ITARDA 22th Work Shop Report2019
22nd Presentation Session for Traffic Accident Investigations, Analysis, and Research

Supplementary analysis of the results of collision damage mitigation brakes(Automatic Emergency Brakes: AEB) in reducing rear-end accidents
Yoshihiko Kinoshita
Senior Researcher, Research Division

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ITARDA 22th Work Shop Report

1.

Introduction

In recent years, much attention has been paid to the frequent occurrence of accidents caused by human factors. We
can now be said to be gradually approaching a period in which the spread and increased use of various preventive
safety devices will expand on account of the fact that such devices are now being equipped on vehicles as acountermeasure to such accidents. On this account, at last year's research presentation session there was a report on
the results of an analysis concerning the results collision damage mitigation brakes (Automatic Emergency Brakes(AEB)) have had in reducing rear-end collision casualty accidents with four-wheel vehicles. Based on this, the factthat equipping AEB on vehicles has proven effective at reducing casualty accidents has been confirmed acrossvarious different vehicle parties, danger perception speeds, times of day, and by the age of the driver. Yet at thesame time, rear-end collision casualty accidents still continue to occur with great frequency even when AEB are
equipped. This has clearly revealed the importance of preventing overconfidence in the sense that people believethat just having AEB equipped will cause their vehicle to stop automatically, enabling them to avoid accidents.
Therefore, rear-end collision casualty accidents that occurred despite the vehicle being equipped with AEB weretaken up and a multiple regression analysis performed on them this year. From this, the results of considering theconnection between the effects of AEB with various variables will be reported. What is more, in an analysis bydriver age from last year, the age distribution of the licensed population was used to estimate the number of vehicles
owned by whether or not they were equipped with AEB and by the owner's age group. However, it is difficult tomake the claim that this is consistent with the distribution of vehicles equipped with AEB owned by age group.
Therefore, the notion of traffic exposure was introduced in order to once more estimate the number of said vehicles
and reanalyze this, with the results of this reported here. In conjunction with this, the results from using traffic
exposure to analyze this by the number of years that have elapsed since the driver obtained their license will bereported. This report will begin by indicating the results of an analysis by age and by the number of years that haveelapsed since the driver obtained their license through the use of traffic exposure, before moving on to providing an
explanation of the results of the multiple regression analysis. In the previous study, the analysis was performed by
dividing vehicles up by kei sized vehicles, small sized vehicles, and medium sized vehicles that are all privately
owned. However, segregating out the number of registered vehicles led to a decrease, thereby leading to results thatwere not statistically significant. On account of this, this study performed a batch analysis by lumping together all
private passenger cars.
[Explanation of how Automatic Emergency Brakes (AEB) function]
Just to reiterate from the previous study, but the mechanism by which AEB operates is shown once more in Fig. 1.
First, radar or cameras detect the presence of a leading vehicle up ahead while the vehicle in question is in motion.
Next, if it detects that the distance between the vehicles is now shorter than a distance that has been set in advance,
the device will determine that there is the possibility of a rear-end collision occurring and sound a warning to alert
the driver of danger. Moreover, it is designed so that should the driver fail to apply the brake and the distance
between vehicles were to shrink, it will immediately pressurize the brakes in order to get ready to apply them.
Should this distance between vehicles shrink still further, it would determine that the risk of a rear-end collision was
extremely high and automatically apply the brakes. However, it is not necessarily always the case that automatically
applying the brakes will make it possible to avoid accidents, and there may be cases in which collisions still occur,
albeit at a reduced rate of speed. It is for this reason that such devices are also called as collision damage mitigation
brakes.

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ITARDA 22th Work Shop Report

Possibility ofa collisionexists

Detection

Moderatepossibility ofa collisionBrakepressurizes

WarningHighpossibility ofa collision

AutomaticallybrakesFig. 1. Mechanism by which collision damage mitigation brakes operate
2.

Number of registered vehicles by whether or not AEB is equipped on them

The previous study used the number of vehicles registered at the end of each year from 2015 - 2017, but this study
newly added in registration data from the end of 2018 (Table 1). Fig. 2 shows the trends in the number of vehicles,
indicating that there has been a sudden surge in the number of vehicles equipped with AEB.
Table 1. Number of registered vehicles at the end of the year by whether or not they are equipped with AEB
End of 2015

End of 2016

End of 2017

End of 2018

AEB not equipped

626,1491,498,747

2,118,382

2,463,351

AEB equipped

1,034,115

2,957,496

5,729,421

9,285,942

Converted to a mid-year
value for 2018

AEB equipped

Converted to a mid-year
value for 2017
Converted to a mid-year
value for 2016
AEB not equipped

End of 2015

End of 2016

End of 2017

End of 2018

Fig. 2. Trends in the number of vehicles registered by whether or not they have AEB equipped
and the number when converted to mid-year values
In the subsequent analysis, the number of accidents per 100,000 vehicles will be calculated for both those vehicles
with and without AEB systems equipped. The number of vehicles that will be used for this will not be the year-end
value. Rather, a mid-year value for the number of vehicles from the beginning of the year (or more accurately, theend of the previous year) and the end of the year will be used. Fig. 2 shows the mid-year values for the number ofvehicles from each year between 2016 - 2018. The total values for the number of vehicles converted to mid-year
values for 2016 - 2018 (as calculated from this yearly median number of vehicles) is shown in Table 2. This numberof vehicles is what will be used in the subsequent analysis.

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ITARDA 22th Work Shop Report

Table 2. Number of registered vehicles between 2016 - 2018 used in the analysis

3.

AEB

No. of vehicles converted to mid-year
values for 2016 - 2018

Not equipped

Equipped

Private passenger cars

5,161,879

13,846,946

Analysis of the effects of reducing rear-end collision casualty accidents

3-1. Traffic exposure
Traffic exposure is an indicator expressing the usage frequency of road traffic. The number of secondary parties,
who are not at fault when traffic accidents occur, is often used for this. For example, when the number of vehicles
that were collided with and thus not at fault during vehicle-vehicle accidents (the secondary party) were compared
for Vehicle A and Vehicle B, the results indicated that the ratio for this was 10:1 for Vehicle A:Vehicle B. From
this, it can be estimated that the ratio of the number of vehicles with traffic exposure for Vehicles A and B travelingon the roads is also 10:1. See "A study on techniques for determining the characteristics of traffic behavior basedon an analysis of traffic accidents," which contains a reference analysis at the end of the paper, for details.
This technique was also used for this analysis. The number of vehicles on the receiving end of rear-end collisions
when rear-end collision accidents occurred was used to calculate the traffic exposure.
Fig. 3 shows the process by which traffic exposure was introduced to calculate the estimated number of vehicles by
the age of the driver and whether or not they have AEB equipped.
Estimated No. of vehicles by
No. of vehicles on the receiving end Composition rate for vehicles on thewhether they had AEBof rear-end collisions by whether receiving end of rear-end collisions byequipped or notthey had AEB equipped or notwhether they had AEB equipped or not

Driver's age- 29

30 - 49

50 - 64

65 - 74
75 -Fig. 3. Relational diagram for the estimated number of vehicles by driver age

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To begin with, the number of vehicles on the receiving end of rear-end collisions (secondary party) in rear-end
collision accidents between 2016 - 2018 was tabulated by whether or not they were equipped with AEB systems,with the results of this indicated on the left-hand side. Figures expressing the composition rate for this are shown inthe middle, which can be considered an indication of the number of vehicles both with / without AEB via this ratioon the road. Then, the results of allocating the number of registered vehicles from Table 2 according to the ratio in
the center form the figures on the right-hand side. In this manner, the number of registered vehicles was calculated
for each age group.
3-2. Results of reducing rear-end collision casualty accidents by the driver's age
The number of rear-end collision accidents by the age group of the driver that caused said accidents (the primary
party) was divided by the number of registered vehicles by each age range found from Fig. 3 and converted to abasis of 100,000 vehicles. The results of this are shown in Fig. 4. From this, high figures were found for the cohortof young people age 29 and younger and the cohort of elderly people age 75 and older, revealing that these agegroups are at an elevated risk of causing rear-end collision accidents. It can be pointed out that results of reducing
rear-end collision accidents from AEB were obtained with a degree of confidence of a 1% significance level forwhole of these age groups. The thin line inscribed on each bar graph shows the confidence interval via an F
distribution. The numerical figures for the reduction rate are listed to serve as a reference for the values obtained
when this is compared against the average values from when AEB is equipped and not equipped.
3-3. Results of reducing rear-end collision casualty accidents by the number of years since the driverobtained their driver's license
Just like with the driver's age, the number of vehicles on the receiving end of a rear-end collision (secondary party)
was tabulated by the number of years since the driver obtained their driver's license for both vehicles with andwithout AEB. The estimated number of registered vehicles was then found for both those with and without AEB
from this composition rate and the number of registered vehicles in Table 2. The number of rear-end collision
accidents was divided using this number of vehicles for each number of years since the driver obtained their driver's
license for the driver that caused the rear-end collision accident (primary party). This was then converted to a
100,000 vehicle basis, with the results of this shown in Fig. 5. This provided a good understanding of the situation,in which the risk of a rear-end collision accident is highest immediately after one obtains one's license, but then falls
as the driver amasses driving experience. AEB proved effective at reducing rear-end collision accidents across every
age group.

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ITARDA 22th Work Shop Report**: Significance level of 1%

No. of casualty accidents per 100,000 vehicles

Without AEB
With AEB- 29

50 - 64

30 - 49

65 - 74

75 -Fig. 4. Results of AEB by driver age**: Significance level of 1%
No. of casualty accidents per 100,000 vehicles

Without AEB
With AEB

Less than
1 yearLess than
2 yearLess than
3 yearLess than
4 yearLess than
5 yearLess than
10 year10 or more
years

Fig. 5. Results of AEB by the number of years since the driver obtained their driver's license

6ITARDA 22th Work Shop Report

4.

Multiple regression analysis regarding rear-end collision accidents caused by AEB-equipped vehicles

The analyses performed thus far have proven that numerous rear-end collision accidents still occur even when AEB
systems are equipped on vehicles. Therefore, this section will focus on rear-end collision accidents caused by AEBequipped vehicles to explain the results obtained by attempting to explore the factors that affect the results of AEB
via a multiple regression analysis.
The weighted least squares (WLS) technique was employed for the analysis, and optimal combinations were found
via a reduction method to find the solution. This technique has also been adopted in papers such as a research report
entitled "Results of an analysis of the correlation between traffic accidents, drivers, and vehicles" published by
ITARDA in the past.
The number of rear-end collision accidents per the number of registered vehicles was adopted as the dependent
variable for the multiple regression equation, while the 14 items shown in Table 3 were set as the explanatory
variables for this. Regarding these items, factors acknowledged as potentially affecting the AEB results wereselected by confirming the trends from the macro data in advance. The details of this will be omitted to avoid anoverly lengthy explanation.
Table 3. List of explanatory variables
Category

Explanatory variable

Danger perception speed of the primaryparty

30km/h or lessOver 60km/h
Urban area
Uninterrupted road section
No signalWet
29 and younger
65 and older

Terrain
Road configuration
SignalRoad surface condition
Age of the primary party driver
No. of years since primary party driverobtained their driver's license

Less than five years ago
CommutingWork
Shopping
Starting up
Accelerating / decelerating in a straight line

Traffic purpose

Type of movement

The number of registered vehicles by model for the vehicles equipped with AEB is needed for the analysis, andlikewise so is the number of rear-end collision accidents by model. The figures for each of these are provided below.
Number of vehicle models equipped with AEB: 473 models
Number of vehicles equipped with AEB: 13,591,448 vehicles
Number of rear-end collision accidents caused by AEB-equipped vehicles: 18,935 accidents
The data to be entered that was prepared from the above conditions was plugged into the multiple regression
equation. However, in order to complete the processing in the ordinary manner, it was necessary to narrow the focus
down to models for which 54 rear-end collision accidents or more had occurred. As a result, in the end the processing
was carried out via the data listed below.
Number of vehicle models equipped with AEB: 63 models
Number of vehicles equipped with AEB: 9,494,650 vehicles (69.9%)

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Number of rear-end collision accidents caused by AEB-equipped vehicles: 15,548 accidents (82.1%)
The results of the analysis are indicated in Table 4. In general, the results of multiple regression analyses involvethe output of numerous cases depending on the combinations of the explanatory variables. Then a figure known asan Akaike Information Criterion (AIC) is used as a criterion for determining which of these offer a high degree ofstatistical reliability, with the smaller this value being the higher the reliability. Therefore, for this study three cases
were taken up and expressed from among the combinations with a small AIC. Variables with a negative regression
coefficient have the effect of causing a drop in the dependent variables (in this case, the number of rear-end collision
accidents per number of registered vehicles). Looking at the results of this indicates that the factors of: obtained
one's license less than five years ago ⇒ urban area ⇒ danger perception speed of 30km/h or less appeared tocontribute the most to reducing rear-end collision accidents in this order. The thinking behind this is that travelingspeed is moderated within urban areas, and is correlated with a danger perception speed of 30km/h or less to acertain extent. If we were to summarize these findings in an easy to understand manner, we would see that novice
drivers who only recently obtained their licenses tend to be most prone to the effects of AEB assistance, with AEB
most likely to prove effective in comparatively low speed ranges where the traveling speed is 30km/h or less inparticular. Most vehicles equipped with AEB, which were the focus of this analysis, are believed to be vehicles
equipped with initial systems that have their target activation speed set lower than around 30km/h. As such, theseresults in which the benefits are not obtained in speed ranges that exceed 30km/h could be described as valid. To
put it another way, in order to expand the effectiveness of AEB at reducing rear-end collision accidents in the future,
it will presumably be necessary to disseminate AEB that target high speed ranges, the installation of which has
already begun in earnest.
Table 4. List of the results of the multiple regression analysis
Regression
No Explanatory variable
coefficient
1 30km/h or less-0.0040

Vehicles equipped with AEB systems
Standard
t value
F valueerror
0.0208-0.195-0.462

Urban areas-0.00610.0478-0.12729 and younger

0.00840.0511

0.163

Less than 5 years-0.01110.0626-0.177Work
Constant term

0.00350.00820.04320.0328

0.081
0.249

2 30km/h or less-0.0051

0.0227-0.223

Urban areas
Uninterrupted roadsection
29 and younger-0.00540.0488-0.110-0.00460.0400-0.1140.00890.05270.169

Less than 5 years-0.01210.0655-0.185Work

0.00450.04340.103

Constant term

0.01120.04270.262

3 30km/h or less-0.0038

0.0215-0.175

Urban areas-0.00420.0494-0.08529 and younger

0.00730.05270.138

Less than 5 years-0.0092

0.0640-0.144

Constant term

0.00680.03270.207

8R2 value

5% F value 5% t value

0.173

2.377

2.002

-0.392

0.186

2.266

2.003

-0.560

0.172

2.531

2.002

ITARDA 22th Work Shop Report

With this study, the R2 value (coefficient of determination) was smaller than 0.2, meaning the analytical results are
somewhat lacking in explanatory power. Therefore, efforts should be made to improve the accuracy of the resultsby accumulating a greater number of data points for the future.
5.

Conclusion(1) The effects of AEB were calculated by both the driver's age and the number of years since the driver obtained
their driver's license via traffic exposure· Both those 29 and younger and 75 and older have an elevated risk of causing rear-end collision accidents.
· The risk of causing a rear-end collision accident decreases the longer one has had one's license.
· AEB was confirmed to be effective across each age group.
(2) Multiple regression analysis on rear-end collision accidents using vehicles equipped with AEB· Benefits were obtained for factors in the order of: Obtained one's license less than five years ago ⇒ urban
area ⇒ danger perception speed of 30km/h or less
· The expectation is that rear-end collision accidents can be further reduced by expanding the spread of nextgeneration AEB that active in high speed ranges of over 30km/h as well in the future.
＜引用・参考文献＞(1) 木下義彦、2018 年、「AEB による追突事故低減効果の分析」、第 21 回 交通事故・調査分析研究発表会
資料、（公財）交通事故総合分析センター(2) 西田 泰、他、2012 年、「交通事故分析に基づく交通行動特性の把握手法に関する研究」 （公社）日本
交通政策研究会（了）

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