Assessment of the Detectability of Vulnerable Road Users:

An Empirical Study

Wentong Yang

, Shota Matsubayashi

, Kazuhisa Miwa

, Shinya Kitayama

, Manabu Otsuka

and Koji Hamada

Graduate School of Informatics, Nagoya University, Nagoya, 4648601, Japan

Institutes of Innovation for Future Society (InFuS), Nagoya University, Nagoya, 4648601, Japan

Advanced Research and Innovation Center, DENSO Corporation, Aichi, 4700111, Japan

Keywords: Traffic Safety, Advanced Driver Assistance System, Driver Cognition, Collision Risk, Vulnerable Road Users.

Abstract: This study analysed the detectability of vulnerable road users (VRUs) as a function of time to the closest point

of approach. We defined four patterns: Gradual Increasing Pattern and High Detectability Pattern as the safe

situation, Immediate Increasing Pattern and Low Detectability Pattern as the dangerous situation and

investigated empirically drivers’ detection patterns. The results showed that motorbikes in the same direction

showed the dangerous pattern, and pedestrians in the same direction also showed the dangerous patterns but

influenced by the distance of closest point of approach. Bicycles in the same direction showed higher

detectability due to their positioning in the driver's field of view for longer time. For the VRUs in the opposite

direction, and those in the left and right direction, participants also showed high detectability. The results give

implications for advanced driver assistance systems (ADAS) design.

1 INTRODUCTION

1.1 Traffic Accidents at Intersections

Urban safety is a pressing issue, and traffic accidents

are one of its major concerns. Intersections have been

identified as accident hotspots, and over half of all

accidents occurred at or near them (Japanese police

department, 2022). Drivers must be alert to the

movement of vehicles, pedestrians, bicycles, and

motorbikes from multiple directions. Hills (1980)

found that driving at intersections requires large

amounts of visual and cognitive resources to deal

with high-density visual stimuli. As accidents at

intersections occur frequently, and the resulting

cognitive challenges are significant, it is imperative

to promptly implement traffic safety measures.

One potential solution to the issue is to rapidly

introduce advanced driver assistance systems

(ADAS), which utilize numerous sensors and

cameras to scan the surrounding environment, and

which provide alerts and notifications to the driver.

However, drivers may not always be receptive to

these notifications. According to Lee et al. (2002),

early alerts may be seen as bothersome, especially for

vigilant drivers (Lee et al., 2002). Naujoks et al.

(2016) revealed that warnings that urge drivers to

respond quickly in urgent scenarios can be perceived

as irritating. Therefore, it is crucial for ADAS to work

effectively and match the attention levels of drivers.

Vulnerable Road Users (VRUs) refers to non-

motorised individuals, including pedestrians, cyclists,

motorbikes, and persons with disabilities. This study

examines scenarios in which a moving vehicle

approaches VRUs at intersections.

Intersections are significant meeting points for

VRUs. Tay (2015) observes that the high complexity

of traffic flow at intersections poses greater risks to

VRUs. Furthermore, Cantin et al. (2009) found that

driver’s distractible attention at intersections

threatens VRUs safety. Furthermore, Werneke and

Vollrath (2012) found that driver distraction was the

primary cause of accidents involving VRUs and

vehicles. Driver distraction could lead to accidents as

VRUs may not be detected successfully. Although

there have been numerous studies on the detection of

VRUs by drivers, most of them were mainly

concerned with the detection characteristics of single

VRUs. In this study, however, we developed a

framework in which the detection characteristics of

many types of VRUs can be examined based on a

unified format.

508

Yang, W., Matsubayashi, S., Miwa, K., Kitayama, S., Otsuka, M. and Hamada, K.

Assessment of the Detectability of Vulnerable Road Users: An Empirical Study.

DOI: 10.5220/0012451300003660

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

In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) - Volume 1: GRAPP, HUCAPP

and IVAPP, pages 508-515

ISBN: 978-989-758-679-8; ISSN: 2184-4321

1.2 Objectives

This research aims to examine how detectability of

VRUs is influenced by an increase in collision risk.

Ascertaining the relationship between collision risk

and VRUs detectability is crucial to enhance road

safety for both drivers and other road users. When

collision risk is high, drivers must detect it with

concentration. Yoshitake et al. (2020) revealed that

drivers direct their visual attention towards the

surrounding of the intersection and detect pedestrians

early enhances the likelihood of avoiding a collision

when turning right at intersections. Such research

emphasises the importance of recognising VRUs in

high-risk collision situations. Further investigation is

necessary to examine the effect of collision risk on

detectability of VRUs. Understanding the

relationship between such variability and

detectability is crucial to improve traffic safety

measures.

1.3 Relationship Between Detectability

and Collision Risk

This study quantitatively measured detectability

using d' based on the signal detection theory. A higher

value of d' indicates better ability to distinguish target

from noise. For the calculation of d', the current study

used fuzzy signal detection theory (Parasuraman et

al., 2000).

Fuzzy signal detection theory modifies the

outcomes of hit, miss, false alarm, and correct

rejection in signal detection theory by distributing

each stimulus-response combination into categories

where they can be partially assigned to multiple

outcomes. In each trial, participants determine the

presence of a VRUs by providing a score of 0-1. Hit

rate, false alarm rate, miss rate, and correct rejection

rate are employed to calculate d', following the

procedure of traditional signal detection theory. The

formula for d' is as (1):

d' = Z (Hit rate) – Z (False Alarm rate) (1)

On the other hand, collision risk is calculated

based on t, until the driver and VRUs approach their

closest point of approach to each other. A shorter t

duration leads to a higher collision risk.

In the realm of shipping, objective index of

potential collisions has been developed, one of which

is the TCPA (Time to Closest Point of Approach) and

DCPA (Distance of Closest Point of Approach)

indicators (Chauvin and Lardjane, 2008).

TCPA corresponds to the time t to the reapproach

point in this study. In this study, two moderator

variables are assumed.

DCPA is considered the first moderator of this

function. Along with DCPA, the degree of traffic

congestion in the driving environment is considered

as another moderator. Bao and Boyle (2009) found

that high levels of traffic congestion led to an increase

in leftward visual scanning when approaching an

intersection to detect VRUs. Hence, these studies

suggest that the traffic congestion affects detectability

of VRUs.

This study analyses the change in detectability of

VRUs with increasing crash risk when drivers turn

right and left at intersections. We utilise the

detectability index d' as the dependent variable, while

the independent variable is the time t until the driver's

vehicle reaches the closest point of approach to

VRUs. In order to reveal these relationships, we

consider two moderators, DCPA and traffic

congestion. Our study formulates a classification of

functions that predict detectability during right and

left turns. Understanding this function is crucial for

the development of ADAS.

2 TYPOLOGIES OF

DETECTTABILITY TYPES

In this study, d' is viewed as a function of the time t

taken to reach the closest point of approach.

Correspondingly, we hypothetically classify four

patterns for this function.

Gradual Increasing Pattern

Figure 1: Gradual Increasing Pattern.

Figure 1 indicates the Gradual Increasing Pattern. As

the collision risk increases, d' increases gradually.

This suggests that detectability increases as the driver

approaches the VRUs. Since such an increase in

detectability is expected to contribute to avoidance of

traffic accidents and risk reduction, this pattern is

judged to be relatively safe.

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Immediate Increasing Pattern

Figure 2: Immediate Increasing Pattern.

Figure 2 indicates the Immediate Increasing Pattern.

d' maintains at a low level and increases immediately

just before approaching VRUs. This pattern indicates

that the driver has not detected the VRUs

immediately before. It is considered a dangerous

pattern.

High Detectability Pattern

Figure 3: High Detectability Pattern.

Figure 3 indicates the High Detectability Pattern. The

driver maintains a consistently high d' regardless of t.

This suggests that the driver maintains persistently

high detectability. Therefore, in such situations, this

pattern is judged to be relatively safe.

Low Detectability Pattern

Figure 4: Low Detectability Pattern.

Figure 4 indicates the Low Detectability Pattern. The

driver maintains a consistently low d' regardless of t.

This suggests that the driver does not detect the

VRUs. Such a pattern is judged to be the most

dangerous because it is difficult to avoid accidents.

And this pattern may pose a significant risk to other

traffic participants.

3 EXPERIMENT

This study used driving videos to examine how

detectability of VRUs changes with increasing

collision risk.

3.1 Participants

A cloud service enrolled 320 participants (166 males,

153 females and 1 other), with a mean age of 41.9

years (SD = 8.71). 287 participants held a valid

driver's licence, and the average duration since the

licence was obtained was 18.1 years. fees were

provided to each participant.

3.2 Driving Video

The driving videos were produced using the Unity

software for 3D graphics by the following five

factors: evaluation targets, time to the closest point

(t), DCPA, traffic condition, and presence/absence of

the target.

To calculate the necessary false alarm rate for d'

calculation, additional videos were produced without

the evaluation target in each condition. As a result, a

total of 320 (10 x 4 x 2 x 2 x 2) operational videos

were prepared.

Evaluation Targets

Ten evaluation targets were adopted (see Figure 6).

When turning left，the evaluation targets included

pedestrians in opposite direction, pedestrians in same

direction, motorbikes in same direction, bicycles in

same direction and bicycle in left direction. When

turning right, the evaluation targets included

pedestrians in opposite direction, pedestrians in same

direction, motorbikes in opposite direction, bicycles

in opposite directions, and bicycles in right direction.

Speeds for pedestrians, bicycles, and motorbikes

were set to 3.6 km/h, 10.8 km/h, and 32.4 km/h,

respectively.

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Figure 5: An example screenshot of observing phase. Text information was displayed at the bottom of the screen during the

video playback, signalling whether the turn was right or left.

Figure 6: 10 types of evaluation targets.

Time to Closest Point (t)

The time t was selected as an independent variable to

be 0s, 3s, 6s, and 9s before reaching the closest point

of approach.

DCPA

DCPA is the distance to evaluation targets when

drivers reached the closest point of approach. It

served as the first moderator with conditions set at 1

m and 5 m.

Traffic Congestion

Traffic congestion, as the second moderator, was

established in two conditions, quiet condition, and

crowded condition. This was achieved by

manipulating the number of traffic participants.

3.3 Procedures

The study comprised of two phases, the observing

phase, and the test phase. During the observing phase,

participants were instructed to watch a video from the

perspective of an actual driver. The video stopped at

seconds before reaching the closest point of approach.

For instance, if t is 3 seconds, the video paused when

there were 3 seconds remaining until the closest point

of approach. Figure 5 shows an example screenshot

of observing phase.

Subsequently, the test phase began. Participants

rated the detectability against the evaluation target as

it appeared in driving video. The evaluation target

image was positioned on the right side of the display

screen. On the left-hand side of the screen, an aerial

view displayed the positions of the participant's

vehicle and the evaluation target at the time of video

cessation. The blue rectangle denoted the participant's

vehicle, while the red circle marked the location of

the evaluation target. Figure 7 shows an example

screenshot of test phase. Participants were presented

with the question, "When the video stopped, was the

target shown on the right-hand side of the screen at

the point indicated by the red circle?" To respond,

participants used a slider bar. This rating scale

presented 'definitely not' on the left end and 'definitely

was' on the right end. Participants moved the slider to

indicate their level of confidence in their perception.

Figure 7: An example screenshot of test phase.

At the outset of the study, the participants were

randomly allocated into four distinct groups. Each

group was shown 80 videos from the total of 320. The

rationale behind dividing the participants into four

groups was to ensure that any of them rated only one

target, for instance, a motorbike approaching from the

opposite direction while turning right. Each of the

four groups received a video displaying a single

evaluation target approaching the closest point of

approach at one of four time-intervals, 0s, 3s, 6s or

9s.

Before the main task, participants performed four

practice trials. During the main task, 80 stimuli were

Assessment of the Detectability of Vulnerable Road Users: An Empirical Study

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presented in a randomised order. A rest was set

following every 20 trials. Upon completion of the

main task, participants were asked to provide

demographic variables. The study parameters

encompassed age, gender, driving history, and the

duration since the individual acquired their driving

license.

4 RESULTS

The results of the analysis, with the time t to the

closest point of approach as the independent variable

and d' as the dependent variable, are shown in the

figure (see Figure 8-17). Note that the moderator is

shown as a legend.

Gradual Increasing Pattern included motorbikes

in opposite direction when drivers turn left,

pedestrians in opposite direction when drivers turn

right and left, bicycles in opposite direction when

drivers turn right and bicycles in right and left

directions when drivers turn right and left. And High

Detectability Pattern included bicycles in same

direction when drivers turn left. These patterns

suggest that as the collision risk increases, the

detectability of the evaluated target also increases,

indicating a safer situation.

Low Detectability Pattern included pedestrians in

same direction in the DCPA 5m condition when

drivers turn right and left. And Immediate Increasing

Pattern included pedestrians in same direction in the

DCPA 1m condition when drivers turn right and left,

and motorbikes in same direction when drivers turn

left. These patterns suggest a hazardous situation

where there is low detectability over an extended

period, despite increased collision risk.

As mentioned before, when assessing pedestrians

in same direction, DCPA significantly adjusted the

patterns as a moderator, there was the Low

Detectability Pattern in DCPA 5m condition, and the

Immediate increasing Pattern in DCPA 1 m

condition. Both patterns have maintained low

detectability over the long term and are thereby

dangerous.

On the contrary, none of the assessment targets

were significantly affected by traffic congestion as a

moderator.

Figure 8: Patterns of motorbikes in opposite direction when

drivers turn right.

Figure 9: Patterns of motorbikes in same direction when

drivers turn left.

Figure 10: Patterns of bicycles in opposite direction when

drivers turn right.

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Figure 11: Patterns of bicycles in same direction when

drivers turn left.

Figure 12: Patterns of bicycles in right direction when

drivers turn right.

Figure 13: Patterns of bicycles in left direction when drivers

turn left.

Figure 14: Patterns of pedestrians in opposite direction

when drivers turn right.

Figure 15: Patterns of pedestrians in opposite direction

when drivers turn left.

Figure 16: Patterns of pedestrians in same direction when

drivers turn right.

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Figure 17: Patterns of pedestrians in same direction when

drivers turn left.

5 DISCUSSION AND

CONCLUSIONS

In this study, the detectability index d' is considered

as a function of the time t, and two dangerous patterns

are defined: the first is the Immediate Increase

Pattern. The second is the Low Detectability Pattern.

In these patterns, d' remains low regardless of t.

Consistently, the Immediate Increasing Pattern

was shown for the motorbikes in same direction when

turning left.

For the pedestrians in same direction, the effect of

the time t was adjusted by DCPA. For the pedestrians

in same direction, the pattern was the Immediate

Increasing Pattern when the DCPA was 1m. And

when the DCPA was 5m, the pattern was the Low

detectability pattern. In any case, these patterns

indicate a dangerous situation. The Low Detectability

Pattern when DCPA was 5m is particularly

dangerous.

As described above, the detectability of VRUs in

same direction decreased, whether they were turning

left or right. These VRUs approach driver from

behind. They had a shorter time in the driver's field of

view than the VRUs approaching from the front. So,

they were therefore considered more difficult to

detect.

Low visibility is also considered a reason for lack

of detectability. Low visibility is one of the most

important factors in accidents (Yousif et al., 2020). In

the present experiment, the motorbikes and pedestrian

in same direction were only visible in the side and

rear-view mirrors for a long time. So, the drivers

maintained a low detectability of such VRUs.

For the pedestrians in same direction, with a

DCPA of 5 m, the pedestrian was only visible in the

side and rear-view mirrors until the video stopped.

With a DCPA of 1 m, the pedestrian appeared next to

the car at the end of the video, which means that

visibility was higher and therefore detectability was

higher.

It is interesting to note that even in same direction,

the detectability was higher for bicycle. The bicycle

in same direction is always in the driver's front left

field of view. Driver passes the bicycle once and the

bicycle approaches again from behind. The long time

spent parallel to the bicycles is thought to be one of

the reasons for the high detectability. In addition, the

driver's experience with bicycles may also play a role

in the detectability of bicycles. A study by Kaya et al.

(2021) showed that drivers with more experience with

bicycles may have greater visual attention related to

the detection of bicycles while driving. The

penetration rate of bicycles in Japan is about 0.5 per

person, a relatively high proportion, so the

detectability of bicycles while driving is likely to be

higher.

The VRUs in opposite direction, and those in the

left and right direction, both showed the Gradual

Increase Pattern, indicating that the participants in

this experiment performed adequate detection.

In addition, the traffic congestion was

manipulated in this experiment, but the results

showed no effect of adjusting the traffic congestion in

any of the situations.

ADAS notifies the driver of warnings in

hazardous situations. This notification must be made

at the required time. Notification of such warnings in

relation to objects for which adequate attention has

been paid may encourage an inappropriate allocation

of cognitive resources to the driver.

Particularly in recent years, there has been a

proliferation of devices installed in cars, and it has

been suggested that the excessive information

displayed by these devices puts pressure on the

driver's cognitive resources. For example, early

warnings may be perceived by drivers as annoying

warnings (Lee et al., 2002), and it has been suggested

that warnings that prompt a quick response in

emergency situations may also be perceived as

unpleasant by drivers (Naujoks et al., 2016).

To address these issues, this study is expected to

provide important guidance for the design of ADAS.

Finally, the limitations of the present study and

future challenges are discussed. In this experiment,

the situation of bicycles in opposite direction when

turning left and motorbikes in same direction when

turning right was not included. This is because such

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traffic is illegal under Japanese traffic law. In the

same way, there are many violations of the law in the

real scene. Discussion on the detectability of VRUs

that violate the law is an issue for the future.

Furthermore, in this study, experiments were

conducted by using driving videos and having the

participants observe them. In future research, it will

be necessary to follow up this experiment by

conducting experiments in more realistic

experimental environments such as VR and driving

simulation.

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