density had a significant effect on the average deceleration and braking duration, while the results of 4.2 showed that all braking characteristics were independent of traffic density, but the decelerations were significantly influenced by the relative motion state, which indicates that the effect of traffic density on braking characteristics is mainly manifested in the free-driving state, and when drivers follow the preceding vehicle to stop at intersections, the braking characteristics are not influenced by the surrounding vehicles, i.e., traffic density, but are significantly related to the relative motion state with the preceding vehicle.Compared with free driving, drivers had a lower average deceleration and longer braking duration when following the preceding vehicle to stop. In addition, in the free-driving state, the high-density traffic also caused drivers to reduce the average deceleration and increase the braking duration. This suggests that both following and high density increase the perceived risk of drivers so that they start braking earlier with lower deceleration level, and therefore, the deceleration process is smoother in these conditions. The maximum deceleration did not show significant differences in the above two cases; however, when the drivers were in the following state, the smaller relative distance or larger relative speed between the two vehicles caused them to increase the maximum deceleration, and the smaller relative distance also caused them to increase the average deceleration. This is reasonable because the violent braking itself is a compensatory measure taken by drivers to decelerate in dangerous situations [37], and the relative distance and relative speed are the direct sources of drivers’ perception of collision risk.


## 5. Summary and Conclusion

This study examined the normal stopping behavior of drivers at urban intersections using natural driving data, focusing on two components of drivers’ reaction characteristics and braking characteristics to the upcoming intersection. In order to measure traffic density and reduce the workload, YOLOv4 was used to identify the vehicles traveling in different directions and their numbers, and the relative motion state was calculated based on the position information of the vehicle target and lane line.Reaction characteristics include approach speed and reaction distance. The results of descriptive statistics initially reflected the distribution characteristics of reaction characteristics, after which one-way ANOVA was applied to clarify the effects of traffic state and traffic density on the reaction characteristics. Specifically, traffic state has an effect on both the approach speed and reaction distance, with following traffic causing drivers to significantly reduce their approach speed and causing drivers with higher speeds to further increase their reaction distance in high-density traffic. Traffic density only affects the approach speed in the free-driving state, and drivers reduce their speed in high-density traffic, i.e., the approach speed is significantly higher without the influence of the preceding vehicle and surrounding vehicles. In addition, there is an obvious correlation between approach speed and reaction distance: the greater the approach speed, the longer the reaction distance; moreover, speed is the main factor affecting the reaction distance.Braking characteristics are another important feature of drivers stopping at intersections. The braking method was modeled and analyzed using average deceleration, maximum deceleration, and braking duration. The results show that the braking characteristics are mainly influenced by the self-vehicle motion state. When the approach speed is higher or the reaction distance is shorter, drivers brake to a stop in a shorter time with greater average deceleration and maximum deceleration. Traffic state and traffic density have an effect on deceleration level and braking duration. Both following state and high-density traffic cause drivers to reduce the average deceleration and increase the braking duration. However, when the drivers are in the following state, their braking characteristics are no longer influenced by traffic density but are related to the relative motion state with the preceding vehicle. Smaller relative distance and larger relative speed make drivers increase braking strength, and smaller relative distance also makes drivers increase deceleration level.In this study, the target detection algorithm is creatively applied to the research of driving behavior. The proposed traffic participant identification method and ranging method can effectively improve the cognitive ability of intelligent driving systems. Furthermore, this study provides some important insights into the normal stopping behavior of drivers at urban intersections, which has important reference significance for the human-like driving design of driver assistance systems. However, it is important to recognize that driving behavior is the result of multiple factors, and future research will be further extended to other variables such as road factors and human factors. On the other hand, future research will further explore the application of the traffic participant identification method and ranging method in actual traffic scenarios, refine the variables characterizing intersection environmental features and relative motion state based on the identified traffic participant information and ranging information, and analyze key factors affecting driver’s driving behavior. After that, the anthropomorphic driving decision models will be established for drivers performing different driving tasks at urban intersections.


## 6. Supplementary Description

Figure1 shows the key acquisition information for the natural driving data used in this study. Figure (a) shows the data acquisition system used in this natural driving study. Figure (b) shows the installation position of cameras, and the collected video data are shown in figure (d). Figure (c) shows the test vehicle used in this natural driving study.Figure2 shows the visualization of target recognition, with fps in the figure representing detection speed and the number on the bounding box representing the confidence level of the target.Figure3 shows the schematic of the ranging algorithm. The lane width and lateral relative distance of the corresponding position on the image are first obtained based on the position information of the lane line and vehicle target, and then, the actual lateral and longitudinal relative distance are calculated by the ratio method and similar triangle principle, respectively. The L and dx in the figure represent the lane width and the lateral relative distance between the front vehicle and host vehicle, respectively.The ranging algorithm was applied to the China-FOT data set. Figure4 shows the ranging effect of the algorithm, i.e., the change process of the lateral and longitudinal relative distances between the two vehicles when the driver follows the front vehicle to stop at the intersection.Figure5 shows the selection method of the moments when the driver releases the accelerator pedal and starts braking. The moment when the accelerator pedal is released is selected as the point where the driver finally reacts to the upcoming intersection, and the moment when the driver starts braking is selected as the point where the driver initially stepped on the brake pedal.Figure6 shows the distribution characteristics of approach speed and reaction distance at different traffic states, with the red line representing the free-driving state and the blue line representing the following state. The solid line represents the first reaction characteristic when the driver releases the accelerator pedal, and the dashed line represents the second reaction characteristic when the driver starts braking.Figure7 shows the distribution characteristics of approach speed and reaction distance at different traffic densities, with the red line representing low density and the blue line representing high density. The solid line represents the first reaction characteristic when the driver releases the accelerator pedal, and the dashed line represents the second reaction characteristic when the driver starts braking.Figures8–11 show the one-way ANOVA results for traffic state on reaction characteristics of drivers at different traffic densities. Among them, Figures 8 and 9 show the effect of traffic state on the first reaction characteristics, i.e., first approach speed and first reaction distance. Figures 10 and 11 show the effect of traffic state on the second reaction characteristics, i.e., second approach speed and second reaction distance. The error bar in the figure is 95% C.I. ∗means significant effect, ×means no significant effect.Figures12–15 show the one-way ANOVA results for traffic density on reaction characteristics of drivers at different traffic states. Among them, Figures 12 and 13 show the effect of traffic density on the first reaction characteristics, i.e., first approach speed and first reaction distance. Figures 14 and 15 show the effect of traffic density on the second reaction characteristics, i.e., second approach speed and second reaction distance. The error bar in the figure is 95% C.I. ∗means significant effect, ×means no significant effect.Table1 shows the traffic target labels established in target recognition and their corresponding descriptions. Vehicle targets are classified into three types according to their shapes, namely, car_front, car_rear, and car_side. Pedestrians, cyclists, as well as traffic lights and crosswalks are also marked and recognized in this study.Table2 shows the effectiveness of the 3 selected target detection algorithms on the China-FOT data set. mAP is mean average precision, which is used to characterize the average detection accuracy of target detection. Average frames per second is used to characterize the average speed of target detection, i.e., the average number of image frames recognized per second by the target detection algorithm.Table3 shows 7 relevant parameters of drivers braking to a stop at intersections, including reaction characteristics and braking characteristics. Speed_1st and distance_1st in the table mean the first approach speed and first reaction distance. Speed_2nd and distance_2nd mean the second approach speed and second reaction distance.Table4 shows the results of descriptive statistical analysis of reaction characteristics under different traffic states and traffic densities, where Min is the minimum value, Mean is the average value, Max is the maximum value, and Std. Dev. is the standard deviation.Tables5–7 show the results of the hierarchical linear regression analysis for average deceleration, maximum deceleration, and braking duration, with self-vehicle motion state, traffic state, and traffic density as the first-, second-, and third-level independent variables. Self-vehicle motion state refers to the second approach speed and second reaction distance. ΔR2 is the change in R2 and ΔF is the change in F, and the P values of their statistical tests are used to verify whether the added variables have a significant effect on the regression model. ∗means P<0.05, ∗∗means P<0.01, and ∗∗∗means P<0.001.Tables8–10 show the results of the hierarchical linear regression analysis for average deceleration, maximum deceleration, and braking duration in the following state, with self-vehicle motion state, relative motion state, and traffic density as the first-, second-, and third-level independent variables. Relative motion state refers to the longitudinal relative speed and distance when the driver starts braking. ΔR2 is the change in R2, and ΔF is the change in F, and the P values of their statistical tests are used to verify whether the added variables have a significant effect on the regression model. ∗means P<0.05, ∗∗means P<0.01, and ∗∗∗means P<0.001.Tables11–13 show the results of the hierarchical linear regression analysis for average deceleration, maximum deceleration, and braking duration in the free-driving state, with self-vehicle motion state and traffic density as the first- and second-level independent variables. ΔR2 is the change in R2, and ΔF is the change in F, and the P values of their statistical tests are used to verify whether the added variables have a significant effect on the regression model. ∗means P<0.05, ∗∗means P<0.01, and ∗∗∗means P<0.001.



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*Source: 7588589-2022-07-14.xml*