AEBS Perception Stability Study of Intelligent Vehicles Based on

C-NCAP

Ziwen Zhang, Xu Wang, Zhibo Zhang, Jianhua Zhou and Mingyang Liu

CATARC Automotive Test Center (Guangzhou) Co., Ltd., Guangzhou, China

Keywords: Automatic Emergency Brake System, Perception Stability, Vehicle Test, C-NCAP.

Abstract: In order to evaluate the perception stability and performance assessment accuracy of the autonomous

emergency braking system(AEBs) which is commonly marketed using the monocular cameras and

millimeter-wave radar fusion scheme, the speed sensitivity and target recognition ability are analyzed through

repeated tests with multiple working conditions as well as targets based on uncertainty. Then the influence on

C-NCAP scoring is determined. The results show that the stability of AEBs manifests a downward trend with

the increase of travel speed where it reduces more in ‘Car to Car moving (CCRm)’ condition than in ‘Car to

Car stationary (CCRs)’ condition. There is influence of stability on C-NCAP scoring that is 3.23% on average,

where the highest value is 6.57% in the high-speed test of CCRm when the lowest value is 2.14% in the high-

speed test of CCRs. The trend of influence that changes with speed is opposite under the two working

conditions of CCRm and CCRs. Greater influence is found for two-wheelers tests than for pedestrians cases.

1 INTRODUCTION

According to worldwide statistic on car accidents,

almost 50 million road users get hurt and 1.3 million

lose their lives due to traffic collision (WHO, 2015).

For the public safe, Automotive are getting more

intelligent at the present time, and active safety has

become one of the hot topics in the field of auto

safety. The development of advanced driver assist

system contributes to reduce driving risk (European

Comission, 2011)-(NHTSA, 2016). Autonomous

Emergency Braking system (AEBs) is an important

part of the active safety function. When a vehicle,

pedestrian or two-wheeler suddenly appears in front

of a moving vehicle with the failure of timely braking

resulting in high risk of collision, the assistance of

AEBs will help to avoid or mitigate the collision so

that it substantially improves the road safety (Fildes

B. et al., 2015). reported that compared to vehicles

without AEBs, similar ones equipped with the system

only encountered 62% rear-end collisions. Research

of Teoh E. R. also shows that AEB intervened in 43%

of rear-end crashes and about two thirds of these

interventions involved auto-brake activation so that

there was a significant reduction on number of

crashes.

However, many studies of AEBs have focused on

how to avoid collisions (Lee, J. et al., 2019)

(Koglbauer, I. et al., 2018), and in fact accidents are

still difficult to avoid in current traffic conditions.

Research results (Cicchino, J. B., 2017)-(Haus S et

al., 2019) have shown that although AEB can reduce

the risk of death and injury in the target population,

there are still about 40% of unavoidable accidents

(Rosén E, 2010), which indicates that attention should

be also paid on AEBs in crushing cases. A study by

Guo Lei et al found that the impact injury to

pedestrians was mainly determined by the collision

speed, and pedestrians were prone to fractures of the

lower limbs when the collision speed was greater than

41 km/h. Islam M reported that there were significant

differences in pedestrian-injury severity in different

speed cases. Also, Doecke S et al found that impact

speed was found to have a highly significant positive

relationship to risk of serious injury for all impact

types. These reported results emphasize the

importance of impact speed, which needs high level

of stability. Therefore, to evaluate the active safety

performance of a vehicle in a collision, the stability

of speed drop during a collision is equally important

in addition to avoiding the collision.

In order to protect the safety of consumers, many

countries and regions have established their own

automobile safety evaluation systems. In China, C-

NCAP (China New Car Assessment Program) has

become one of the important standards for evaluating

the safety performance of new cars. And now it is an

500

Zhang, Z., Wang, X., Zhang, Z., Zhou, J. and Liu, M.

AEBs Perception Stability Study of Intelligent Vehicles Based on C- NCAP.

DOI: 10.5220/0012286600003807

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Seminar on Artiﬁcial Intelligence, Networking and Information Technology (ANIT 2023), pages 500-508

ISBN: 978-989-758-677-4

important basis and reference for Chinese consumers

to choose a car. However, though C-NCAP provides

comprehensive test cases for new cars, the perception

uncertainty of sensors is not in the consideration of C-

NCAP. Chengyong Niu et al has found that the AEBs

sensors can be influenced by environmental factors,

which will lead to significant fluctuations in the

performance of automatic emergency braking.

According to the scoring of the assessment process,

in the case of the estimated value of the manufacturer

not provided, the test is conducted only once, and

even with the estimated value, only three tests are

conducted. Therefore, the fluctuations could result in

a certain level of uncertainty in the scoring of C-

NCAP with limited tests.

Based on the above discussion, perception

stability study is essential for the active safety

assessment of intelligent vehicles. And rare relevant

researches can be found at present. In this paper,

multiple course tests with different targets based on

AEBs cases in C-NCAP (version 2021) are conducted

and the results are further studied using uncertainty

analysis according to Evaluation and Expression of

Uncertainty in Measurement. The perception stability

characteristic of AEBs is found when vehicle travels

in various speed and under different target objects.

Combined with the evaluation rule of C-NCAP, the

influence of the perception stability on the score is

discovered as well. The conclusion has practical

significance for social traffic safety and provides data

support for component manufacturers to improve

sensor performance and design new fusion solutions.

Also it presents a more objective perspective for car

manufacturers and consumers with respect to C-

NCAP evaluation scores, which may help to develop

subsequent C-NCAP test protocols and more accurate

scoring rules.

2 UNCERTAINTY ANALYSIS ON

C-NCAP CASES

C-NCAP version 2021 tests for AEBs are divided into

AEB CCR (vehicle-to-vehicle rear-end condition)

and AEB VRU (vehicle-to-vulnerable road user),

where AEB VRU can be divided into three types of

crash objects, namely Ped (pedestrian), BTA

(Pedestrian Target Adult), and STA (Scooter Target

Adult). In this paper, based on the cases above

respectively, repeated tests at different speed points

will be conducted to analyze the uncertainty of the

results. It should be noted that this paper only

analyzes the speed at which collisions will occur,

because cases that can avoid collisions do not need to

consider the stability of the velocity drop.

2.1 Test Preparation

Tian-Yong studied that the sensing solution of AEBs

using millimeter wave radar fused with camera,

which well balances the cost and safety performance,

has become the choice of a large number of car

manufacturer nowadays. And in this paper, in order

to gain representative results, the sample car is

equipped with 5 cameras outside the vehicle as well

as 3 millimeter wave radars and 6 ultrasonic radars.

Besides, a identical sample has been tested based on

CNCAP with a announced scoring rate of 82% when

the average scoring rate in 2022 is 80.81% (Fanyu Liu,

2022).With the AEBs configuration and the C-NCAP

scoring, the test results of the selected sample car

posses representative and indicative value. Figure 1

(a) shows the prototype vehicle in the AEB CCR test

scenario. Figures 1(b) and (c) show the equipment for

testing (ABD driving robot system) and the test

subjects (4A target dummies, including pedestrians,

bicycles, and scooters). All the equipments of tests

are in good conditions and the accuracy requirements

can be met through measurement and inspection.

(a)Picture of the sample car in AEB CCR test.

(b)Steering, throttle and brake robot.

AEBs Perception Stability Study of Intelligent Vehicles Based on C- NCAP

501

Figure 1. Main hardware configurations of C-NCAP tests.

The uncertainty of the impact speed during the test

is mainly influenced by:

1) Uncertainty Introduced by the Test Method.

In the test process, the driver's ability to control the

vehicle will inevitably produce random errors, such

as whether the test vehicle speed is well stabilized in

the standard test speed within the specified time

frame. The unstable performance of the AEB sensor

of the sample vehicle itself can also lead to systematic

and random errors in the test. The state of the test

sample vehicle will also produce systematic errors,

such as the degree of vehicle break-in, tire pressure,

and the degree of wear on the car's wheels.

2) The Component from the Linearity Error of

the Device. According to the requirements of

appendix C 6.1.3.1 of C-NCAP version 2021, the

speed accuracy of the device is required to be 0.1km/h.

The speed accuracy of the model RT3002 high-

precision gyroscope used in this paper is calibrated to

0.05km/h.

Considering the small error caused by the

equipment and environment, when the vehicle

deceleration is mainly controlled by the AEBs, the

uncertainty in this study is mostly contributed by the

sensor stability of the test vehicle.

From the perspective of uncertainty, this paper

carries out variable control from the following

aspects: on the one hand, for the test equipment and

testers, the calibrated fixed base station differential

GPS positioning equipment is used to ensure the high

precision positioning of the vehicle, with a

positioning accuracy of 0.02m. In addition, ABD

driving robot with strict tuning is used to repeat the

precise control of the vehicle, without changing the

driver during the all tests. On the other hand, the

environment and the state of the sample car during the

tests also need to be ensured. The test site is

CATARC Automotive test site of south China.

Before conducting each test, it is confirmed that the

ground is dry, flat with clear lane lines without

rainfall, overheating and crosswind. Also the

visibility is checked to be higher than 1km. Vehicle

tire tread depth must be normal and the target location

of each test is fixed.

2.2 Uncertainty Calculation of

AEB-CCR

In C-NCAP version 2021, the scenario for CCR is

defined as a two-vehicle rear-end crashing condition.

Specifically, the test vehicle approaches the front

vehicle from the rear at constant speed while the front

vehicle is driving at low speed or stationary. Refer to

Figure 2, where V

VUT

is the speed of the test vehicle

and V

GVT

is the speed of the vehicle in front.

Figure 2. The test scene schematic of AEB CCR in C-

NCAP.

According to the requirements of C-NCAP, all

speed conditions of CCRs and CCRm are tested

separately. If AEB is successfully triggered and a

crash occurs, it is recorded as a valid test, and the

effective crash speed is recorded until the valid test

reaches 10 times. All the test result of CCR cases are

listed in Table 1.

Table 1. The results of CCR test.

Case CCRs CCRm

Initial speed

(km/h)

40 50 60 70 80 60 70 80

Impact

speed

(km/h)

15.5 17.6 21.1 27.7 30.6 8.2 15.4 22.3

2 14 16.3 22.3 28.8 32.7 7.9 10 17.3

3 15.3 18.4 24.5 27.4 33.6 5.5 11.3 23.8

4 13.8 19.2 20.8 25.2 35.4 6.2 13.5 16.5

5 12.7 20.7 23.4 26.6 35.8 4.7 17.2 18.4

6 14.4 18.7 22.6 28.3 36.5 8.3 12.2 23.3

7 16.3 19.5 24.7 25.1 32.5 7.9 14.8 25.8

8 16 16.9 25.1 24.4 33.6 8 11.2 25.1

9 16.5 17.5 24.5 25.7 31.8 4.5 17.5 27.5

10 14.2 18.1 25.3 27.3 34.3 6.9 13.3 20.3

According to reference (Evaluation and

Expression of Uncertainty in Measurement: JJF,

2012), the calculation procedure for the evaluation of

uncertainty components is as follows:

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502

1. Calculate the arithmetic average of 10

collision speeds for each speed case:





(1)

where n=10, S

represents the crash speed of group i.

2. Calculate the variance S

and standard

deviation S:

)(









(2)

)(

)1(

)(









(3)

3. Then the standard uncertainty u

introduced by

test method can be calculated through:

)(

)1(

/)(









SSi

nSSu

(4)

4. The inertial GPS combined test system used in

this paper, qualified by a third party, has an absolute

velocity accuracy of ±0.05km/h, indicating that the

half-width of the dispersion interval of the instrument

measurements is a=0.05km/h, estimated as a

rectangular distribution with confidence factor k=

√

then:

kau

/

(5)

where u

is the uncertainty introduced by the

equipment.

5. The synthetic uncertainty u

and the final

extended uncertainty U can be calculated by the

following formula:

bac

uuu 

(6)

KuU



(7)

where K is the inclusion factor and it is taken as 2 in

this paper.

So the uncertainty results of CCR are listed in

Table 2.

Table 2. AEB CCR uncertainty calculated results.

Parameter CCRs CCRm

Initial

speed(km/h)

40 50 60 70 80 60 70 80

Adverage of

impact

speed(km/h)

14.87 18.29 23.43 26.65 33.68 6.81 13.64 22.03

Mean

squared

error(km/h)

1.24 1.36 1.52 1.53 1.61 1.49 2.63 3.99

(km/h) 0.39 0.43 0.48 0.48 0.51 0.47 0.83 1.26

(km/h) 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03

(km/h) 0.39 0.43 0.48 0.48 0.51 0.47 0.83 1.26

U(km/h) 0.79 0.86 0.96 0.97 1.02 0.94 1.67 2.52

The extended uncertainty U indicates the possible

drift of impact speed value in this study. The

confidence level of the test results in the interval of

the impact speed V

impact

±U is 95% based on the above

calculation (Evaluation and Expression of

Uncertainty in Measurement: JJF, 2012).

2.3 Uncertainty Calculation of

AEB-VRU

In the scoring rules of C-NCAP, when it comes to

VRU cases that are plotted in figure 3, there are only

two kinds of scoring in cases with initial speed over

40km/h: if the speed drop at the time of the collision

is less than 20km/h, the scoring rate is 0, otherwise it

is 100%. So there will only be influence of impact

speed uncertainty on cases with initial speed equal or

less than 40km/h. Besides, during the tests, it is found

that collision occurs only when the sample travels at

40km/h.

As shown in figure 3, the selected cases of VRU

test are CPFA, CSFA and CBNA that respectively

mean an adult pedestrian travel from the left side of

the sample velocity direction to the collision position,

a two wheel bicycle rider travel from the left side of

the sample velocity direction to the collision position

and a two wheel scooter travel from the right side of

the sample velocity direction to the collision position.

The reference points are at the shoulder for pedestrian,

at the bottommost part of the crankshaft of the bracket

for bicycle, at the most forward place for scooter. ‘50’

means the collision happen at the middle of the width

of the car. None offset case is in the consideration that

it will be presented in the future study.

(a) CPFA test case

AEBs Perception Stability Study of Intelligent Vehicles Based on C- NCAP

503

(b) CSFA test case

Figure 3. VRU test scenes schematic in C-NCAP.

vut,

Ped

, V

STA

and V

BTA

indicate the initial speed of

the vehicle under test, the adult pedestrian, the scooter

and the bicycle, respectively.

Results of ten tests for all cases of VRU are listed

in Table 3.

Table 3. Impact speed results of AEB VRU.

Case CPFA CBNA CSFA

Initial speed(km/h) 40 40 40

Impact

Speed

(km/h)

10.3 12.3 11.8

2 9.7 13.3 13.8

3 11.7 13.5 13.4

4 10.5 12.5 10.9

5 9.1 13.6 14.1

6 10.2 10.7 11.2

7 10.7 11.5 12.7

8 10.6 13.8 11.3

9 9.9 10.2 10.3

10 8.4 10.7 13.8

Similarly, using Eqs. (1)-(7), the uncertainty

assessment results for AEB_VRU can be obtained

and Table 4 lists the calculated results for CPFA,

CBNA, and CSFA, showing the degree of dispersion

of the impact speed.

Table 4. AEB VRU uncertainty calculated results.

CPFA CBNA CSFA

Initial speed(km/h) 40 40 40

Average impact speed(km/h) 10.11 12.21 12.33

Mean squared error(km/h) 0.96 1.44 1.47

(km/h) 0.30 0.45 0.46

(km/h) 0.03 0.03 0.03

(km/h) 0.31 0.46 0.46

U(km/h) 0.61 0.91 0.93

3 DISCUSSION

3.1 Analysis of Test Results

From the course test and uncertainty analysis in the

previous chapter, it can be seen that the impact speed

rises with V

vut

in CCRs condition: when V

vut

40km/h, the average impact speed is 14.87km/h and

the speed drop is 25.13km/h, while when V

vut

80km/h, the average impact speed is 33.68km/h and

the speed drop is 46.32km/h. This indicates that

higher initial speed leads to higher impact speed

though AEBs is involved. Same conclusion is shown

in Figure 4 that though the speed drop rises, cases

with higher initial speed may result in higher impact

speed with greater possibility of collision. Moreover,

histogram in figure 5 shows that the uncertainty also

increases with rising initial speed. At V

vut

=40km/h, U

value is 0.79km/h, and the interval of impact speed

results is [14.08,15.66]km/h. These results increase to

be 1.02km/h and [32.66,64.67]km/h at higher

vut

=80km/h where the value of U is up 29%

compared to case at V

vut

=40km/h. This illustrates that

in an emergency braking scenario against a stationary

vehicle in front, the dynamic sensing capability and

accuracy of the camera and radar fusion scheme may

decrease as the vehicle travels at higher speed,

exhibiting a more erratic performance.

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Figure 4. Uncertainty and impact speed results at various

initial speed in CCRs cases.

In cases of CCRm, similar characteristics are

found compared to CCRS, but also new features are

shown. Red line in Figure 5 indicates the rise of

impact speed with higher initial vehicle speed: when

vut

is 60km/h, average impact speed is 6.81km/h and

relative speed drop ∆V

rel

is 33.19km/h, while when

vut

is 80km/h, average impact speed is 22.03km/h

and relative speed drop ∆V

rel

is 37.97km/h. The

intervals of impact speed with V

vut

=[60,70,80]km/h

are [5.87,7.75]km/h, [11.97,15.31]km/h,

[19.51,24.55]km/h, respectively. It can be seen that as

the test vehicle speed increases, ∆V

rel

also increases.

However, compared to CCRs, ∆V

rel

is found to be

larger at the same V

vut

. Combined with the subjective

driver perception during the tests, this may be due to

the fact that the sensor may mistakenly identify the

vehicle in front as stationary and issue a more

aggressive braking command when the dynamic

recognition capability is not sufficient.

Figure 5. Uncertainty and impact speed results at various

initial speed in CCRm cases.

Figure 6 compares the variation of ∆V

rel

with V

rel

for the two operating conditions of CCRm and CCRs.

It illustrates that ∆V

rel

may be larger at low speed

conditions in CCRm, but the rate of change is higher

in CCRs, indicating that ∆V

rel

tends to be the same for

both cases as V

rel

increases.

Figure 6. Comparison of ∆V

rel

of CCRs and CCRm with the

same V

rel.

For the C-NCAP road vulnerable user test, this

paper conducted CPFA, CBNA, CSFA in the test

vehicle speed of 40km / h working conditions

respectively and the uncertainty comparison results

obtained can be seen in Figure 7. The results showed

that the average values of impact speed for pedestrian

crossing, bicycle crossing and motorcycle crossing

are 10.11km/h, 12.21km/h and 12.33km/h,

respectively, with the extended uncertainty values of

0.61km/h, 0.91km/h and 0.93km/h, and the collision

speed intervals are [9.5,10.72]km/h, [11.3.

13.12]km/h, [11.4,13.26]km/h. Among these three

operating conditions, the pedestrian crossing scenario

is the one with the lowest impact speed and

uncertainty values. In contrast, when facing bicycles

and motorcycles, the impact speed and uncertainty

magnitude are very close, and the AEB system shows

similar performance in front of both objects, probably

due to the more similar morphology and behavior of

both, while the more complex morphology and

posture make higher performance requirements for

sensors than pedestrian recognition.

Figure 7. Uncertainty and impact speed results at various

initial speed in VRU cases.

AEBs Perception Stability Study of Intelligent Vehicles Based on C- NCAP

505

3.2 Influence on C-NCAP Scoring

According to the scoring rules of C-NCAP version

2021, the score rate of each test speed point in AEB

CCR and AEB VRU is P=(V

rel,test

-V

rel,impact

)/V

rel,test

where V

rel,test

is the relative speed of the test vehicle

and the front vehicle at the beginning of the test and

rel,impact

is the relative speed of the test vehicle and

the front vehicle at the time of collision. Based on the

score rate, the specific scores are calculated as follow:

kjiPS

score







(8)

where P represents the score rate,

represents the

speed weight, j is the scene weight, and k is the full

scene score. All the parameters in equation (8) can be

seen in the following Table 5:

Table 5. Parameters for calculating the final score in

equation (8).

Parameter V

vut

k i j

CCRs

40 4 3/14 1/2

50 4 1/14 1/2

60 4 3/14 1/2

70 4 1/14 1/2

80 4 1/7 1/2

CCRm

60 7 1/8 1/2

70 7 3/16 1/2

80 7 3/16 1/2

CPFA 40 2 2/7 1/2

CBNA 40 4 2/7 1

CSFA 40 4 1/3 1

Then the influence of uncertainty on score can be

calculated using the same equations. Combined with

the intervals of results of each cases conducted in

previous chapters, the upper and lower extremes of

the scores due to the uncertainty in the AEB series of

C-NCAP tests and the magnitude of the one-sided

fluctuations can be directly obtained. The calculated

results are given in Table 6. It is shown that all tests

in this study receive a total score of 4.06 based on

average impact speed. Furthermore, considering the

uncertainty, the extreme value of the score is 4.20 for

the high level and 3.92 for the low level, with a

difference of 0.28 and a one-sided error of 0.14.

Table 6. Score result and influence of uncertainty.

Case V

vut

Score of

average

impact

speed

Tota

scor

Extreme

value

For

high

level

Extreme

value

For

Low

level

One-side

error

CCRs

40 0.27

4.06 4.20 3.92 0.14

50 0.09

60 0.26

70 0.09

80 0.17

CCRm

60 0.36

70 0.48

80 0.42

CPFA 40 0.21

CBNA 40 0.79

CSFA 40 0.92

There are 11 test cases in total conducted in this

study that generate the one-side error of 0.14 in score.

However, C-NCAP has 75 cases which means that the

final error can be far more than it with more

influencing factor in different offset rates and light

conditions.

The one-side error for every single case can be

calculated through

%100

score average

error side-one

level influence 

(9)

that the result represents the influence of uncertainty

caused by stability of AEBs sensors on the score of

every single case and it is plotted in Figure 8. The

black dash line is the average level which is 3.23%.

Figure 8 indicates the following conclusions:

1) In CCRs cases, though the uncertainty

increases with rising Vvut, the influence level

decreases with maximum of 3.12% in 40km/h and

minimum of 2.14% in 80km/h. Moreover, each case

of CCRs has lower influence level than average and

the differences between them are not obvious. This

indicates AEBs has more stable sensitivity of speed

and has a relatively mature technology in car-to-

vehicle stationary conditions.

2) The performance in CCRm of Figure 8 is

contrary to which in CCRs. The influence level grow

rapidly with higher Vvut that it is 2.86% in 60km/h

and 6.57% in 80km/h. Furthermore, the results of

70km/h and 80km/h are significantly higher than the

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506

average level and 2 to 3 times larger than that of other

scene cases. This founding shows high sensitivity of

speed of the sensor perception stability in front

moving vehicle recognition which may caused by a

misjudgment of the front distance to the target.

3) For VRU tests, the results are corroborated

with the previous analysis for Figure 5. The influence

level is 2.08% for Ped while for BTA and STA it

becomes obviously larger that is around the average

level. The influence level for Ped of 2.08% which is

the lowest value among all tests shows the most stable

capability for pedestrian recognition.

Figure 8. Influence level of uncertainty on C-NCAP score.

4 CONCLUSIONS

This study analyzes the stability of AEBs sensor

perception through uncertainty calculation based on

C-NCAP test cases. Five kinds of tests are selected

which are CCRs, CCRm, VRU_Ped, VRU_BTA and

VRU_STA and each of these test has been repeated

for 10 times at all initial speeds that would cause

collision. The characteristics of the AEBs sensing

stability are obtained and the influence level of the

impact speed uncertainty on C-NCAP score is further

analyzed. Main findings are as follow:

1. As vehicle speed increases, even if the AEB

system is functional, the impact speed of the

vehicle will still increase accordingly resulting in

a higher risk of injury in the event that a collision

cannot be avoided. At the same time, the sensor

performance stability of the AEB system shows a

significant downward trend: the value of U at

80km/h rises by 29% compared to which at

40km/h in CCRs; the value of U at 80km/h rises

by 168% compared to which at 60km/h in CCRm.

The stability of the AEBs is worse in recognizing

a front moving vehicle.

2. When the relative speed between two

vehicles is low, the impact speed under CCRm

would be lower than it is under CCRs. However,

they tend to be consistent with higher relative

speed.

3. The 11 cases in this study generates a one-

side error of 0.14 for C-NCAP score. Furthermore,

it can be inferred that the error will be much larger

for all 75 cases according to C-NCAP with

various offset rates and light conditions.

4. It is interesting that though the value of U

increase with rising vehicle speed, the influence

level of it on C-NCAP score has the opposite trend

in CCRs cases, which is lower than the average

level under all test with different vehicle speed.

The average level is 3.23%, compared to which

the influence level of CCRm cases is significant

larger especially under high speed which is the

highest value of 6.57% among all conducted cases.

This finding shows high sensitivity of speed of the

sensor perception stability in front moving vehicle

recognition.

5. For VRU cases, the value of U and the

influence level on score of Ped case is obviously

lower than other two cases that are around average

level. It indicates that pedestrian recognition may

be relatively more stable and the more complex

morphology and posture makes higher

performance requirements for sensors on bicycle

and scooter recognition.

The above conclusions have safety implications

for social traffic that drivers are not recommended to

rely on AEBs and let down their guard while the

vehicle is travelling. The perception stability

characteristics of AEBs provide reference for

component manufacturers to improve sensor

performance and design new fusion solutions. Finally,

in the evaluation of the active safety of the vehicle,

there are errors in the score caused by uncertainty,

which provides a more objective perspective for

ordinary consumers to understand the vehicle

performance information through C-NCAP, and also

provides support for the subsequent C-NCAP to

improve the test protocols and develop more accurate

scoring rules.

ACKNOWLEDGMENTS

This work is financially supported by National Key

R&D Program of Guangdong Province under the

project “Research on Key Technologies for

Performance Testing of Automated Vehicles and

Components” under the number 2020B0909050003.

AEBs Perception Stability Study of Intelligent Vehicles Based on C- NCAP

507

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