Intention-based Prediction for Pedestrians and Vehicles in Unstructured

Environments

Stefan Kerscher

, Norbert Balbierer

, Sebastian Kraust

, Andreas Hartmannsgruber

Nikolaus M

uller

and Bernd Ludwig

Continental Automotive GmbH, Siemensstraße 12, Regensburg, Germany

Department of Electrical, Media and Computer Engineering, Deggendorf Institute of Technology, Deggendorf, Germany

Institute of Information and Media, Language and Culture, University of Regensburg, Regensburg, Germany

nikolaus.mueller@th-deg.de, bernd.ludwig@ur.de

Keywords:

Prediction, Path Planning, Uncertainty Estimation, Autonomous Driving, Kalman Filter.

Abstract:

Motion prediction for holonomic objects in unstructured environments is an ambitious task due to their high

freedom of movement compared with non-holonomic objects. In this paper, we present a method for inferring

the future goal of holonomic objects by a heuristic generation of target points (tp) and following discriminating

decision making. The target points are generated, in a manner that covers the most common motion hypotheses

like ”following” or ”staying”, safety relevant motion hypotheses like ”crossing future ego trajectories” or the

”movement to special points of interest”, e.g. gained from a map. Subsequently, for each considered object

a trajectory to the inferred target point will be planned. Finally, the uncertainty of the trajectory is estimated

by applying a Kalman Filter with a dynamically adjusted process noise matrix. An additional beneﬁt of this

concept is its ability to cope with a different quality of context knowledge, so it can produce sound results

even at poor structured environments.

1 INTRODUCTION

Making automated vehicles really autonomous, they

must be able to cope with every situation on the street

and solve occurring problems on their own. For a

resilient and anticipating motion planning, the au-

tonomous car has to understand the intentions and

plans of its trafﬁc participants. This task is espe-

cially demanding for pedestrians, due to their holo-

nomic constraints. The problem becomes even more

evident since pedestrians are not necessarily bound

by a structure of lanes or streets. One possible way

to overcome these issues and make a robust predic-

tion of an object is a pure kinematic prediction with

the use of a Kalman ﬁlter like shown in (Schneider

and Gavrila, 2013), for example. Such linear predic-

tion methods are very precise in the short term fu-

ture (t

pred

< 2s). The further we are looking into the

future, the less reliable gets this prediction method.

This error stems from the non-linear movement of

the pedestrians, their goals, or new situations they en-

counter. To summarize, this error stems from the lack

of context knowledge.

Other methods try to include the context knowl-

edge like trafﬁc lights or spacial information about

the walkway (Hashimoto et al., 2015). There has

also been done some work in the ﬁeld of goal-directed

prediction of objects. Dagli shows in (Dagli and Re-

ichardt, 2002) an aim-based lane change recognition

method with Bayesian Networks. Rehder shows in

(Rehder et al., 2015) and (Rehder and Kloeden, 2015)

a goal-directed prediction method. This method

works on a grid representation and introduces the

goals as a gaussian mixture model which is updated

by the use of a particle ﬁlter. Karasev models the

behavior of pedestrians as Jump Markov process in

(Karasev et al., 2016). The possible goals are prede-

ﬁned and are not sensitive to a changing environment.

For prediction, he uses a Rao-Blackwellized particle

ﬁlter.

Even though short term predictions for trafﬁc par-

ticipants based on their kinematic are rather reliable

(more so for trafﬁc participants with non-holonomic

constraints, than trafﬁc participants with holonomic

constraints), for long term predictions the kinemat-

ics are not sufﬁcient anymore. Long term predictions

require the usage of context knowledge. Several con-

cepts show us, how we can calculate reliable features

Kerscher, S., Balbierer, N., Kraust, S., Hartmannsgruber, A., Müller, N. and Ludwig, B.

Intention-based Prediction for Pedestrians and Vehicles in Unstructured Environments.

DOI: 10.5220/0006679103070314

In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 307-314

ISBN: 978-989-758-293-6

307

from the vehicle dynamics or the surrounding and use

them for the prediction of the prospective motion. Ex-

amples for those concepts are (Gindele et al., 2013)

or (Tang et al., 2015). The prediction task in well

structured environments is not easy, but it is unlikely

harder in unstructured environments without lanes,

lane markers or any trafﬁc guidance. In this paper,

we understand wide places, pedestrian areas or park-

ing lots as unstructured environment, for example.

Or to deﬁne it in a general way: As unstructured

environment, we consider places where no clear de-

ﬁned infrastructure guides the movement of the trafﬁc

participants, or they are not known by the autonomous

system. Without this context knowledge, the calcula-

tion of various features gets impossible and some ma-

neuvers, like changing the lane, are getting invalid if

there is no lane anymore.

For those depicted problems in the ﬁeld of mo-

tion prediction, we present an approach which uses

heuristics to ﬁnd possible target points of trafﬁc par-

ticipants and thus narrowing the solution space for

future motions. Subsequently, a discriminating deci-

sion process decides the most probable aim of the ob-

ject. With the inference of target points we are able to

predict possible future motions of trafﬁc participants

(e.g. pedestrians), bypassing the lack of context infor-

mation.

2 PROPOSED APPROACH

In the proposed approach, we model the intention

recognition task as a goal-driven process and predict

the dynamic objects towards those goals. A pseudo

algorithm is given in Algorithm 1. Trying to reduce

the complexity of the intention recognition task, we

apply appropriate heuristics to ﬁnd a rough estimation

of possible goals, called target points. This happens

in line two in Algorithm 1. Subsequently, we are cal-

culating, in line three to six, target point related fea-

tures for every point and evaluate with a classiﬁer, if it

could be the true goal. In the next step, we choose the

most probable result of our classiﬁcation step, which

is now regarded as the future goal of the considered

object. For unknown objects, an initialization step

is executed, where a ﬁrst shortest path trajectory is

planned to the calculated aim and the covariance ma-

trix is initialized. In the following step, we predict

the object towards its most probable target point. The

prediction step itself is divided into several steps and

described in detail in 2.4.

The mentioned steps of the presented algorithm

are now described in detail.

Algorithm 1: Target point intention recognition algorithm.

1: for all Obj do

2: T P = updateTargetPoint()

3: for all T P do

4: Feat = calcTargetPointFeatures()

5: doIn f erence()

6: end for

7: t p

best

= chooseBestTargetPoint()

8: if Obj != known then

9: initPrediction()

10: end if

11: [Pos,CoVar] = predictOb ject()

12: end for

2.1 Target Point Generation

The purpose of the generation of target points is the

reduction of the possible goals of a regarded object

using heuristics. In Algorithm 1, this task is done in

line two. Without reduction, the amount of possible

goals is inﬁnite and the calculation of the most prob-

able one impossible. For the true target of the traf-

ﬁc participant to be in the domain of possible targets,

their number and distribution is crucial, otherwise the

predictions will be misleading and counterproductive.

Is the distribution of target points too dense, more op-

tions are matching with the true aim and the calcu-

lation process is getting very costly. Therefore, our

target points have different origins. As a conservative

safety measure, we search for possible target points

(safety points T P

Sa f

) of trafﬁc participants that dan-

gerously interfere with the motion of the ego vehicle.

Further, we consider possible midterm goals of trafﬁc

participants and place the target points (motion points

T P

Mot

) in such a way that a natural motion pattern is

enabled. The last source of target points is the usage

of special points of interest (T P

PoI

) like crosswalks,

pedestrian lights or bus stations. This leads to a de-

ﬁned set of target points

T P

sum

= T P

Saf

+ T P

Mot

+ T P

PoI

(1)

Further, the heuristics for ﬁnding appropriate

target points are explained in detail.

2.1.1 Target Points from Safety Relevant Motion

Hypothesis

To ensure safety we have to look for target points of

trafﬁc participants that would dangerously interfere

with the ego motion. So we generate a target point in

such a way, the regarded object has to cross the future

trajectory of the ego vehicle. If an object is already

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

308

located inside the area around the future trajectory of

the ego vehicle, we have to check if, and in which

direction the object will leave this area. So we get

T P

Sa f

= T P

crossing

+ T P

leaving

. (2)

Crossing Future Ego-Trajectory

Figure1 shows the generation of the target point. In

the simplest form, the possible target point of the

pedestrian lies perpendicular on the opposite side of

the ego trajectory. This method is also possible if we

are moving in totally unstructured environment. Is

more information available, like the lane markings or

walkways, we can set TP

crossing

at the opposite side of

the lane or on the opposite walkway respectively.

Figure 1: Generation of the crossing points.

Leaving Ego Trajectory

Similar to the generation of the crossing target point,

we generate two target points on the left and right

side of the trajectory, if the object is located inside

the driving path of the vehicle. This can be seen in

Figure2. With those two points, we can detect the di-

rection, the object is leaving the ego path.

Figure 2: Generation of the leaving points.

2.1.2 Target Points from Common Motion

Hypothesis

In many cases it is too ambitious to recognize a spe-

ciﬁc goal for a dynamic object. One reason might be,

that the real goal of the dynamic object is far away and

its only midterm goal is to follow the road for a while.

In those situations, we must offer basic motion hy-

pothesis to the intention recognition process, too. We

propose ”Staying at place”, ”Moving along the course

of the ego motion” or ”Leaving the regarded area”

as those basic motion hypothesis. Those three types

form the set of the motion hypotheses target points

T P

Mot

= T P

Stay

+ T P

Mac

+ T P

Lra

(3)

Staying at Place

The target point for staying at place is set to the cur-

rent place of the dynamic object and is held there,

since the object did not move away for a certain dis-

tance. If inequation (4) is fulﬁlled, the location of this

target point is updated.

t p

− X

ob j

| > d

max

(4)

where |X

t p

− X

ob j

| means the euclidean distance from

the target point to the position of the dynamic object

and d

max

the maximum permitted deviation from the

generated target point.

Move Along the Course of the Ego Motion

One important task for the ego vehicle is to recognize

dynamic objects, which are following the same way

or are approaching the ego vehicle. We ﬁrst assume

sane behavior and set target points in a way, that the

dynamic object can pass by the ego vehicle or follow

the road on the left and right side. The distance from

the considered object to the assumed target points is

very important, because this will affect features that

are calculated from the target point, for example the

relative heading to the target point.

Figure 3: Generation of the target points for an object which

is moving along.

Leaving the Regarded Area

Taking the explained target points into account, an ob-

ject can move around the ego car, cross, leave or fol-

low the ego trajectory, but we still have a blind spot,

the dynamic object cannot move to. If the object is

located on the right side of the driving path of the ego

vehicle, it has no chance to leave the regarded area to

the right side. In this case, a target point is set on the

Intention-based Prediction for Pedestrians and Vehicles in Unstructured Environments

309

left side of the vehicle’s path, in case the object wants

to cross. But we must also set a target point on the

right of the regarded object, so it has a potential goal

on this side.

2.1.3 Target Points as Points of Interest

Points of interest are spots which are attractive for

dynamic objects. We have to distinguish online be-

tween different types of objects. Points of interest

are detected by the sensor system or entered into a

map ofﬂine. Examples for points of interest in case of

pedestrians might be crosswalks, bus stations of traf-

ﬁc lights. For vehicles those points might be parking

lots or gateways.

2.2 Feature Calculation

One big problem in intention recognition of pedestri-

ans or vehicles in unstructured environments is ﬁnd-

ing meaningful features that give hints for the future

movement of the object. This task is difﬁcult, because

we have no lane markers or other distinctive points to

infer information from. In this proposed method, we

use target points as point of reference and we calcu-

late target point relative features between every object

and its associated target points. Additional features

are derived from the position of the generated target

point and its surroundings. Useful features are ex-

plained subsequently.

• Is the tp located in an area designated for this

class?

We assume, that all classes prefer using the space

which is designated for them. A pedestrian for

example can cross every street or motorway, but

will usually prefer walkways.

• Is the tp a point of interest?

Special target points are points of interest. They

bear stronger attraction to the dynamic objects,

because of a special function or use like cross-

walks or trafﬁc lights.

• Is the tp blocked by law?

This feature shows, if the object is breaking law at

the attempt of reaching this target point.

• Is the tp blocked by an object?

If an object cannot reach a target point, because

another object blocks the way to it, the object will

search a different way to its goal.

• Is the tp a stay point?

This feature is true, if the target point is recog-

nized for a standing object

• What is the velocity towards the tp?

We calculate the velocity component which is

pointing towards the target point, as a feature.

• What is the acceleration towards the tp?

The same as described for the velocity is done

with the acceleration. The component of the ac-

celeration, which points towards the target point

is used as a feature.

• What is the heading towards the tp?

The angular difference between the heading of the

object and the direct line to the considered target

point.

• What is the yaw rate towards the tp?

The yaw rate towards the target point is used as a

feature.

2.3 Decision Making

After explaining the heuristics that lead to our target

points, we now show the process of ﬁnding the most

probable goal of a dynamic object. For this task, we

use a Naive Bayesian Classiﬁer to estimate the possi-

bility for the considered point to be the future goal of

the object.

For the classiﬁcation, a Naive Bayesian Network

is applied, see Figure 4, which is fed with target point

related features. The Bayesian Network was designed

with the SMILE Engine and the GeNIe Modeler from

(LCC, 2017). The process is done for every target

point, which is associated with the regarded dynamic

object. All used features are calculated online. The

features described in 2.2 are used as inputs of the

Bayesian Network. After the inference process, we

rate the target points of every dynamic object. Before

we can predict the movement of the object, we have

to choose the most probable target point.

Figure 4: Naive Bayesian Net (LCC, 2017).

2.4 Trajectory Planning and Position

Estimation

After concluding the future goals of an object, we

have to predict its movement. To accomplish this task,

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

310

we developed a method which combines two meth-

ods. A simple linear prediction with the method of the

shortest path, calculates a trajectory from the current

position of the object to the calculated target point.

This is derived from the assumption, that people will

likely take the shortest path (Hoogendoorn and Bovy,

2004). For now the interaction with other obstacles

and objects is neglected, but is intended to be imple-

mented in a future increment of the algorithm. Fur-

ther, we want to estimate the real position of the object

by applying a Kalman Filter. By this way, we also get

an estimation of the uncertainty of the path, the dy-

namic object will take. To make the algorithm work,

we need an initialization step for new objects shown

in 2.4.1. For known objects, only the prediction step

from 2.4.2 is executed.

2.4.1 Initialization and Shortest Path Trajectory

Planning

In the initialization phase of a new object, we have to

calculate the shortest path to the chosen target point

by applying a constant velocity model. Additionally,

we have to set the values of our Kalman system for the

ﬁrst time. Subsequently, the calculation of the short-

est path is described.

i+1

= X

+ T

delta

∗V

pred

(5)

when V

pred







meas

|cos(phi)

meas

|sin(phi)



(6)

meas

is the measured velocity vector of the dy-

namic object. V

meas

is turned until it points to the cho-

sen target point and is now named V

pred

. Starting from

the current position of the object, the trajectory points

are calculated by adding the product of V

pred

with the

temporal interval T

delta

of the points. This process

is executed, until a trajectory point is reached, which

fulﬁlls following inequation

T P < T

delta

∗V

pred

(7)

2.4.2 Position Estimation and Uncertainty

In the second step of the prediction, the application of

the Kalman Filter delivers us a probabilistic estima-

tion of the true position of an object in the future and

estimates the occurring uncertainty.

The Kalman Filter

The application of a Kalman ﬁlter incorporates two

steps. When we conduct the prediction step, the cur-

rent states ˆx of a system are extrapolated into the fu-

ture by applying a model F of a process. Also the co-

variance matrix is predicted by this model and an ad-

ditive component Q, which models the process noise.

The relevant equations are shown below.

ˆx

k|k−1

= F ˆx

k−1

(8)

k|k−1

= F

k−1

+ Q

k−1

(9)

In the second step, an update can be performed, if

there are new measurements of our state available.

For updating the position of our state, we perform

equation (10), which needs the Kalman gain

from

equation (14) and the Innovation ˜y

from equation

(12). The Innovation is calculated from a difference

of the new measurement and the current system state

and the measurement matrix H. The Kalman gain is

calculated by multiplying the covariance matrix the

transposed measurement matrix and the inverse In-

novation covariance S

. The Innovation covariance

is a kind of summary of the uncertainty of the mea-

surement data. It is calculated from the measurement

matrix and the estimated covariance

k|k−1

which is

added to the sensor noise matrix R

. The update of

the estimated covariance matrix

is shown in equa-

tion (11).

ˆx

= ˆx

k|k−1

˜y

(10)

k|k−1

−

(11)

˜y

= z

− H

ˆx

k|k−1

(12)

= H

k|k−1

+ R

(13)

k|k−1

−1

(14)

For our application, we want to use the beneﬁts of

the Kalman ﬁlter, like the state estimation dependent

on measurements and the corresponding uncertainty.

But we also want to use our recognized intention for

enhancing the state estimation. To grasp both advan-

tages in one method we now present our approach.

Combining Path Planning and State Estimation

In this paragraph, we explain how path planning and

state estimation can be combined to get a sound pre-

diction. A pseudo algorithm is shown in Algorithm 2.

The steps of the initialization phase from line one to

ﬁve are already explained in 2.4.1. How the algorithm

works can also be seen in the Fig. 6 till Fig. 8. In the

top left ﬁgure ﬁve, we can see an object which is rec-

ognized for the ﬁrst time. The measured position is

. A shortest path trajectory was planned to its most

probable goal and the uncertainties are initialized, vi-

sualized by the ellipses around the trajectory points.

In the next time step k = 1 we will get a new mea-

surement from the vehicle’s state, which is X

, shown

Intention-based Prediction for Pedestrians and Vehicles in Unstructured Environments

311

as a red cross in Fig. 6. For a later usage, we need

the point from the shortest path trajectory of the last

step at the time of the new measurement X

. Even

though the points in the shortest path trajectory are

ordered isochronal, a new measurement is most likely

not taken at the time of a predicted trajectory point.

For that reason, we have to interpolate between these

points. This is done in line nine in the pseudo al-

gorithm. The interpolated point X

1int

is shown as a

brown cross in Fig. 6.

The interpolated point is set as the new Kalman state

in line ten. As last step in the update phase, we exe-

cute the Kalman update step, with the new measure-

ment as input. As a result, we get the updated vehicle

state between the interpolated state and the measured

value. In the bottom left Fig. 7, we can see the up-

dated point as green point X

1up

Algorithm 2: Object prediction algorithm.

1:  Initialization phase

2: k = k

3: setKalmanMatrices()

4: shortPathTra j

= calcShortPath(X

)

6:  Iteration phase

7: while algorithm is running do

8:  Update phase

9: X

= getMeasurement()

10: interpPoint

11: interpTemp(X

, shortPathTra j

k−1

)

12: setKalmanState(interpPoint

)

13: X

= per f ormKalmanU pdate(X

)

14:

15:  Prediction phase

16: updateSystemNoise(X

)

17: shortPathTra j

= calcShortPath(X

)

18: for all Points in shortPathTra j

19: CoVar = calcKalmanCovarPrediction()

20: end for

21: k = k + 1

22: end while

The ﬁrst step in the prediction phase, which is also

shown in Fig. 7 is the update of the process noise ma-

trix Q. By changing the process noise matrix, we want

to model a changing uncertainty of the position of the

object. As measurement of the uncertainty, we use the

difference of the current position measurement X

and

the former predicted position of the object at the cur-

rent time X

kint

. So we get for the new process noise

matrix









| x

kint

− x

kup



0 0 0

0 f



| y

kint

− y

kup



0 0

0 0 f



| ˙x

kint

− ˙x

kup



0 0 0 f



| ˙y

kint

− ˙y

kup









(15)

Figure 5: Initialization. Figure 6: New measurement

and interpolation.

Figure 7: Kalman and pro- Figure 8: New prediction.

cess noise update.

Modeling Q in this way, we get a bigger covari-

ance matrix, if the measured position differs a lot

from the predicted. As a consequence we can show,

the uncertainty is growing if the object is leaving the

predicted trajectory. Therefore, the prediction is not

trustworthy anymore. Is the object moving along the

trajectory, the uncertainty stays small.

In Fig. 8, the steps in line 16 till 19 in the pseudo

algorithm are visualized. The green crosses represent

the new planned shortest path trajectory from line 16

in the pseudo algorithm, to the chosen target point.

After the calculation of the positions, the prediction

step of the Kalman ﬁlter is executed for the covari-

ance for every point in the trajectory. This delivers

us the uncertainty in every trajectory point, which is

displayed as green conﬁdence ellipses around the tra-

jectory points in Fig. 8. These steps are processed for

every new measurement and therefore generate a sen-

sitive prediction of the future movement of an object

with a meaningful uncertainty estimation.

3 APPLICATION TO DIFFERENT

DYNAMIC OBJECTS

The presented approach should serve as a framework

for predicting the future motion of various objects. In

Fig. 9, you can see the main blocks of the framework

and the realized form of this work. Within this paper,

we present the application of this structure onto a pre-

dicting algorithm for pedestrians in an unstructured

environment.

Therefore, we used the described heuristic meth-

ods for target point generation and the shortest path

trajectory for path planning. For different objects or

in a better known environment, we can choose our

target point in a different way and plan the trajectory

with other algorithms. This modular design allows us

to use better ﬁtting algorithms if the situation changes,

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

312

Figure 9: Modular setup of the prediction algorithm.

or respectively more information about the environ-

ment or the objects is available.

4 CONCEPT TEST WITH REAL

WORLD DATA

The presented concept was evaluated with real world

data, recorded in Frankfurt, Germany. Within this real

data test, we wanted to evaluate the presented con-

cept in terms of intention recognition for pedestrians.

As data source, a Continental MFC400 camera sen-

sor was used, which delivered classiﬁed objects and

the relevant input data for the prediction task. In Fig.

10, we can see the camera image atop and a bird’s eye

view graphic below. In the shown scene, a truck is

driving along a street and a pedestrian is going on a

walkway in the same direction. In the bird’s eye view,

we can see the truck as big red object with id 0 and

the pedestrian as red dot with id 1. The ego vehicle is

drawn as green rectangle. The intention recognition

process here is done for the pedestrian on the right

side and so the brown target points are associated to

it. We can see seven target points, whereby ID97 rep-

resents the target point which would be the goal, if the

pedestrian will stay at place. Four target points are

modeling the possibility that the pedestrian is follow-

ing the course of the ego vehicle. Those points are

ID98, ID99, ID100 and ID101. Target point ID102

would be the possible goal if the pedestrian tries to

cross the ego vehicle’s path. ID103 completes the set

for this object and would be the goal, if the pedes-

trian would like to leave the relevant area to the right.

As most probable target point, the point with id 100

is chosen and a trajectory is planned towards it. In

this concept evaluation test, we did not use all in-

puts of the Bayesian Network from Fig. 4. We as-

sumed a poor description of the environment and so

we only used the relative heading, velocity and the in-

formation whether the considered target point is a stay

point. Underneath this picture, the results for the esti-

mation of the most probable target point is visualized.

For each target point, there is depicted the course of

the percentages, if it was considered as the true aim.

In the bottommost ﬁgure, again a bird’s eye view is

shown, which depicts the error ellipses for this cur-

rent timestamp.

Figure 10: Intention recognition example with target points,

shortest path and uncertainty estimation.

5 CONCLUSIONS

In this paper, we presented a novel goal oriented ap-

proach for the task of intention recognition for dy-

Intention-based Prediction for Pedestrians and Vehicles in Unstructured Environments

313

namic objects. Possible goals are set by applying

heuristics from the human motion, trying to cover the

natural motion of humans. Additional goals are intro-

duced to recognize safety relevant motions of the ob-

ject or including frequently visited goals of humans.

The following path planning and prediction of the

movement combines the advantages of state estima-

tion and intention recognition methods. This gives us

a robust estimation of the possible trajectory and the

uncertainty of the calculated path of the considered

object. For future applications of this concept, an en-

vironment classiﬁcation has to be taken into account

for a better placement of the target points. Also the

evaluation of the target points should be more envi-

ronment and context sensitive. In the path planning

task, other models should be tested to include more

information like the used time or the danger of differ-

ent possible ways to the target point. Also the inter-

action between different dynamic objects should be

modeled in the future.

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