倉田奨励金研究報告書 第 51 集｜2019 年度（第 51 回）助成
自然科学・工学研究部門：1 年助成■受領№1397

歩行者の安全・安心と交通円滑性を満たす自動運転車両の挙動要件に関する研究
代表研究者井料美帆

名古屋大学大学院環境学研究科准教授

A study on the requirements of maneuver of automated vehicles to satisfy
pedestrians’ safety and traffic efficiency
Principal Researcher

Miho Iryo,
Graduate School of Environmental Studies, Nagoya University, Associate Professor

自動運転車両(AV)の本格的導入には，交通円滑性への影響を最小限にしつつ歩行者の安全性を担保することが重要である．本研究では単路部無信号横断歩道を対象に，ルールベースと強化学習を組み合わせたハイブリッド車両挙動モデルを提案し，遅れの増加を抑制しつつ，歩行者の予測困難な行動にも対応できることを示した．またバーチャルリアリティ歩行実験により，AV の危険行動が歩行者の危険な横断行動を誘発しうる危険性を示した．
For the implementation of automated vehicles, it is important to ensure the safety of pedestrians
without having significant problems in terms of traffic efficiency. In this study, a hybrid model of vehicle
maneuver was proposed by combining a rule-based model and a model with reinforcement learning, atunsignalized midblock crossings. It was shown that the model successfully reacted to unexpected
maneuver of pedestrians with reducing vehicle delay. The possibility of risky pedestrian behavior triggeredby hazardous AV maneuver was also shown via virtual-reality experiment.

1．研究内容understand pedestrian maneuver against AVs,

1.1 Background and objective

since AVs need to make their motion plan taking

In Japan, 70% of pedestrian-vehicle fatalities
occur when pedestrians are crossing streets

into account the unexpected but possible reactionsof pedestrians to them.

(National Police Agency, 2020). The unsignalized

The aim of this research is to investigate themid-block crosswalks (UMCs) are associated withsettings of autonomous vehicles which can satisfy

safety risks for pedestrians. Meanwhile, theboth safety and efficiency. For that, we haveemergence of Autonomous Vehicles (AVs) could

proposed a novel agent-based framework of traffic

provoke unexpected challenges in the urban traffic

simulation as well as a machine-learning algorithm

environment, particularly on UMCs, whereof AVs. Furthermore, a virtual-reality experimentpedestrians are exposed to the AV flow. It is

was conducted to understand the reaction of

possible to apply discreet maneuver of AVs topedestrians toward such vehicles.

ensure safety, while it may have great impact onefficiency of traffic. Additionally, it is important to倉田奨励金研究報告書 第 51 集｜2019 年度（第 51 回）助成
自然科学・工学研究部門：1 年助成

The conflict zone is the area enclosed by thexextension lines corresponding to the boundaries of
the approaching vehicle and the pedestrian. The

Far-side sidewalk

blue areas are the waiting areas representing theWaiting area
(xv, yv)
Conflictzonelp(xp, yp)

wp

place where pedestrians usually stand and waitlv

before crossing. Both pedestrians and vehicles
w

have visual ranges defined as a circle with theNear-side sidewalk

radius of sight distance. All visual obstacles andtarget objects are assumed to be rectangularshapes, and pedestrians and drivers can only0Figure 1. Two-dimensional coordinate system
at a UMCR on a two-lane street

detect obstacles within their visual ranges an in
Figure 2.
Pedestrian agents have perception, decision andwalking behavior modules. In the decision models
they judge whether they can complete crossing thePm
Cj

Bj

Dj

Aj

conflict zone by keeping safety margin, forecasting
behavior of approaching vehicles. Vehicle agentsOi,6Oi,5Oi,3Oi,7Oi,8Oi,4Oi,2Oi,1do the same, while there are 2 types of drivers
assumed: 1 obeys the pedestrians’ priority rule andthe other try to compete with pedestrians over the(a) Pedestrians’ vision fieldright of way.
The simulation was validated using the empirical
data videotaped in Inabachi-cho, Nagoya. Most of theparameters including reaction time of vehicles andpedestrians and deceleration rate are explicitly given
from literature without tuning, the accuracy of themodel in terms of Post Encroachment Time (PET),which is the surrogate safety measure, was considerably(b) Vehicles’ vision field

acceptable.

Figure 2. Example visual field calculation inreal scenarios

1.2.2 Machine-learning based optimization for
efficiency and safety

1.2 Agent-based traffic simulation for evaluation ofdetailed decision-making procedure
1.2.1 Model framework

Motion planning model was proposed considering
the worst condition that pedestrians do not pay
attention at all to drivers. Two new models are

The simulation framework is illustrated byestablished for AVs: a rule-based model that solves

taking a UMC with refuge islands (UMCRs) as an

motion plans through a fixed calculation procedure

example as shown in Figure 1. The crosswalk isincorporating several optimization models, and aconsidered to be located within a two-dimensional

learning-based model that replaces the deterministic

coordinate system. Pedestrians are constrained tooptimization

move vertically (y-direction), while the vehicles

reinforcement learning. In addition, a hybrid model

are constrained to move horizontally (x-direction).

was proposed as the third model in which rule-based

processwith

policy-gradient倉田奨励金研究報告書 第 51 集｜2019 年度（第 51 回）助成
自然科学・工学研究部門：1 年助成

acceleration value is introduced regularize the action(Scenario 1) and those who are distracted andspace of the proposed learning-based model.

greatly change their speed while crossing (ScenarioThe developed models were assessed through2). The learning-based model has outstanding

simulation experiments in which pedestrian speed

safety performance, whereas the rule-based model

profiles were defined using empirical data fromleads to remarkable safety problems. For distracted

field surveys. Figure 3 shows PET distributionpedestrians with significant crossing-speed changes,

when vehicles face pedestrians with normal speed

rule-based AVs lead to a 5.1% probability ofserious conflict and a 1.4% crash probability.
Figure 4 investigates the delay of a single vehicle
with different models. The vehicle volume is fixed
as low as 50 veh/h for a given scenario in order toavoid the effect of vehicle accumulation on theexperimental results. Settings of all pedestrians arethose of Scenario 1. The rule-based model
provides the least delay while the larger delayoccurs even without pedestrians in learning-basedmodel. The hybrid model takes advantages of both(a) Scenario 1 (benchmark)models, which successfully avoid short PETs andthe delay do not unnecessary increase as thelearning-based model.
1.3 Virtual Reality experiment for pedestrian’ reaction
1.3.1 Settings
In the traffic simulation of the previous section,pedestrian

motionwaseitherassumedorgenerated based on the observed pedestrian
profiles who face ordinary vehicles. However,pedestrians may react to AVs differently if the(b) Scenario 2 (Distraction + great speeddifference)
Figure 3. PET distribution

maneuver of AVs are different from those ofordinary human-driven vehicles. Therefore, avirtual-reality (VR) experiment was conducted tounderstand pedestrian behavior reacting to various
types of approaching vehicle maneuver.

(a) VR view
Figure 4. Average delay per vehicle(b) VR participant

Figure 5. VR environment倉田奨励金研究報告書 第 51 集｜2019 年度（第 51 回）助成
自然科学・工学研究部門：1 年助成

crosswalk. The first few vehicles passes the100%
90%
80%
70%
60%
50%
40%30%
20%
10%0%crosswalk without deceleration and then theMild braking

subject vehicle approaches with time gap of 3

Harsh braking

seconds. The subject vehicle will take either of theDangerous
harsh braking

deceleration patterns.
1.3.2 Results and findings

02

4

68Starting time (s)1012Figure 6. shows the distribution of the time
when pedestrians start crossing after the last(a) 40km/h

vehicle passes. Some participants facing dangerous
harsh braking start earlier before the subject

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Mild brakingvehicle arrives. In a meantime, pedestrians facing

Harsh braking

harsh braking or mild braking carefully wait untilthe vehicles almost complete stopping. Since theDangerous harshbraking

dangerous harsh braking correspond to vehicles
with the rule-based model which failed to stop in
Section 1.2, it implies that the hazardous

02

468Starting time (s)1012maneuver of rule-based model also triggers thehazardous maneuver of pedestrians.

(b) 60km/h
Figure 6. Distribution of pedestrian crossing
start time

1.4 Conclusions
Thisresearchintroduced

anagent-based

Forum 8 UC/Win-Road (Version 15.1) was

pedestrian-vehicle interaction model for evaluating

utilized for building the VR environment andsafety at unsignalized midblock crosswalks. Thesimulation.

head-mounted

hybrid motion planning model of rule-based anddisplay (HTC VIVE Pro) and were asked to walklearning-based models improved both surrogate

through a room (Figure 5). In the experiment, asafety measures and vehicle delays by adjusting

two-way two-lane road is developed in the VR andapproaching speed profiles to the crosswalk, while

the participant walk to cross an unsignalized

thecrosswalk. 3 deceleration patterns of approachingconditions that may have collisions. Furthermore,

vehicles are applied; mild braking, harsh braking

the VR experiment also revealed that the hazardousand dangerous harsh braking. Mild braking is the

maneuver similar to the failure patterns of thecase that vehicles start deceleration early enoughrule-based model can cause the hazardous crossing

to safety stop before the crosswalk. Harsh braking

maneuver of pedestrians.

Participantswore

rule-basedmodelmayhave

hazardous

is that vehicles apply maximum deceleration rate

In this study, the pedestrian maneuver isand can stop in front of the crosswalk. Harshanalyzed in the limited number of conditions indangerous braking also decelerates in maximum

the VR experiment. As future works, it isdeceleration rate but cannot complete stopping

necessary to analyze various other patterns,

before reaching the crosswalk. Initial speed ofespecially considering the variation of crosswalk

approaching vehicles are set as either 40 km/h andgeometries

60 km/h.

systems

Participants start walking from 2m behind thecrossings.

forandotherpedestrians

informationtoprovisionencouragesafe倉田奨励金研究報告書 第 51 集｜2019 年度（第 51 回）助成
自然科学・工学研究部門：1 年助成

２．発表（研究成果の発表）
Zhu, H., Han, T., Alhajyaseen, W.K.M., Iryo-Asano, M.
and Nakamura, H.: Can Automated Driving Prevent
Crashes with Distracted Pedestrians? An Explorationof Motion Planning at Unsignalized Mid-block
Crosswalks, Accident Analysis & Prevention, Vol.173,106711, 2022.
Zhu, H., Iryo-Asano, M., Alhajyaseen, W.K.M.,
Nakamura, H. and Dias, C.: Interactions betweenAutonomous Vehicles and Pedestrians at Unsignalized
Mid-block Crosswalks Considering Occlusions by
Opposing Vehicles, Accident Analysis & Prevention,
Vol. 163, 106468, 2021.