spatial-temporal patterns and pedestrian simulation

Spatial-temporal patterns and pedestriansimulation

By Nan Hu*, Suiping Zhou, Zhongke Wu, Mingquan Zhouand Benjamin Eng Keong Cho* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

In this paper, we propose a framework for modeling lower-level pedestrian navigational

behaviors. We aim not only to generate realistic simulation results but also to make

our framework flexible and extendible, and easy to use for model developers. A

divide-and-conquer methodology is first adopted to divide the complex navigational

behaviors into three levels, which allows us to focus on the intermediate level. We then

propose a pattern-based framework for modeling pedestrian navigational behavior at this

level. In our framework, spatial-temporal patterns are used to represent the situational

perception, and a pattern-matching mechanism is proposed to model the navigational

choices of individual pedestrians. To demonstrate the effectiveness of our framework, a

computational model is constructed to simulate pedestrian behaviors in a corridor with

medium to relatively high density of pedestrians. Simulation results with this model are

summarized in this paper. Copyright # 2010 John Wiley & Sons, Ltd.

KEY WORDS: navigational behavior, pedestrian simulation, spatial-temporal patterns

Introduction

Modeling human-like pedestrian navigational beha-

viors in a virtual environment is still a challenging task

to date. In real life, various cognitive and navigational

activities are involved for pedestrian to reach their

destinations in a complex dynamic environment with

various static obstacles and moving objects. From a

computational model point of view, these activities can

be broadly classified into two levels. At the higher level,

path planning is involved to plan a rough path for a

pedestrian to move through the environment from a

starting location to a destination. At the lower level,

maneuvers are carried out by the pedestrian to navigate

along the path while keeping away from potential

collisions with other objects or pedestrians in the

environment.

The higher-level navigational behavior simulation has

been generally well studied 1–3. On the other hand,

lower-level pedestrian navigational behaviors involve

dynamic interaction between pedestrians and the

environment and among themselves in relatively short

distances. It represents a more challenging, yet inter-

esting issue in navigational behavior modeling and

simulation. Some steering mechanisms 4–7 have been

studied and applied in different models and seemingly

realistic steering behaviors have been generated.

However, a salient feature of realistic steering behaviors

can hardly be reflected by existing approaches: ped-

estrians can effectively steer even in a relatively dense

crowd without much dedicated effort. This is an

important feature for a computational model to achieve

high degree of realism that can be observed in real life

situations.

This paper presents a novel pattern-based framework

to address this challenge in modeling lower-level

pedestrian navigational behaviors. In this framework,

an agent’s perceived information and subjective judg-

ment on the situation are summarized as spatial-temporal

patterns. An agent will match the perceived patterns

with some prototypical cases in its experience. That is,

an agent’s decision-making process is essentially treated

as a pattern-matching process.

COMPUTER ANIMATION AND VIRTUAL WORLDS

Comp. Anim. Virtual Worlds 2010; 21: 387–399Published online 24 May 2010 in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/cav.341* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

*Correspondence to: N. Hu, Parallel and Distributed Comput-ing Center, School of Computer Engineering, Nanyang Tech-nological University, Singapore.E-mail: [email protected]

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Copyright # 2010 John Wiley & Sons, Ltd.

This pattern-based framework provides good poten-

tial to reflect the salient feature of pedestrian steering

behaviors in two aspects:

� First, the use of patterns in modeling pedestrians’

perception and judgment on the situation reflects

the fact that pedestrians usually process information

in a bulk/parallel manner. It has endowed our frame-

workwith the human-like capability of effective situa-

tional assessment.

� The pattern-matching mechanism imitates the effi-

cient decision-making process of human beings based

on their experience 8,9. Note that different expertise

levels result in different efficiency in pedestrians’

steering behaviors. For example, most adults are

experienced walkers in normal situations. They keep

sufficient patterns to help them perceive, judge the

situation rapidly, and avoid the potential collision

efficiently based on corresponding empirical instruc-

tions. Thus, they are observed to move smoothly even

in crowded places. On the contrary, children, who

have acquired few patterns and corresponding

empirical instructions in their experiences, must often

resort to some reactive actions to resolve collisions in a

close distance. Thus, their movements in crowded

places often look less smooth compared to adults

and are more likely to bump into others.

This paper is organized as follows: Section 2 describes

some related work on pedestrian modeling and simu-

lation. Our modeling methodology and the proposed

pattern-based modeling framework for pedestrian simu-

lation are presented in Section 3. Based on this framework,

wedescribe a computationalmodel to simulate pedestrian

behaviors in a corridor scenariowithmedium to relatively

high density of pedestrians. Test cases and simulation

results are shown in Section 5. Section 6 concludes this

paper and also provides ideas for future work.

RelatedWork

There have been many attempts to simulate the lower-

level navigational behaviors of pedestrians, where

collision avoidance plays a central role. In general,

existingwork can be broadly classified into three classes:

force-based, rule-based, and example-based models.

Force-based approaches essentially treat an individ-

ual as a physical entity and impose some artificial forces

on the entity to control its motion. A typical example of

this approach is Helbing’s social force model 10, where

some artificial forces have been introduced to constrain

the movement of an individual in a similar way as

Newtonian mechanics. Although plausible simulation

results have been observed by such model, we feel that

the treatment of an individual as a physical entity is

oversimplified. In particular, these social forces are not

adequate to reflect the richness in pedestrian interaction.

In agent-based pedestrian simulations, various forms of

potential field have been used to reflect agents’ personal

preferences and decision-making in their steering

behaviors 4,11–13. In these works, collision avoidance is

generally achieved through a series of comparisons

between an agent and each potential threat. Spatial

information is processed in a separate and sequential

manner, while real pedestrians seem to process spatial

information in a bulk and parallel manner.

Rule-based models achieve collision avoidance based

on certain pre-defined rules 3,5,14. In References 15–17, a

layered or multi-resolution approach is used to model

an individual’s steering behaviors in a crowd based on

certain collision avoidance rules. In general, these rules

are tightly coupled with conditions that have been

foreseen, thus the models do not react well to

exceptional situations that inevitably arise in a high-

density crowd simulation. Furthermore, it is difficult for

users to specify such rules for different situations.

To overcome the downside of the rule-based approach,

example-based approach has been proposed and applied

in crowd simulation recently 18,19. Real-life examples of

the moving trajectories of individuals within a crowd are

recorded, extracted, and stored. Agents in the simulation

analyze the current situation and compare with the stored

examples and apply the moving trajectories of the

matching example. Though seemingly natural behaviors

can be generated, such approach synthesizes the paths

for the agents. Only external factors such as the

temporal-spatial information that influence the naviga-

tional behaviors can be extracted from the image-based

examples. Unique traits of individual persons are not

easy to be modeled with such approach.

Our work aims to imitate how real pedestrians make

steering decisions corresponding to the situations in real

life, and to generate plausible navigational behaviors

naturally. Geometricmodels such as 20,21 make use of the

space–time concept and discretize the surrounding

environment into regions. In our pattern-based

approach, spatial-temporal information is processed in

a bulk/parallel manner through the recognition of

spatial-temporal patterns from the situation. Behavior

diversity is naturally and easily reflected in the different

prototypical patterns that are stored as an individual’s

experience cases.

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Copyright # 2010 John Wiley & Sons, Ltd. 388 Comp. Anim. Virtual Worlds 2010; 21: 387–399

DOI: 10.1002/cav

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Methodologies andModelingApproaches

We believe that realistic lower-level navigational behaviors

can be generated by imitating how pedestrians make

navigational decisions in various real life situations and

modeling how they execute such decisions in loco-

motion. We first make two assumptions to describe and

explain the lower-level navigational behaviors, and then

investigate and model pedestrians’ decision-making

processes based on these assumptions.

Two Assumptions

Assumption 1: Experienced pedestrians proactively

apply certain simple steering strategies to minimize

the chances of performing instinctive reactions to avoid

imminent collisions.

It is amazing that most pedestrians can avoid collisions

efficientlywithout dedicated effort even in crowded areas.

Various steering behaviors such as following, overtaking can

be frequently observed in different situations. One of the

distinctive characteristics of these behaviors is that

proactive planning is involved. We refer to such a plan

of actions to avoid collision as a steering strategy. In

addition to such planned strategies, we also observe (less

frequently) some instinctive reactions such as standing still

suddenly or sliding body significantly that pedestrians resort

to in order to avoid imminent collisions. We do not

observe such instinctive reactions frequently for experi-

enced adults as they are equipped with various steering

strategies and tend to proactively apply these steering

strategies to prevent potential collisions in advance, thus

to avoid close contact with other pedestrians 22–24. While

for young kids with less experience of steering, they may

have to resort to instinctive reactions to avoid imminent

collisions, which also make their movement less efficient.

This assumption has two implications for pedestrian

modeling and simulation:

� First, we may divide the lower-level navigational

behavioral model into some (high-level) steering strat-

egies and (low-level) instinctive reactions.

� Second, instinctive reactions are relatively simple and

largely similar in various situations. In addition, with

good steering strategies, instinctive reactions seldom

occur. Therefore, our design focuses on steering strategies.

Assumption 2: A limited number of steering strategies

are enough for pedestrians to move smoothly in real-life

situations through scheduling and executing these

steering strategies properly based on their experience.

Though pedestrians perform many different

steering behaviors under different situations, a limited

number of steering strategies are commonly observed

in daily life such as following, overtaking, and side-

avoiding 22–24. A person’s steering strategies are usually

acquired from his/her past experience. In terms of

steering behaviors, experience influences pedestrians

from two aspects. Firstly, individual pedestrians

with different steering experiences may store different

sets of steering strategies in their long-term memory.

Secondly, even with the same set of steering strategies,

different experiences may cause pedestrians to select

and execute steering strategies in different ways. This

suggests that even equipped with the same limited

number of steering strategies, different pedestrians may

make different decisions on when to use which steering

strategies, thus they may demonstrate different steering

behaviors.

This assumption also has two implications on the

pedestrian modeling and simulation:

� First, it suggests a divide-and-conquer methodology

such that we may investigate limited sets of steering

strategies to generate complex behaviors in different

situations.

� Second, it suggests a modeling approach that empha-

sizes the role of experience in pedestrian decision

making. In this regard, pattern-based approach is a

good attempt, which will be discussed later.

Divide-and-ConquerMethodology

Based on the two assumptions, a divide-and-conquer

methodology is proposed for the design of ourmodeling

framework. As shown in Figure 1, the complex lower-

level pedestrian navigational behaviors are composed of

the default behaviors and the maneuvers for collision

avoidance. Maneuvers can be further divided into two

parts as steering behaviors and instinctive reactions and

our framework will focus on modeling the steering

behaviors due to Assumption 1. Based onAssumption 2,

steering behaviors consist of a limited number of

steering strategies from a strategy pool. Pedestrians

select strategies from the strategy pool and schedule

them in a proper sequence to achieve collision

avoidance. Each selected steering strategy is executed

through a sequence of locomotive actions (e.g., move,

stop, turn, etc.).

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SPATIAL-TEMPORAL PATTERNS* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

As shown in Figure 1, complex navigational behaviors

are divided into three levels, which allow a modeler to

focus on different levels in the hierarchy to meet

different design requirements. The divide-and-conquer

methodology has two main advantages:

� First, it naturally reflects the way real pedestrians

make decisions during navigation, e.g., pedestrians

will not consider how to execute a steering strategy

until it selects it.

� Second, it simplifies the modeling of complex naviga-

tional behavior. On one hand, it allows model devel-

opers to focus on behaviors with less complexity at a

time in top-down manner. On the other, it avoids the

complicated process of fine tuning of various influ-

ential factors that directly affect the locomotion of the

agent. Note that navigational behaviors are often and

naturally described in some higher levels than the

locomotive level in daily life. For example, we say

‘‘A is overtaking B’’ instead of ‘‘A increases his speed by

how many units and turns his direction by how much angle

to the left/right side of B.’’ In our framework, steering

behaviors lie in the intermediate level, which balances

the mentioned modeling problems of the higher and

lower level behaviors.

Pattern-based Framework

Figure 2 shows our modeling framework which follows

the divide-and-conquer methodology. We address two

important phases in the cognitive process that are

involved in selecting and executing steering strategies:

formation of situational awareness and pattern matching.

Pedestrians need to form up situation awareness about

the current situation. According to Endsley 25, there are

three levels of situation awareness: a person’s perception

of environmental elements within a volume of time and

space, the comprehension of their meaning, and the

projection of their status in the near future. In our

framework, agents continuously assess the environment

during navigation through sensory inputs (e.g., vision,

audition, etc.). Based on the sensed information,

comprehension and prediction aremade on the situation

for the current moment t as well as for a short period of

time Tp in the near future.

In the framework, we have proposed to use spatial-

temporal patterns to represent some prototypical spatial-

temporal configurations that pedestrians rely on to

quickly recognize the current situation. Figure 3 illus-

trates the pattern-matching mechanism that we pro-

posed to model the cognitive processes of a pedestrian.

Figure 1. Divide-and-conquer methodology.

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We first describe the steering strategy selection process.

From the situation awareness module, an agent will

recognize certain global spatial pattern as the perceptive

description and overall judgment on the current

situation. Together with its intention1, the agent tries

to match the global spatial pattern with certain

prototypical patterns (X1–Xn) from its experience

instances (E1–En). The matched prototypical pattern Xi

serves as the cue to retrieve the associated empirical

steering strategy Si. In case no matched pattern is found,

the agent needs to make decisions through a trial-and-

error process. Such case usually happens for a child,

who lacks the experience in recognizing the global

spatial patterns. Nevertheless, the match does not need

to be perfect as we will discuss in the next section.

Once a steering strategy Si is selected, the associated

empirical instructions Yi on how to execute steering

strategy Si will be retrieved. We model the empirical

instructions Yi in the form of a set of qualitative rules

(Ri1–Rik). These rules serve as the benchmarks to instruct

the agent to execute the specific steering strategy Si in a

particular way based on certain prototypical conditions.

Such conditions of the rules are usually kept in the

experience cases of the agent as certain patterns (P1–Pk)

about the situation. For example, one of the rules in Yi

could be ‘‘If there is enough available space on the right hand

side of the target, then overtake the target from the right hand

side.’’ The condition of this rule can be perceived as a

prototypical spatial pattern that reflects the particular

spatial configuration of the situation. The agent will then

match the various local patterns in the current situation

with the prototypical patterns. Based on the match, the

resultant execution Ak will be applied to the current

situation.

Implementation of aComputational Model

Based on the proposed framework, we have imple-

mented a computational model for simulating ped-

estrian steering behaviors in a medium to relatively

high-density scenario. We choose this scenario since

higher density in general will create higher opportunity

of potential collisions in the simulation, which may

involve various interesting steering behaviors. Such

scenario also represents many real-life situations such as

a corridor or a passway at a city link. The steering

strategy pool contains the three commonly observed

strategies that pedestrians usually adopt: following,

overtaking, and side-avoiding. We will focus on the

development of the key components of this compu-

tational model such as situation awareness and pattern-

matching process in this section.

Situational Awareness

Wemodel the three levels of situation awareness (SA) of

an agent.

Level1SA: perception. Currently, we only consider

the vision inputs as pedestrians mainly reply on visual

spatial information during navigation. Figure 4 illus-

trates the vision and attention system in the model.

In our current design, the vision of an agent is

constraint to the vision angle of 1208 and vision range of

around 5m in a relatively dense crowd, which is a

reasonable estimation with sufficient simplicity. How-

ever, vision angle can be further extended as the agent

actively perceives the environment, where head and eye

rotation is involved. Our model can easily adapt to

model such feature by includingmore columns to record

Figure 2. Modeling framework.

1The intention of an agent is generated from the higher-levelcognitive process and it indicates the steering strategy that theagent prefers to apply in the current situation.

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DOI: 10.1002/cav


the spatial information as will be discussed later. Vision

center indicates the moving direction of the agent. The

fan-shape originated from the agent and enclosed by the

vision range indicates the spatial information available

to the agent at each simulation frame. Information

collectedwith theAttention range of an agentwill be used

by the agent for decision making.

Level 2 SA: comprehension. To imitate the continu-

ous spatial information perceived by pedestrians, we

represent agents’ perception on the current spatial

information through radial intersection scanning. The

space within the vision range as shown in Figure 4 is

partitioned into 12 equal sectors by a total of 13 radials

including two boundaries eradiated from the agent. An

integer array is then used to represent the processed

spatial information within the attention range based on

the intersection status of the 13 radials. The value of this

array is designed as:

0: if the radial does not hit any agent

1: if the radial hits an agent with the same moving

direction

�1: if the radial hits an agent with the opposite

moving direction

If more than one agent is hit by the same radial within

the attention range, the closer one to the observing agent

will be considered. The processed spatial information as

shown in the Figure 4 is thus represented as:

SAðt; 0Þ¼ ½000� 1011100000�

From this array, we may easily infer that ‘‘there is

available space in my right front direction.’’ We will discuss

on how to form patterns from this spatial representation

in more detail in the next subsection.

Level 3 SA: projection. In addition to the spatial

information obtained from the Level 2 SA, agents further

assess the situation through mental prediction. In our

design, prediction is based on the current status (e.g.,Figure 4. Agents’ vision and attention system.

Figure 3. Pattern-matching mechanism.

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relative speed, relative direction, etc.) of the perceived

information. The spatial information in the predicted

frames will be extrapolated. Note that only the spatial

information of other agents within the vision range at

the current framewill be predicted to represent the basic

predictive ability of human in real life. For example, a

pedestrian usually cannot predict the information of

others behind a wall that he/she cannot see.

The 1D integer array in Level 2 SA can be extended to

a 2D array by taking the spatial information in the

predicted frames into account. For example, the Level 3

SA with three predicted frames is represented as:

SAðt; 3Þ ¼0 0 0 �1 0 1 1 1 0 0 0 0 0�1 �1 �1 0 0 1 1 1 0 0 0 0 0�1 �1 0 0 1 1 1 1 1 0 0 0 0�1 0 0 0 1 1 1 1 1 0 0 0 0

2664

3775

Such 2D representation is not only capable of

representing the continuous spatial information in a

discrete form, but also capturing the dynamic change of

spatial information in the temporal domain automati-

cally. Furthermore, the array data structure provides

good feasibility for formulating the pattern-matching

process in computer program as will be discussed.

Though the static obstacles are not included in the

example as demonstrated in Figure 4, it is worth

mentioning here that we handle the static obstacles in

the formation of the three levels of SA in the similar way

as we process the dynamic obstacles (e.g., other agents).

The only difference lies in the representation of the

spatial information for the perceived static obstacles

within the attention range of the observing agent. We

may assign a distinctive value (e.g., 2) to represent that a

direction is blocked by static obstacles.

Pattern-matching Processes

Steering Strategy Selection. We have proposed a

simple steering strategy selection scheme based on the

agent’s general situational judgment and intention.

This scheme is based on a fuzzy pattern-matching

mechanism.

The general situational judgment usually involves the

immediate recognition of certain global spatial pattern at

the current time instance without further prediction.

Thus, only the first row of 2D representation of the

situation awareness is used in this process. Such global

spatial pattern is characterized by the spatial features

such as themoving direction aswell as the relative positions

of other agents to the observing agent. The value sets for

these spatial features are formulated into fuzzy sets as:

A¼ {same, mostly same, mostly opposite, opposite}

B¼ {front is fully blocked, front center is blocked with

available space on either side, front center is not blocked}

The value set for intention of the agent is defined as a

crisp set as:

C¼ {overtaking, following, side-avoiding}

The value set of the result of the selection is defined as

another crisp set as:

Y¼ {overtaking, following, side-avoiding, instinctive reac-

tions}

The relation between these variables can be described

using some fuzzy rules in the form of Y¼ f (A, B, C). The

fuzzy rules reflect different decisions on the strategy

selection of different pedestrians according to their

different experiences. For instance, one of such rules

corresponding to the example shown in Figure 4 is ‘‘if the

front center is blocked by agents with same moving direction

and there is available space on either side, and the preferred

strategy is overtaking, then the strategy overtaking is

selected.’’ By aggregating all of such rules, a check-up

table can be built to imitate the experience instances of

pedestrians.

Steering Strategy Execution. Once a specific steer-

ing strategy is selected, the agent needs to properly

execute the selected strategy. Issues such as ‘‘how to

choose an overtaking (or following) target’’ and ‘‘how to

overtake (or follow) the target’’ need to be resolved to

guide the agent’s locomotive actions. In this subsection,

we discuss the pattern recognition, pattern matching,

and instruction retrieval processes that have been

implemented in the model.

Local Pattern Recognition. As shown in Figure 5, we

consider three spatial features of the spatial configur-

ation: relative distance (sd), relative direction (sa), and the

spatial span (sw, e.g., the spatial width perpendicular to

the observing agent’s moving direction).

Based on these features, a spatial pattern can be

specified ps (sd, sa, sw). Note that the parameters (e.g., d1,

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d2, u1, u2, w) are for visual illustration purpose only. In

actual implementation, we make use of the situation

awareness in the form of a 2D integer array based on the

following value assigning scheme:

� sd is set according to the row index of a particular row

in the array, where spatial information grouping are

performed. Note that an agent only processes spatial

information (e.g., set to 0, 1, or �1 etc.) within its

attention range when forming its situation awareness.

Thus, relative distance can be measured in terms of

the number of predicted frames. In our design, the

particular frame is identified as the first row in the 2D

array that contains the least number of 0s with the

seventh (e.g., vision center) column is not a 0. In this

row, the available space for the observing agent is

most limited. Thus, there is least number of choices

available and the least amount of time for the agent to

execute the steering strategy according to the com-

prehension and prediction on the situation at the

current time instance.

� sa is set according to the direction deviation from the

observing agent’s vision center. It can be calculated

easily as the differences between the column index of

the group of spatial information to the interest of the

observing agent and 7 (e.g., column index of the vision

center).

� sw is set based on the continuity of the group of spatial

information at the particular frame as discussed above

in setting sd. Specifically, the value equals to the

number of continuous 1 or �1 or 0 at the particular

row of the 2D array.

PatternMatching. In general, pattern matching is the

act of checking for the presence of the constituents of a

given pattern. Thus, pattern-matching process can be

realized through a series of comparisons between paired

features that are used to define the pattern. We calculate

the similarity value for each pair of the features as

g j ¼ Gjðfj; fej Þ, where Gjð�Þ represents the function to

measure the similarity value between the pair of features

of type j. fj and fej represent the features of type j that are

used in the definition of the recognized pattern in the

current situation and the prototypical pattern stored in

the experience of the agent, respectively.

The similarity between the two patterns can then be

obtained by aggregating the similarity values for all the

features as g ¼ Pwj � g j, wherewj represents the relative

importance of different features.

Empirical Instruction Retrieval. Several recognized

local patterns may be matched with the same proto-

typical patterns with different similarity values. Our

empirical instruction retrieval mechanism is not solely

based on the pattern similarity value, but also considers

the adaptation effort to apply the instruction in the

current situation. The adaptation effort is measured as

the aggregated change in moving speed and direction.

Some pedestrian studies 22,26 provide guideline for the

specific settings in the aggregation. By considering the

adaptation effort, not all the local patterns are necess-

arily compared with the prototypical patterns in the

empirical instruction for the specific steering strategy

execution. This makes our model more efficient in terms

of runtime performance.

Simulation Results

We have implemented the aforementioned compu-

tational model in liteC provided by GameStudio A7.

Figure 6 shows some different views of a testing scenario.

We have tested themodel by tracking the navigational

behaviors of some individual agents in some typical

testing cases and analyzing their steering choices in such

cases together with their perceived spatial-temporal

patterns. In particular, we examine the execution

processes of individual steering strategies (following,

overtaking, and side-avoiding) as well as the autonomous

scheduling of these strategies.

Figure 7 shows a typical test case for our model. The

visionary radials are drawn in black lines. The personal

space of the observing agent as well as those who have

entered into the attention range of the observing agent is

shown as a cylinder. We specify the individual

parameters (e.g., personal space, number of predicted

frames, etc.) and the rules relating prototypical cases and

Figure 5. Spatial pattern.

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corresponding instructions based on some existing

pedestrian studies and our own experiences as ped-

estrians in real life. For example, a typical rule for

overtaking that we have implemented is ‘‘if there is

available space on the right hand side, then overtake from right

hand side.’’ It should be pointed out that these parameters

and rules can be easily changed according to different

requirements based on different set of experiences.

In the testing case shown in Figure 7, we set the

intention of the observing agent as ‘‘overtaking,’’ which

indicates that the agent will always attempt to overtake

others if the situation allows it to do so. In the beginning

(see Figure 7a), it notices the group of two agents in front

and the wall on the left-hand side blocks its way. In

Figure 7b, it decides to overtake from the right-hand side

of the group in front. Later in Figure 7c, its newly

perceived spatial-temporal pattern indicates that there is

no available space in front for the next few frames. Note

that in Figure 7c, the agents on the right front of the

observing agent are out of the attention range of the

observing agent at the current frame. However, since

prediction is involved in the proposed pattern recog-

nition process, the agent can actually perceives the

situation as ‘‘no available space for overtaking’’ and will

change its steering strategy from overtaking to following

temporarily. The agent executes the following steering

strategy in Figure 7d and e. In Figure 7e, the newly

perceived spatial-temporal pattern indicates that there

will be available space on the left-hand side since the

agent occupying the space currently is overtaking others

and will make the space available. Thus, the overtaking

steering strategy is selected by the agent. Figure 7f and g

illustrates the overtaking process.

In Figure 8, we have tested cases with agents moving

in different directions. We have observed that agents

avoid each other in a cooperative manner, and

temporary lane-formation phenomenon can also be

observed during this process. Due to page limit, we will

Figure 6. Different views of simulation cases.

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DOI: 10.1002/cav


not explain this test case in detail. More testing results

can be found from the supplementary videos.

Though a quantitative validation of the model is still

in progress, the current simulation results are promising

in the way that:

� Our model is capable of replicating some interesting

pedestrian behaviors in real life situations.

� By integrating our model with some higher level

cognitive layers, macroscopic phenomena such as

lane-formation and leader-following could be

achieved.

� The model naturally follows the way pedestrians

make decisions based on their perceived spatial-

temporal patterns and experiences. The model makes

it easier for developers to specify the rules that reflect

individual experiences to cater for different appli-

cations.

Certain limitations of our current implementation can

also be found from these test cases. First, since single

thread programming is used currently, all the agents

make decisions at the same frame, which may some-

times cause unrealistic decisions. Second, the evenly

distributed visionary radials may cause difficulties for

pattern specifications to describe groups of agents at the

peripheral area within the attention range of the

observing agent. Thirdly, the static 1208 of vision angle

may cause an agent to miss some spatial information

that could be detected by real pedestrians. As discussed,

this problem can be partially solved by expanding the

number of columns in the 2D array form of situation

Figure 7. Automatic steering strategy scheduling between overtaking and following strategies.

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awareness. Research such as Reference 24 that focuses on

modeling the gaze attention of the agents could provide

idea for further improvement on our model as well.

Conclusion and FutureWork

We present a pattern-based framework that aims to

imitate how real pedestrians perceive andmake steering

decisions in daily-life situations. This new framework

has the potential to improve the realism of pedestrian

simulation. As the spatial-temporal information percep-

tion and reasoning processes of this pattern-based

approach are more natural and closer to how we make

steering decisions in daily-life, the framework makes it

easier for model developers to specify various

parameters and behavioral rules in simulating ped-

estrian behaviors in different situations. With this

pattern-based approach, the complex cognitive pro-

cesses involved in steering decision making are

essentially transformed to pattern-matching processes

between the perceived spatial-temporal patterns and the

prototypical cases in agents’ experience.

The current simulation results are promising. We plan

to further refine some of the key components in our

framework, especially the situation awareness and the

representation of spatial-temporal patterns. Quantitat-

ive validation of the model will also be conducted.

Nevertheless, we hope that the idea presented in this

paper can direct researchers in this area with a fresh

perspective.

ACKNOWLEDGEMENTS

This work is supported in part by the Singapore National

Research Foundation under Grant NRF2007IDM-IDM002-052.

References

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Authors’ biographies:

Nan Hu is currently a Ph.D. candidate in the School ofComputer Engineering at Nanyang Technological Uni-versity (NTU), Singapore. He received his B.Eng. inComputer Engineering from NTU in 2007. His currentresearch interests include decision-making of ped-estrians during navigation, situation awareness,spatial-temporal pattern representation, and matchingmechanism in modeling crowds of pedestrians.

Suiping Zhou is currently an Assistant Professor in theSchool of Computer Engineering at Nanyang Techno-logical University (Singapore). Previously, he worked asan Engineer in Beijing Simulation Center, China Aero-space Corporation, and then joined Weizmann Instituteof Science (Israel) as a Post-doctoral fellow. He receivedhis B.Eng., M.Eng., and Ph.D in Electrical Engineeringfrom Beijing University of Aeronautics andAstronautics(P.R. China). He is a member of IEEE and his current

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research interests include large-scale distributed inter-active applications (e.g., MMOGs), parallel/distributedsystems, and human behavior representation in model-ing and simulation. He has published over 70 peerreviewed articles in these areas. He is currently anAssociate Editor of the International Journal of Compu-ter Games Technology. He has served as technical pro-gram committee member of many internationalconferences and workshops in computer games andvirtual environments.

Zhongke Wu is a Professor in College of InformationScience and Technology, Beijing Normal University,P.R. China. He received B.Sc. in Mathematics fromPeking University in China in 1988, and M.Eng. andPh.D. in CAD/CAM from Beijing University of Aero-nautics & Astronautics, China, in 1991 and 1995 respect-ively. His current research interests include computergraphics, geometric modeling, CAD/CAM, volumegraphics and medical imaging, scientific visualization,animation and virtual reality.

Mingquan Zhou is the Dean of College of InformationScience and Technology, Beijing Normal University,P.R. China. His current research interests include com-puter graphics, 3D visualization. He has received 11awards of province andministry. He has published over300 important papers in this field.

Cho Eng Keong Benjamin is currently an undergradu-ate student in School of Computer Engineering atNanyang Technological University.

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spatial-temporal patterns and pedestrian simulation

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