Capturing the Variety of Urban Logical Scenarios from Bird-view

Trajectories

Christian King, Thilo Braun, Constantin Braess, Jacob Langner and Eric Sax

FZI Research Center for Information Technology, Haid-und-Neu-Str. 10-14, Karlsruhe, Germany

Keywords:

Logical Scenario Extraction, Scenario Coverage, Urban Scenarios.

Abstract:

Driving scenarios are an essential part of validation of future highly automated driving (HAD) systems. In

order to provide a valid proof of safety, it is crucial to test the system in as many realistic driving scenarios as

possible. For this reason, it is necessary to extract driving scenarios from recorded data. A particular challenge

in urban trafﬁc is that there is a high degree of interaction between road users that needs to be considered. In

this paper we present a concept for a maneuver-based extraction of driving scenarios. The extracted scenarios

are provided in a format that supports a swift understanding of the content. In addition to the mere driving

scenarios, parameter ranges for each scenario are grouped and aggregated from the data. Hence, the scenarios

extracted with the presented concept can be used for re-simulation during the validation. We provide some

results from the scenario extraction for an intersection from the INTERACTION data set.

1 INTRODUCTION

Early driver assistance systems, such as cruise con-

trol, Anti-lock Braking System (ABS) or Electronic

Stability Program (ESP) were inﬂuenced solely by the

driving condition of the driver’s own vehicle (VDA,

2015). The ongoing automation of the driving task

results in today’s driver assistance systems being in-

creasingly inﬂuenced by the vehicle’s static and dy-

namic environment. Future Highly Automated Driv-

ing (HAD) systems, such as an urban intersection as-

sistant, must additionally be able to operate freely

within a trafﬁc area. Thus, the interaction with the

environment and surrounding trafﬁc will become a

crucial aspect for these systems. The possible driv-

ing situations in HAD systems can be assigned into

four quadrants dependent on the risk and the knowl-

edge about the driving situation, shown in Figure 1

(Stavesand, 2019). In order to be able to provide

proof of safety during the validation process, a spe-

cial emphasis must be placed on the unknown risks.

Established test concepts utilize a requirement-based

test approach that derives test cases from system re-

quirements (Sax, 2008). Hence, the approach has the

limitation that only known risks can be considered.

Referring to the four quadrants in Fig 1, requirement-

based testing can only address driving situations that

are already known and additionally covered by re-

quirements. Moreover, the consideration of all pos-

Known

Unknown

Safe

Unsafe

Known

Safe

Situations

Unknown

Safe

Situations

Known

Risks

Unknown

Risks

Figure 1: Classiﬁcation of possible driving situations in

highly automated driving according to (Stavesand, 2019).

sible interactions in an open world context by system

requirements in sufﬁcient granularity is not feasible.

A promising approach for the validation of HAD

systems is scenario-based testing. In contrast to the

requirement-based test approach, driving scenarios

form the basis for test case creation (P

utz, 2017).

The fundamental idea of scenario-based testing is that

if all theoretically possible driving scenarios can be

enumerated and the autonomous vehicle is tested in

all scenarios, a statement about safety of the sys-

tem can be derived (de Gelder and Paardekooper,

2017). The assumption is made that if a vehicle

has successfully completed a particular scenario, that

other, similar scenarios will also be successfully com-

pleted (Shwartz et al., 2017). In his work, Bagschik

King, C., Braun, T., Braess, C., Langner, J. and Sax, E.

Capturing the Variety of Urban Logical Scenarios from Bird-view Trajectories.

DOI: 10.5220/0010441204710480

In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021), pages 471-480

ISBN: 978-989-758-513-5

471

proposes three levels of abstraction for scenarios

(Bagschik et al., 2017). Functional scenarios repre-

sent the highest and most abstract level of scenarios.

At this level, operational scenarios of the develop-

ment object are collected on a semantic level. The

next level is formed by the so-called logical scenarios,

which map the functional scenarios onto a physical

state space. They represent driving scenarios by enti-

ties and relationships of these entities with the help of

parameter ranges in the state space. Concrete scenar-

ios are deﬁned as the lowest level. They represent

driving scenarios uniquely by entities and relation-

ships of these entities using ﬁxed values in the state

space.

Within the project PEGASUS, funded by the Ger-

man Federal Ministry for Economic Affairs and En-

ergy (BMWi), a 6-level model was developed, which

can be used for the description of scenarios, see Fig-

ure 2. This model uses different levels to repre-

sent the different aspects and properties of a scenario

(PEGASUS-Projekt, 2018). The ﬁrst two levels L1

and L2 of the model describe the basic road layout

on which the scenario takes place. This includes both

the road topology and its characteristics, as well as the

guidance infrastructure such as construction barriers,

lane markings and trafﬁc signs. Temporary modiﬁca-

tions, such as changed lane alignments or guidance fa-

cilities in construction sites, are described in the third

level L3. Moving objects, as well as maneuver-based

interaction between road users is part of the fourth

level L4. The ﬁfth level L5 is used for modeling en-

vironmental conditions and their inﬂuence on the lev-

els L1 to L4. Digital information, such as Vehicle-to-

Everything (V2X) communication or digital maps, is

described in the last level L6.

In order to obtain test scenarios for the validation

either a knowledge or data-driven approach can be

used (Stellet et al., 2015). The knowledge-driven ap-

proach derives test scenarios from system use cases

and addresses known driving states. By ﬁltering rele-

vant cases from empirical data and extracting driving

scenarios from it, the data-driven approach enables

the identiﬁcation from previously unknown driving

states. The empirical data encompasses data from

Naturalistic Driving Studies (NDS), Field Operational

Tests (FOT), accidents as well as driving simulator

and real world test trials (Ebner, 2014).

A major remaining challenge in scenario-based

testing is to extract a comprehensive and representa-

tive set of driving scenarios from recorded real world

data. To ensure that the extracted scenarios can also

be used for validation, a semantic description must be

available.

In this paper we present a concept for extracting

Street level (L1)

• Geometry and topology

• Condition and boundaries

Traffic infrastructure (L2)

• Construction barriers

• Signs and traffic guidance

Temporal modifications L1 and L2 (L3)

• Geometry and topology overlay

• Time dependent > 1 day

Movable objects (L4)

• Dynamic, movable

• Interactions, maneuvers

Environment conditions (L5)

• Influence on properties of other level

Digital Information (L6)

• V2X Information

• Digital maps

Figure 2: The 6-layer model to structure scenarios pre-

sented by PEGASUS(Project, 2019).

logical scenarios in an urban environment from bird-

view trajectories. This concept is applied on an inter-

section from the INTERACTION data set (Zhan et al.,

2019). The presented approach considers each vehi-

cle in the data set as a ego vehicle and is based on

the assumption that each vehicle can experience mul-

tiple composite scenarios. Thus, a much greater vari-

ety of logical scenarios can be extracted from the data

set. The description of the logical scenarios focuses

on driving maneuvers and the interaction with other

road users. Moreover, we present a visualization tool

that makes the data set accessible and browsable on a

logical scenario level. This tool can be used to search

speciﬁcally for scenarios with a certain content.

This paper is structured as follows: Section II

presents the related work regarding the extraction of

logical scenarios. In the third section we introduce

our concept. In Section IV we apply the concept on

an intersection from the INTERACTION data set and

show some results. After the evaluation in Section

V, some conclusions and future work are presented in

Section VI.

2 RELATED WORK

Zhao presents an open database that includes natural-

istic driving scenarios extracted from public data col-

lected by the Safety Pilot Model Deployment program

in an urban environment (Zhao et al., 2017). The pre-

sented database covers six different types of scenar-

ios:

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472

Extraction of

driving

maneuvers

Categorization

of traffic

participants

Identification

of concrete

scenarios

Aggregation

to logical

scenarios +

parameter

space

Trajectory data

Map data

Visualization

of logical

scenarios

Figure 3: Requited steps for the presented scenario extraction approach.

• Free ﬂow scenario

• Car-following scenario

• Cut in scenario

• Lane change scenario

• Pedestrian crossing scenario

• Cyclist scenario

The data set used to perform the scenario extraction

is comprised of of object-list data from radar sensors,

information provided by a vision-based system from

Mobileye as well as a trip summary. For each scenario

Zhao provides an algorithm to query the respective

scenarios from the open accessible data base.

(Langner et al., 2019) extract scenarios of dy-

namic length for one trafﬁc participant including traf-

ﬁc infrastructure from map data, such as curviness,

slope and speed limits. The segmented scenarios are

enriched with a feature vector, containing relevant in-

formation for the system under test. For combining

the scenarios to logical scenarios, common cluster al-

gorithms are applied on the feature vector.

(Hartjen et al., 2019a) provide a semantic descrip-

tion of extracted scenarios for urban trafﬁc based on

maneuvers. The maneuvers are grouped to infrastruc-

ture maneuvers, describing connections to the infras-

tructure, object-related maneuvers, describing inter-

action with other participants, and vehicle state ma-

neuvers, describing behavior without a connection to

external elements. The extracted maneuvers serve

as basis for logical scenarios, whereby the individ-

ual maneuver parameters are combined to parameter-

distributions. In further work, they show the appli-

cation of the concept for scenarios with interactions

with pedestrians (Hartjen et al., 2019b).

(Erdogan et al., 2019) extract scenarios based

on recorded trajectories focusing cut-in/out and lane

changes scenarios on highways. For detection of

these, they implemented rule-based, supervised and

unsupervised classiﬁcation algorithms and compared

their results.

(Elrofai et al., 2016) extract turns and lane change

scenarios from only in-car sensor data. For the detec-

tion, a physical model is used. The extracted scenar-

ios are enriched with characterizing parameters.

An important purpose of scenarios during the val-

idation is that they serve as a basis for test deﬁnition.

Therefore, they should be available as logical scenar-

ios to enable the generalization approach. To test in-

teractive systems, a simple replay of trajectories is no

longer sufﬁcient. The scenarios must therefore be ex-

tracted in a format that represents the relationships of

the road users at an abstraction level that allows an

interactive re-simulation. In addition, representative

parameter spaces and parameter distributions are re-

quired for a reliable validation.

3 EXTRACTION CONCEPT

We present an approach that identiﬁes scenarios with

respect to the 6-layer model, shown in Figure 3. For

this purpose, we place a special focus on the interac-

tions with other road users at the layer L4, represented

by driving maneuvers. The ﬁrst step of the proposed

concept comprises the identiﬁcation and categoriza-

tion of all relevant trafﬁc participants from input data.

Based on the classiﬁcation, a driving maneuver ex-

traction is performed. Subsequently, concrete scenar-

ios are identiﬁed, which are then aggregated into logi-

cal scenarios. The ﬁnal step is the visualization of the

results to enable further use. The individual steps are

described in more detail below.

Input Data. The input data for the extraction are

vehicle trajectories, for example from the INTERAC-

TION data set (Zhan et al., 2019), as well as map in-

formation. The map is represented in the Lanelet2

framework (Poggenhans et al., 2018) and includes

lane information, a routing graph and regulatory el-

ements. The entire processing chain from Figure 3 is

performed for each vehicle in the data set. Thus, each

vehicle can be both ego-vehicle and participant in the

extracted logical scenarios.

Categorization of Trafﬁc Participants. The initial

step of the extraction concept is the categorization of

trafﬁc participants. All surrounding vehicles are ex-

amined to determine if they are a preceding vehicle

Capturing the Variety of Urban Logical Scenarios from Bird-view Trajectories

473

Figure 4: Possible interaction of ego vehicle (orange) with

other road users (green): (a) merging, (b) diverging, (c)

crossing.

or otherwise interacting with the ego vehicle. Hence,

this work considers three different types of interac-

tion between the ego vehicle and other road users, see

Figure 4.

Merging. The trajectory of another road user merges

on the ego trajectory and become a preceding ve-

hicle.

Diverging. The trajectory of a preceding road user

diverges from the ego trajectory.

Crossing. The trajectory crosses the ego trajectory in

space and time.

In order to reduce complexity, non-interacting ve-

hicles on adjacent lanes are not yet considered for sce-

nario extraction in the work. The different interaction

types are determined by crossing lanelet paths and tra-

jectories. Moreover, a crossing requires a positive

post encroachment time (PET). The PET describes

the time interval between two objects when the ﬁrst

object leaves a conﬂict point and the second object

enters into it (Paul, 2019).

Extraction of Driving Maneuvers. During the

next step, a maneuver recognition is performed for

all relevant surrounding vehicles. We use a maneu-

ver model based on Hartjen (Hartjen et al., 2019a). A

distinction between driving maneuvers and the driv-

ing context is introduced in addition to the work of

Hartjen. The driving context includes interactions

with other road users as well as further information

on whether a lane change was performed, for exam-

ple, on the crossing area or the access road. Since the

data sets contains some lane changes that could not be

trivially performed on the routing graph, the maneu-

ver of illegal lane change is additionally introduced.

Identiﬁcation of Concrete Scenarios. Based on

the driving maneuvers and the driving context we

perform the identiﬁcation of concrete scenarios. The

presented work is based on the assumption that each

vehicle can experience multiple composite scenarios.

A brief example might clarify this concept, shown in

Figure 5: The ego vehicle (yellow) experiences three con-

crete scenarios: turn left with crossing participant (1), turn

right (2) and turn left and turn right (3).

Figure 5. For the purpose of this contribution,

the ego vehicle will always be represented by the

orange object in the images. The example shows a

vehicle turning to the left with crossing trafﬁc and

then directly turning right. In terms of the validation,

both the turn left, the turn right and the combination

of both are interesting. Therefore, the complete

driving sequence can be considered as one scenario

with two turns or two smaller scenarios with one

turn each. Thus, three scenarios can be derived from

this single driving sequence. This distinction can

be particularly interesting for statements regarding

a potential coverage. At the layer L4, the concrete

scenario is represented as a sequence of maneuvers,

while preserving the causality of the maneuver se-

quence. To reduce artifacts in scenario identiﬁcation,

we have introduced rules for when a scenario is

considered valid:

• The track of the object does not start on a crossing

area

• The object must cross an intersection

Finally, a parameter determination is carried out. The

parameters depend on the maneuver performed. For

instance, the minimum distance to the preceding vehi-

cle can be determined during a follow maneuver, but

not if the intersection is crossed alone.

Aggregation to Logical Scenarios. In the last ex-

traction step, the concrete scenarios are aggregated

into logical scenarios. We distinguish logical scenar-

ios as different maneuver sequences. Thus, concrete

scenarios with the identical maneuver sequence can

be grouped into the same logical scenario and their

explicit parameter values can be converted to param-

eter value ranges for the logical scenario. Lastly the

logical scenarios are stored in a database with follow-

ing information:

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474

Meta-Information. Logical scenario ID, including

concrete scenarios (Id, track id, start and stop

time), Parameter space

L1/L2. Reference to the map

L4. Maneuver Sequence

Visualization of Logical Scenarios. To visualize

the extracted logical scenarios we build a dashboard

that represents scenarios in a searchable and struc-

tured way. For this purpose, a preﬁx graph is used

in which nodes describe groups of logical scenarios.

The maneuver sequences are represented as succes-

sions in the preﬁx graph and scenarios of the child

nodes are more precise subdivisions of the respective

parents. Additionally, we use a subset graph for a

more in-depth visualization. The subset graph shows

the logical scenarios included in the group of logi-

cal scenarios selected in the preﬁx graph. In contrast

to the preﬁx tree, each node represents a logical sce-

nario. The subset graph utilizes a tree ordering and

each parent node includes the maneuver sequence of

the child nodes. Additionally, the maneuver sequence

and representative scene overviews from the scenar-

ios are displayed to support the understanding of the

logical scenario. This combination of a preﬁx graph

and subset graph create a tool that allows a step-by-

step ﬁltering for scenarios with a certain content.

4 IMPLEMENTATION AND

RESULTS

The proposed scenario extraction approach is imple-

mented and applied on the DR USA Intersection EP

intersection from the INTERACTION data set. This

demonstrates that the approach is suitable for extract-

ing logical scenarios and representing them in a se-

mantic browsable manner. Additionally, further infor-

mation on occurrence probabilities and distributions

of logical scenarios are determined. First, general in-

formation about the extracted logical scenarios is pre-

sented. Thereafter, the actual content of the extracted

scenarios will be interpreted and discussed in more

detail. The data set comprises an American intersec-

tion without trafﬁc lights and with a total number of

around 750 vehicles and a high density of aggressive

behavior and near collision situations (Zhan et al.,

2019). The topology of the considered road section

is shown in Figure 6. The intersection is composed

of two crossing areas and access roads from all direc-

tions. The indicated arrows visualize the direction of

travel. For the sake of simplicity, the term scenario

Figure 6: Intersection DR USA Intersection EP from the

INTERACTION data set with marked driving direc-

tions(Zhan et al., 2019).

will refer to a logical scenario in this section. Con-

crete scenarios are explicitly mentioned.

4.1 General Analysis of the Results

Throughout the data set, the presented approach iden-

tiﬁed 1172 concrete scenarios, which are then clus-

tered into 504 logical scenarios. From the ﬁgures it is

apparent that a signiﬁcant number of vehicles experi-

ence several concrete scenarios. Figure 7 shows the

distribution of the number of concrete scenarios per

vehicle. The abscissa shows the number of concrete

scenarios per vehicle. The ordinate shows how many

vehicles have the respective number of concrete sce-

narios. Approximately 100 vehicles in the data set

do not meet the requirements listed in Sec. 3 and

therefore no concrete scenario can be identiﬁed for

them. The majority of vehicles have one or two con-

crete scenarios. The proportion of vehicles with 3

concrete scenarios is about the same as that with 0

concrete scenarios. There are vehicles for which 4 or

in one exception even 6 concrete scenarios are iden-

tiﬁed. The next analysis examines how the concrete

scenarios are distributed among the logical scenarios,

shown in Figure 8. The abscissa represents the num-

Figure 7: Distribution of the number of concrete scenarios

per vehicle.

Capturing the Variety of Urban Logical Scenarios from Bird-view Trajectories

475

Figure 8: Distribution of the number of concrete scenarios

per logical scenario.

ber of concrete scenarios in a logical scenario and on

the ordinate the occurrences is shown on a logarithmic

scale. As it can be seen from Figure 8, there are only

a few logical scenarios that encompasses a large num-

ber of concrete scenarios. The largest logical scenario

comprises 113 concrete scenarios, which corresponds

to around 9.6% of the total number of concrete scenar-

ios. This scenario solely involves a straight passing

of an crossing area without any interaction with other

road users and thus serves as the basis for a variety of

larger scenarios.

Figure 9: Four concrete scenarios from the largest logical

scenario.

Four exemplary concrete scenarios from this log-

ical scenario are illustrated in Figure 9. Furthermore,

Fig 8 reveals that most of the extracted logical sce-

narios are formed by only one concrete scenario. In

this context, the term singular logical scenarios is in-

troduced. Possible reasons for this can be the special

characteristics of the scenarios on the one hand or the

limited size of the data set on the other hand. Since

only concrete scenarios with an identical maneuver

sequence are combined into a logical scenario, even

small variations ensure that concrete scenarios are no

longer combined. Therefore, we have analyzed the in-

ﬂuence of the different maneuver types on the number

of scenarios. For this purpose, we examine in a ﬁrst

step how many scenarios would have been identiﬁed

without a certain maneuver type. In the second step,

the scenario extraction is performed with this inves-

tigated maneuver type. By comparing the scenarios

found, a conclusion is drawn about the inﬂuence of

the maneuver type. A key ﬁnding from this analysis

is, that temporal variations in the maneuvers concern-

ing only the ego vehicle’s state, e.g. acceleration or

standing still, lead to the biggest increase in scenar-

ios. Additionally, the preceding object maneuver also

highly inﬂuence the number of scenarios. Overall,

91% of the logical scenarios contain only three or less

concrete scenarios, 95% six or less.

4.2 Interpretation of Extracted

Scenarios

In order to be able to use scenarios for validation, the

content of the individual scenarios must be known.

Hence, we analyze on the one hand how the vehicle

navigates through the road network and on the other

hand how it interacts with other road users. This in-

formation can then be used, for instance, for scenario

design during re-simulation.

Navigation through Road Network. The scenar-

ios are examined with regard to the turn and lane

change maneuvers, see Table 1. 39.29% of the ve-

hicles in the data set did not perform any turn ma-

neuvers and crossed the intersection straight ahead.

The remaining vehicles performed at least one left or

right turn. Thus, 31.94% of the scenarios include at

least one turn left and 28.77% involve at least one turn

right.

Turn left Turn right

Turn left + turn right Turn right + turn right

Figure 10: Logical scenarios with different turn maneuvers

extracted from the dataset.

The data suggest that the maneuvers are reason-

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476

Table 1: Number of extracted logical scenarios regarding turn and lane change maneuvers.

Scenario deﬁning

maneuver

Number of extracted logical scenarios

total relative

Turn maneuver

Turn left 161 31.94%

Turn right 145 28.77%

Cross straight 192 39.29%

Turn left + turn right 8 1.59%

Turn right + turn right 2 0.39%

Lane change maneuver

Follow lane 363 72.02%

Lane change 141 27.98%

Illegal lane change 34 6.7%

ably evenly distributed. Moreover, eight vehicles ﬁrst

performed a turn left and then a turn right. Two vehi-

cles performed two turn rights in succession. An illus-

tration of the various turn maneuvers is shown in Fig-

ure 10. It can be seen from the ﬁgures that 27.98% of

the vehicles perform a lane change maneuver. Among

these 141 logical scenarios, 34 scenarios include a ve-

hicle that performs an illegal lane change. A logical

scenario that includes a composition of all three lane

change maneuvers is shown in Fig 11. Since (Zhan

et al., 2019) do not provide any information about the

exact location of the intersection we do not know the

surrounding infrastructure. Based on the trajectory in

Figure 11: Logical scenario composed of an illegal lane

change (1), follow lane (2) and a lane change (3).

Figure 11 we assume that there might be a parking

space in the top right corner. However, driving onto

or off the road has not been explicitly classiﬁed as a

maneuver and is therefore identiﬁed as an illegal lane

change.

The distribution of lane change and turn maneuvers

depends on the intersection and the trafﬁc ﬂow. Nev-

ertheless, the given results are a representative distri-

bution for the presented intersection.

Interaction with Other Road Users. Considering

the extracted logical scenarios with respect to interac-

tions with the surrounding trafﬁc, we ﬁnd that 22.82%

of the scenarios take place without any preceding ve-

hicle. 68.85% of the scenarios include at least one

follow maneuver and 50.79% approach maneuvers,

see Table 2. As mentioned before, the preceding ma-

Table 2: Number of extracted logical scenarios regarding

the preceding maneuver.

Scenario deﬁning

maneuver

Number of extracted

logical scenarios

total relative

No preceding 115 22.82%

Approach 256 50.79%

Follow 347 68.85%

neuver is the second largest inﬂuencing factor on the

number of scenarios. This is also reﬂected in the vari-

ety of possible maneuver sequences. Figure 12 visu-

alizes all existing maneuver sequences as a preﬁx tree.

Starting at a root node each path to a ﬁnal node rep-

resents an existing maneuver sequence of a scenario.

We used following color code within the graph:

no preceding yellow circle

approach red circle

follow green circle

ﬁnal node blue circle

Figure 12: Visualization of existing maneuver sequences

as a preﬁx tree (yellow circle = no preceding, red circle =

approach, green circle = follow, blue circle = ﬁnal node).

Capturing the Variety of Urban Logical Scenarios from Bird-view Trajectories

477

Table 3: Number of extracted logical scenarios regarding

the interaction types.

Number of

logical ScenariosInteraction

type total relative

Max Number

of Vehicles

Crossing 163 32.34% 9

Merging 194 38.49% 6

Diverging 111 22.02% 3

Crossing +

Merging

53 10.51%

Crossing:3

Merging: 2

Crossing +

Diverging

23 4.56%

Crossing: 2

Diverging: 2

Merging +

Diverging

26 5.16%

Merging: 3

Diverging: 1

Crossing+

Merging+

Diverging

16 3.17%

Crossing: 2

Merging: 1

Diverging: 1

The shortest scenario consists of one maneuver

and is created through the slicing of larger scenar-

ios. The longest maneuver sequence consists of two

consecutive sequences: ”no preceding” - ”approach” -

”follow”. During the design of test scenarios, the pre-

ﬁx tree from Figure 12 can be used to search for real

logical scenarios with a certain maneuver sequence.

In addition to the preceding object, different types of

interaction with other road users are also considered.

For this purpose, the logical scenarios are examined

in more detail with regard to the different types of in-

teractions presented in Figure 4. Moreover, we also

consider the number of participants within the inter-

action as a parameter of the logical scenario. By look-

ing at the ﬁgures in Table 3, one can see that merging

is the most common type of interaction with a cumu-

lative frequency of occurrence of 38.49%. In contrast,

crossing has the highest number of involved objects,

with a total number of nine crossing vehicles within a

logical scenario. Figure 13 shows a more detailed his-

togram about the number of vehicles for each interac-

tion type. The number of interacting vehicles is plot-

Number of interacting vehicles by interaction type

1 2 3 4 6 7 8 9

Number of Vehicles

Occurences

100

crossing

merging

diverging

Figure 13: Distribution of the number of interaction vehi-

cles by interaction type.

Figure 14: Logical scenario that include crossing (1), di-

verging (2) and merging (3) objects.

ted on the abscissa and the frequency of occurrence

is plotted on the ordinate on a logarithmic scale. Ac-

cording to the diagram, most interactions involve one

or two vehicles.

Moreover, scenarios that have a combination of

two or three interactions are also present in the data

set and have been identiﬁed. Figure 14 shows a log-

ical scenario that include all three types of interac-

tion. The scenarios includes a preceding object that

diverges from the ego trajectory and passes the inter-

section straight ahead. Additionally, the ego vehicle

have to wait for two crossing objects from left and the

oncoming trafﬁc as well as two vehicles merge into

the ego trajectory.

The representation of the logical scenarios in a preﬁx

and subset graph allows an easy and direct selection

of more remarkable scenarios like the one presented

in Figure 14. The selection setup for this scenario is

”turn left” in the preﬁx graph and ”merging + crossing

+ diverging” in the subset graph.

5 EVALUATION

Currently, there is no set of reference for scenarios ex-

tracted from the data set or any other type of ground

truth that could be used to validate the results of the

presented extraction. Thus, the evaluation has to be

done empirically. For the validation of the scenar-

ios with respect to turn and lane change maneuvers,

a ground truth is constructed from the existing data.

For this purpose, all tracks entering or leaving the

map on certain lanelets are identiﬁed. These track

clusters are then be compared to the extracted sce-

narios. No mismatches are found from the extracted

scenarios to the created ground truth. A random man-

ual review of logical scenarios is used to validate the

scenarios regarding the interactions with other road

users. In the process, it is checked for selected logi-

cal scenarios whether the assigned concrete scenarios

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478

matched in terms of content. Although all logical sce-

narios considered are judged to be plausible and cor-

rect, no general claim to correctness can be derived

from this small reviewing sample. In a third step, we

re-simulate individual scenarios in a simulation envi-

ronment and apply the scenario detection to the simu-

lated data. The results show that our approach is able

to correctly identify the initial scenario again.

The presented approach provides the capability to

search speciﬁcally for logical scenarios with a cer-

tain content in the data set. In addition, it provides

important information relevant to scenario design and

possible coverage statements, such as distribution of

certain maneuvers or scenario parameter.

6 CONCLUSIONS AND FUTURE

WORK

In this contribution, we presented a concept for the

extraction of logical scenarios in an urban environ-

ment. Thereby, a special focus is on the interaction

with other road users. The presented approach there-

fore operates centrally on the performed driving ma-

neuvers and the existing driving context. In particular,

interactions such as crossing, merging and diverging

are considered.

We presented some results from the application of

our approach to an intersection from the INTERAC-

TION data set. A total number of 1172 concrete sce-

narios were extracted from this data set, which can be

aggregated into 504 logical scenarios. Based on the

extracted logical scenarios a dashboard is created that

allows an easy analysis of the scenarios as well as en-

ables a semantic browsability of the data. For each

logical scenario the maneuver sequence is shown, as

well as all assigned concrete scenarios and the param-

eter ranges with their distribution.

As discussed earlier and shown in Figure 8, our

set of logical scenarios consists mainly of singular

logical scenarios and only a few larger logical sce-

narios are found. At this point, it must be exam-

ined to what extent an increase in the volume of the

data set results in the ﬁlling of known logical scenar-

ios or the creation of new logical scenarios. Consid-

ering a sufﬁciently large and representative data set,

such singular logical scenarios would represent cor-

ner cases. An extension of the maneuver catalog to

include pedestrian-related maneuvers as well as fol-

lowing objects or objects on adjacent lanes is also

necessary in order to adequately consider these inter-

actions as well. Although a complete validity proof

is not possible due to the lack of reference scenar-

ios or ground truth, we are convinced that the work

will make a valuable contribution to the validation

of HAD. The extracted scenarios can be used for a

scenario-based testing approach and with the avail-

ability of a representative data set, statements about

the coverage and relevance of individual logical sce-

narios can also be made. Future work will also focus

on the application of the approach on the entire IN-

TERACTION data set as well as other available data

sets. As part of this, the aggregation of results will

also become a future research question.

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