ITARDA 24th Work Shop Report2021
The 24th Traffic Accidents Investigation Analysis Research Presentation Meeting

Study of the Field of View (FOV) of AEB for Pedestrian When the Vehicle Turns Right~ Targeting Pedestrians While Crossing the Road on the Crosswalk ~Naoki Nishiyama
Researcher, Research Department

1

ITARDA 24th Work Shop Report

1. Background
Pedestrians were the highest number of fatalities and serious injuries in traffic accidents according to thecircumstances at the time of the accident in 2020. When we focus on pedestrians' crossing places, the number ofpedestrians during crossing the road on the crosswalk is the highest, and vehicles turning right recorded the highest
number as vehicle action in such situations.
In Chapter 2, we analyzed the features of pedestrians' traffic accidents of fatalities and serious injuries duringcrossing the crosswalk caused by vehicles turning right from the perspective of traffic accident macro data
(hereinafter referred to as "macro data") and studied their countermeasures. In Chapters 3 and 4, we materialized
the countermeasures by using the results of the investigation into actual traffic accident examples that occurred in
Ibaraki Prefecture (hereinafter referred to as "micro data").

2. Features of the accidents of pedestrians while crossing the crosswalk caused by vehicles turning right andthe policy of countermeasures
To grasp the features of fatal and serious injury accidents of pedestrians who cross the crosswalk caused by
vehicles turning right, we compared such accidents with general trend of pedestrian-vehicle accidents with regard
to danger perception speed, driver human factors, and age groups of pedestrians. The macro data in this chapter
were totalized as a total of primary parties and secondary parties for four-wheeled vehicles turning right with theroad configuration being intersections.
2-1. Danger perception speed
As the danger perception speed in fatal and serious injury accidents, 30 km/h or less accounted for 98.2% in
accidents of pedestrians whocross the crosswalk caused byvehicle turning right, while it

30 km/h 10 km/h oror less
less
14.6%
20.5%

10 km/h or
less
25.4%

accounted for 65.9% in theentire

pedestrian-vehicle

accidents. (Fig.1 Red-framedpart)

Therefore,

itisunderstood that for vehicles
turningright,

countermeasures for the speed of

20 km/h or less
63.1%

Pedestrians who cross the crosswalk
while the vehicle turns right (N=1390)

30 km/h or
less
12.5%

20 km/h or
less
27.2%

Pedestrian-vehicle accidents(N=6952)

Fig.1 Ratio of the number of fatalities and seriously injured persons according
to danger perception speed of drivers in each type of accident (2020)

30 km/h or less is extremely
effective against fatal and serious injury accidents of pedestrians.

2ITARDA 24th Work Shop Report2-2. Driver human factors
According to the ratio of human factors in accidents of pedestrians who cross the crosswalk caused by vehicle
turning right, "delay in noticing", which is a sum of failure to confirm safety factors and failure to pay attention
forward, accounts for 97.5%, larger than 90.0% as the ratio for the entire pedestrian-vehicle accidents. (Fig.2 Redframed part) This shows that the ratio of accidents that could have been prevented if the driver had found thepedestrians is larger.
Particularly, the ratio

Misjudgments, etc.
2.1%

of failure to confirm
safetyaccounts

81.9%

and

56.6%

respectively,

which

Failure to pay
attention
forward15.6%

forMisjudgments,
etc. 8.0%

Inappropriate
operation 0.3%
Delay innoticing
97.5%

Delay innoticing
90.0%

Failure to confirm
safety factors
81.9%

means the ratio in
accidents caused by

Failure topayattention
forward
33.4%

Inappropriate
operation
1.8%

Failure toconfirm safety
factors
56.6%

Pedestrian-vehicle accidents (N=6952)

vehicle turning right islarger than that of theentire

pedestrian-vehicleaccidents.

Pedestrians who cross the crosswalk
while the vehicle turns right (N=1390)

Fig.2 Ratio of the number of fatalities and seriously injured persons
according to human factors of drivers in each type of accident (2020)(Fig.2 Blue part) Thissuggests that there are many cases where the driver had an intention of confirming safety when turning right at theintersection but the confirmation was insufficient, resulting in accidents.
2-3. Age groups of pedestrians
When we focus on age group next, the ratio of persons aged 65 years old or older accounts for 63.2% in accidentsof pedestrians who cross the crosswalk caused by vehicle turning right, which is higher than 60.1% as the ratio of
the entire pedestrian-vehicle accidents. As indicated in 2-1, accidents caused by vehicle turning right occurred
relatively at a low speed range of 30 km/h or less. Therefore, it can be considered that the ratio of persons aged 65
years old or older becomes high because their impact resistance is generally low.
2-4. Policy of countermeasures
Based on the three features mentioned above, as the policy of countermeasures, we consider the following cases
effective: [1] a vehicle turning right at 30 km/h or less at the intersection [2] detects that a pedestrian is on thecrosswalk (i.e., prevents oversight) and [3] stops to prevent it contacting with the pedestrian.
We will advance this study by focusing on the prevention of accidents caused by vehicle turning right atintersections by the Autonomous Emergency Braking System (AEB) for pedestrians, which is considered to meetsuch requirements.

3. Study of the the field of view (FOV) of AEB using micro data
3-1. Development of study items
In this section, using micro data, we aim to specify the FOV required for the operation of AEB in accidents caused

3

ITARDA 24th Work Shop Reportby vehicle turning right. Also, after Chapter 4, we define the travelling directions of crossing pedestrians as the samedirection (when the direction of a vehicle turning right is same as the crossing direction of pedestrian ) and theopposite direction (when the direction of a vehicle turning right is opposite to the crossing direction of pedestrian ),and study the cases by classifying them into two categories.
3-2. Specify the FOV using micro data
(1) Preconditions for accident reconstruction
In this study, we assume that a vehicle equipped with AEB for pedestrians is running at intersection andthere is no oncoming vehicle. Also, we reconstructed accidents using micro data under the following conditions.
Table 1 Preconditions for accident reconstruction
Speed ofUnder 65 years old

65 years old or older

Joggingpedestrians

5 km/h(1.39 m/s)

3.5 km/h(0.97 m/s)6 km/h(1.68 m/s)

Vehicle speed

Assume that a vehicle runs at danger perception speed at intersection
・Casualty accidents that occurred from 2009 to 2020 are targeted (opposite
direction: 7 cases, the same direction: 13 cases)
・The position of starting turning right is identified from the travel track of vehicle

Othersand defined as the point for entering the intersection.
・There are no other crossing people and obstacles which can become noise during
detection.
・Even if FOV becomes large, the performance of AEB sensor does not change.

Casualty accidents are used as accident data to secure a certain number of data. Also, the extracted data include
T-junction and five-street intersection and cover various intersections from the perspective of the number of lanes.

(2) Method of specifying FOV
The procedure for specifying FOV using micro data is as follows and the example is shown in Fig.3.
[1] Specify the collision site, estimate vehicle travel track and pedestrians travel track, and create their models.
In such case, define the coordinate system by setting the travelling direction of pedestrian as X axis and thecollision site as X = 0.
[2] Calculate the sum of the AEB's detection reaction time and braking time at danger perception speed as thetotal braking time, and identify the position of vehicle and pedestrian respectively backward the total braking
time from the collision site. In such case, we assume that AEB’s detection reaction time is 1.0 second withthe braking condition increasing to the maximum deceleration of 0.65G in 1.7 seconds and maintaining 0.65G
afterwards.
[3] Specify the FOV by which sensor can detect pedestrians. In such case, we assume that the AEB sensor isinstalled at the center of the front end and the detection range is 1 m forward or backward of the pedestrianby considering steps of pedestrian. Two examples in which vehicles did not start turning right at the timing
4

ITARDA 24th Work Shop Reportspecified in [2] were excluded from the study.

5Pedestrian
travel track

Position ofpedestrian at thetime of detection
15200-505

10-5

Vehicle travel
track

FOV-10Braking staring
point-15-20

Fig.3 Accident example and schematic drawing that specified FOV

4. Study results and consideration

direction of pedestrians. As a result, a collision
can be avoided when the FOV is from 40° at theminimum to 70°at the maximum in the case ofopposite direction and when the FOV is from60°at the minimum to 120°at the maximum in

20

100%

1575%

1050%

525%

00%
405060

Opposite

7090

100 110 120

FOV (Degree)
Same
Unavoidable

対向direction 同方向
direction

the case of the same direction. (Fig.4).

80

Cumulative percentages

The FOV varies according to travellingNumber of accident avoidance

4-1. FOV at each travelling direction of pedestrians

回避不能

Inapplicable
対象外

Fig.4 The number of accident avoidance andcumulative percentages of FOV(by travelling direction of pedestrians）

4-2. Study of the difference of FOV

We used the examples where the FOV became the maximum and the minimum according to travelling directionof pedestrians and studied the factors for difference between them.

5

ITARDA 24th Work Shop Report(1) Examples for pedestrians crossing in the opposite direction
In the cases of pedestrians crossing in the opposite direction, examples that the FOV is the minimum (40°)
and the maximum (70°) are shown in Fig.5. The difference between the minimum FOV and the maximum
FOV resulted from a difference ofthe number of lanes of the roads
where the vehicle was running

5500-505101520-50

before turning right. Whereas the-5-5

number of opposite traffic lanes-10-10before the vehicle turned right was-15-15

two in the example of theone in the example of

101520-20-20

minimum FOV, the number was

5A. Example of FOV 40°

theB. Example of FOV 70°

Fig.5 Example of the minimum and maximum FOV when thepedestrian is crossing in the opposite direction

maximum FOV. Therefore, it canbe considered that, in the example

that the FOV became the maximum, because the radius of rotation at the time of turning right becomes small,the direction of the vehicle at the time of detection (tangential direction) was not directed toward crosswalk,which made the FOV large.

(2) Examples of pedestrians crossing in the same direction
In the cases of pedestrians crossing in the same direction, examples that the FOV is the minimum (60°) andthe maximum (120°) are shown in Fig.6. The main factor for the difference between the minimum FOV andthe maximum FOV is vehicle speed. The danger perception speed was 20 km/h in the example of the minimum
FOV and 30 km/h in the example of the maximum FOV. If the speed is faster, the position where detection isrequired becomes farther from the collision site due to the relation with braking time. Therefore, the direction
5of the vehicle at the time ofdetection (tangential direction) is
not directed toward crosswalk,

50-50510-515

200-50510which made the FOV large. Also,-10-10

FOV became the minimum, we-15-15

investigated a case where the-20-20vehicle speed is increased to 30C. Example of FOV 60°

D.Example of FOV 60°

using the example C where thekm/h.
The results show that with an

1520-5Fig.6 Example of the minimum and maximum FOV when thepedestrian is crossing in the same direction

increase in speed, the FOV became 70°. Therefore, it can be considered that if the vehicle speed is increased,
the FOV also increases.

6ITARDA 24th Work Shop Report

4-3. When the vehicle speed is assumed at 30 km/h uniformly for all examples
The macro data show that accidents in which the vehicle speed was 30 km/h or less accounts for 98% and theFOV becomes large if the vehicle speed becomes faster. Therefore, we studied the condition where the vehicle speed
Insuchcondition, 19 of 20 cases
can be covered with theFOV 120° (Fig.7). Also, thecircled example was one of
the examples excluded fromthe study in 3-2 (2). The
reason why the relevant
example was excluded from120120804000123

Number of opposite traffic lanes before thevehicle turned rightPedestrians' travelling direction: Same4Detection angle (degree)

examples.

Detection angle (degree)

is assumed at 30 km/h in all

80
4000123

4

Number of opposite traffic lanes before thevehicle turned rightPedestrians' travelling direction: Opposite

Fig.7 Relation of the number of opposite traffic lanes before vehicles turn right and FOV
when the vehicle speed is set uniformly at 30 km/h

the study is that the vehicle did not start turning right at the timing of detection because it was running at 40 km/h.
However, when the vehicle speed is decreased to 30 km/h, the detecting position changes to a position after thevehicle turns right.

5. Summary
5-1. Obtained knowledge(1) Based on macro data, we analyzed the features of traffic accidents of pedestrians while crossing crosswalk
caused by vehicles turning right, which resulted in many numbers of fatalities and seriously injured persons.
The results of the analysis show that [1] 98% of the accidents occurred when the danger perception speed
was 30 km/h or less, [2] 98% of human factors were delay in noticing and particularly the failure to confirm
safety factors (although the driver had intention of confirming safety factors but the confirmation wasinsufficient) accounted for 82%, [3] 65 years or older accounted for the largest ratio of 63% among
pedestrians surveyed.
(2) Based on the results in (1), we studied accident avoidance by AEB for pedestrians and specified the FOV
according to travelling direction of pedestrians using actual examples based on micro data. Micro data in 19of 20 cases can be covered with the FOV 120°of a sensor that corresponds to pedestrians.
5-2. Future main tasks(1) To prevent malfunction of AEB for pedestrians, develop AEB that does not operate when the vehicle is goingstraight through coordination with car navigation systems and obtaining information by road-vehiclecommunication for the purpose of predicting the course for turning right from the point when the vehicle
starts turning right(2) Create algorithm that can corresponds to conditions where there are two or more crossing pedestrians(3) Enhance the performance of sensors for securing accuracy required even if the FOV is widened.

7