Future Parking Applications: Wireless Sensor Network Positioning for

Highly Automated in-House Parking

Andrea Jung

, Paul Schwarzbach

and Oliver Michler

Institute of Trafﬁc Telematics, Technische Universit

at Dresden, Germany

Keywords:

Future Parking, Automated Parking, Wireless Sensor Network WSN, Markov Localization.

Abstract:

One of the bottlenecks for motorized individual transportation for end-to-end trips is the search for parking

space. Common solutions to minimize spatial needs are in-house parking garages, but even in those, ﬁnd-

ing available parking lots can be quite time consuming. In this contribution we therefore present a cheap

and retroﬁttable parking system, enabling automated entrance to parking lot reservation, navigation and clear-

ing for already existing parking garages. One of its key component is a robust indoor positioning based on

Wireless Sensor Networks (WSN) enabling vehicle independent and automated routing. We will provide a

general overview of WSN measurement principles and propose two possible technology candidates, a 2.4

GHz narrow-band technology and Ultra-Wide Band (UWB). Furthermore, a robust range-only positioning

approach utilizing Markov Localization, called Probability Grid Positioning (PGP), is presented. With the

help of UWB and IEEE 802.15.4 ranging modules the algorithm is qualitatively evaluated with measurements

in a car park in Leipzig, Germany. Our proposed PGP approach leads to overall smoother trajectories com-

pared to a state-of-the-art Least Squares Estimation (LSE) and thus achieves accurate and robust positioning

in demanding heavy-multipath environments. This can build the foundation for future work in the ﬁeld of

highly-automated in-house parking.

1 INTRODUCTION

Mobility is one of humanity’s fundamental needs.

With a worldwide constant increase of urbanization

energy-, time- and space-efﬁciency for transportation

processes are indispensable. For motorized individual

trafﬁc, the search for parking space is the bottleneck

of efﬁcient source to sink tours in urbanized areas.

Many kilometers are covered by a car driver in search

of free parking lots every year. For this reason, the fo-

cus of Future Parking solutions is becoming increas-

ingly important in research and development.

Since parking space on streets or open areas are

extremely limited in cities, parking garages or under-

ground car parks are often provided alternatively. For

indoor parking applications, there are approaches that

use Wireless Sensor Networks (WSN) to support the

driver in ﬁnding a parking space up to fully automated

parking approaches where the driver simply leaves the

vehicle in front of the parking garage (Friedl et al.,

2015).

https://orcid.org/0000-0003-1019-6134

https://orcid.org/0000-0002-1091-782X

Figure 1: System overview.

(Ibisch et al., 2013) present a system for localization

and tracking of vehicles in a parking garage using

a network of Light Detection and Ranging (LIDAR)

sensors. These LIDAR sensors, which are embedded

in the environment, are adjusted near the ground and

parallel to the ground level to enable measuring points

on the wheels as a basis for detection. An advantage

of the system is the accuracy on wide lanes, while

other sensors fail due to their limited range.

710

Jung, A., Schwarzbach, P. and Michler, O.

Future Parking Applications: Wireless Sensor Network Positioning for Highly Automated in-House Parking.

DOI: 10.5220/0009891107100717

In Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2020), pages 710-717

ISBN: 978-989-758-442-8

(Crisostomo et al., 2019) proposed an image-

processing based smart parking system for multi-

storey parking garages. The system was validated

with several video-feeds from different in-house park-

ing garages. It determines whether the parking lots

are occupied by indicating a red outline if a car is oc-

cupying a parking space and then turns green when it

is unoccupied.

An approach using light sensors to determine the

presence of a vehicle is presented in (Srikanth et al.,

2009). The WSN subsystem forwards the information

to a management server which transmits the occu-

pancy information of the parking spaces to the guid-

ing nodes and entrance system. A full-featured proto-

type model was implemented in order to validate the

parking management system which includes also the

availability of reservation.

Fully automated car parking relies often on me-

chanical handling. (Eswaran et al., 2013) proposed

a lift mechanism which transports the car to an allo-

cated parking lot. Various sensors, motors and soft-

ware are necessary to detect and transport the car.

The disadvantage of these systems is the initial cost

of building such parking garages. Another approach

uses the conventional concrete garages that can be

transformed into an automated parking system with-

out the deployment of sensors in each of the parking

lots (Nayak et al., 2013). The presented technology

uses robotic valets, which are the vehicle carriers that

park them compactly in a given space of the parking

area.

While these approaches and parking garages us-

ing robotic valet parking systems in general are very

promising in regards to time and space efﬁciency,

they are usually newly built at high costs. Hence, we

present an approach in which existing parking garages

can be retroﬁtted cost-effectively with an entrance to

parking lot booking and navigation system, enabling

a completely new car park management concept. Fig-

ure 1 gives an overview of the in-house parking tech-

nology with an automated booking system. In ad-

dition to the goal of intelligent control of parking

garages, the inner-city trafﬁc ﬂow is also to be con-

trolled. On the basis of this technology, for exam-

ple, parking spaces can be sublet or micro-hubs can

be implemented for inner-city logistics. Speciﬁcally,

a radio-based indoor localization technology is being

developed, whereby vehicles are navigated from the

barrier at the entrance to the booked parking space

using a Sensor-Tag (Coin) (cf. ﬁgure 2) and a Parking

App. Only the infrastructure and no vehicle informa-

tion is used with this technology. The great advan-

tage of this approach is therefore the independence

from different vehicle features and the possibility to

also deploy the system with so-called legacy vehicles.

Via the background system the car park operator can

monitor the current parking space occupancy and the

current events in the car park in real time using the

monitoring portal.

Figure 2: Test vehicle with sensor tag on the dashboard.

Next to presenting the previously described system,

this contribution discusses different technologies for

WSN indoor positioning, as a robust positioning is a

key feature for navigation within the parking garage.

For applications with high demands in terms of accu-

racy, range-based approaches (cf. section 3) are often

utilized, directly measuring distances between ﬁxed

anchors and mobile tags. In our conducted work, we

discuss both the usage of Ultra-Wide Band (UWB)

and a narrow-band technology at 2.4 GHz for the de-

scribed application. Furthermore, a robust position

estimation scheme is presented, utilizing Markov Lo-

calization, also referred to as Probability Grid Posi-

tioning (PGP), which is compared to a state-of-the-art

positioning method, revealing its superiority in terms

of accuracy.

The rest of the paper is organized as follows: sec-

tion 2 provides an overview of the proposed auto-

mated in-house parking system, followed by section

3 giving an overview on available measurement prin-

ciples to obtain spatial information within a WSN as

well as the discussion on possible technology candi-

dates as a basis for robust indoor positioning. Finally,

section 4 introduces a robust state estimation based

on a presented Markov Localization approach. The

paper concludes with a summary and proposals for

future research work in section 5.

2 SYSTEM OVERVIEW

In this section we provide an overview of the pro-

posed automated in-house parking system in detail

Future Parking Applications: Wireless Sensor Network Positioning for Highly Automated in-House Parking

711

especially about the necessary infrastructure and in-

formation technology. A general overview of our ap-

proach gives ﬁgure 1 as already described in section

The WSN as basis for the robust indoor position-

ing consists of ﬁxed anchors distributed in the parking

garage and mobile tags which are issued to the driver

at the barrier. Figure 3 shows the physical architecture

of our proposed automated in-house parking system.

The anchors are linked to multiband antennas via a

serial interface. This opens up the possibility of cov-

ering several frequency bands with a single radiating

element. Details about the WSN including its special

aspects are described in the following sections.

WSN

Anchors

Multiband

Antenna

Anchors Anchors

Multiband

Antenna

Multiband

Antenna

Access

Technique

Computing Unit

Positioning

Computing Unit

Map Data

Computing Unit

Parking Garage

Management

Tags

Computing Unit

App-Portal

Serial

Communication Link

WSN

Vehicles

Figure 3: Physical architecture.

Besides the WSN and its components there are dif-

ferent computing units for the automated processes.

Each mobile tag transmits his radio-frequency data

over a central Access Point (AP) to the positioning

computing unit where the sensor information is used

to estimate the position of the vehicle. A possible ap-

proach to robust positioning is proposed in section 3.

The computing unit for the map data is responsible

for the upper-level navigation process from the bar-

rier to the booked parking lot. In addition to the static

and dynamic map data of the entire parking garage,

the trajectories to each parking space for the route

guidance are also stored in this unit. Robust and pre-

cise positioning is an essential requirement for au-

tonomous driving. With a digital map, the errors of

a positioning system can be compensated by suitable

map matching algorithms. For the whole booking and

reservation process as well as the monitoring of the

parking space occupancy the management computing

unit is used which controls the access technique based

on all processed events.

The communication link (e.g. WiFi or in the fu-

ture 5G) between the individual subsystems offers

a reliable communication to provide centrally pro-

cessed position data in the vehicles. This enables a

coupling with the navigation system of the vehicle for

future automated processes.

3 INDOOR POSITIONING:

WIRELESS SENSOR

NETWORKS

For indoor applications speciﬁcally, the go-to posi-

tioning technology Global Navigation Satellite Sys-

tem (GNSS) is typically not available or its perfor-

mance is degraded up to the point, where reliable po-

sition estimation is not possible anymore. Therefore,

different technologies have to be exploited. Since this

contribution presents a system which is intended to

be retroﬁtted in existing parking garages and indepen-

dent of any sensor information provided by customers

and their cars, only several approaches are applicable.

3.1 WSN Localization

As already stated, the proposed future parking sys-

tem aims to provide a retroﬁttable and vehicle in-

dependent indoor positioning system, leading to re-

strictions for localization baseline technology selec-

tion as on-board sensors like Inertial Measurement

Units (IMUs) cannot be used. Similar to GNSS, WSN

can determine geometric relations utilizing transmit-

ted and received signal properties. Figure 4 gives an

overview of different principles to obtain geometric

relations within a WSN.

Spatial Information in WSN

Distance

Runtime-based

PoA

RSS

Angle

AoA

Proximity

Connectivity

based

Figure 4: Overview of different measurement principles in

WSN used to obtain spatial information.

In general, WSN localization approaches are clas-

siﬁed between range-based and range-free methods

(Mendoza-Silva et al., 2019). Range-free methods

mainly include proximity based methods like Cen-

troid or DV-HOP (Paul and Sato, 2017). The main

advantage of these approaches are their cheapness,

low complexity and energy efﬁciency. However, the

resulting accuracies are highly dependent on various

ICINCO 2020 - 17th International Conference on Informatics in Control, Automation and Robotics

712

inﬂuences like network topology and density of nodes

(Mesmoudi et al., 2013).

We therefore focus on range-based methods,

which generally include both distance and angle mea-

surements as input information. Since the incor-

poration of additional hardware like antenna arrays

are necessary for Angle of Arrival (AoA) estimation,

these are not considered for our described use case.

3.2 Range-only Localization

The most common localization applications for WSN

include distance based methods. Up ﬁrst, Received

Signal Strength (RSS) measurements can easily be

used as they are provided by almost all radio devices.

The concept behind this approach is to use channel

models for the occurring path loss considering known

transmission power and hardware (e.g. antenna gain)

and estimating distances in comparison with the mea-

sured RSS. However RSS distance measurements are

not very robust as the environment has a large impact

on possible fading occurrences (slow and fast fad-

ing). Additionally, channel modelling can be a dif-

ﬁcult task. To overcome these downsides, RSS proﬁl-

ing or ﬁngerprint approaches can be applied, creating

a map with signal strengths for different locations be-

forehand. However, accuracies are still ﬂuctuant es-

pecially in challenging environments (cf. ﬁgure 5).

The measurement of the phase shift, also known

as Phase of Arrival (PoA), is another common method

for determining the distance between two sensor

nodes. One advantage of this method is that the

transceivers do not need to be synchronized in time

(Bensky, 2008). The PoA concept makes use of the

phase difference of two different frequencies caused

by a signal propagation delay and estimates the dis-

tance between the two sensors with their transceivers.

The PoA method is extremely suitable for applying

ranging for narrow-band radio-frequency transmis-

sion systems.

Runtime-based distance measurements include a

variety of measurement principles, such as Time of

Arrival (ToA), Time Difference of Arrival (TDoA),

Two-Way ToA or Symmetrical Double-Sided Two-

Way Ranging (SDS-TWR). The distance measure-

ment in ToA procedures is made possible by deter-

mining the signal propagation time (Bensky, 2008).

Such a Time of Flight (ToF) measurement is per-

formed by recording different time stamps of trans-

mitted and received messages. This requires precise

synchronization between transmitter and receiver. To

avoid this source of error, the Two-Way ToA method

is based on measuring the signal propagation time

twice based on an asynchronous and asymmetrical

process. The SDS-TWR method can be seen as an ex-

tension of the Two-Way ToA. In contrast, it is based

on a symmetrically initialized measurement of the

distance twice, thus on a total of 3 messages (Jiang

and Leung, 2007).

3.3 Used Hardware for Validation

To validate the robust range-only approach proposed

in detail in section 4 we use two different WSN tech-

nologies. Table 1 shows a comparison of the 2.4 GHz

narrow-band technology from Metirionic (Metirion-

icGmbH, 2020) and the 6.5 GHz UWB MDEK1001

evaluation kit from Decawave (Decawave, 2017).

Table 1: Comparison of used WSN technologies.

2.4 GHz UWB

Standard IEEE 802.15.4 IEEE 802.15.4a

Frequency

Range

2400-2483.5

MHz

6240-6739.2

MHz

Point-

to-point

range

up to 1 km up to 60 m

(RTLS: 25 to

30 m)

Ranging

method

PoA, Advanced

ToA

Two-Way ToA

Hardware Demo-Kit Me-

tirionic GmbH

Decawave

MDEK1001

Both technologies are based on the IEEE 802.15.4

wireless standard. In contrast to UWB, the 2.4 GHz

Metirionic technology can be regarded as a narrow-

band wireless technology with 83 MHz bandwidth

(MetirionicGmbH, 2020). Its limited bandwidth

poses great challenges in determining the distance be-

tween two sensor nodes compared to UWB with about

500 MHz. However, the big advantage of the technol-

ogy is the long range and functionality in Non-Line-

of-Sight (NLOS) environments as well as in complex

environments (e.g. through several walls). Due to

the lower transmission power, the devices from De-

cawave have a smaller point-to-point range from up

to 60 m (Decawave, 2017). By using the Real-Time

Location System (RTLS) in combination with Blue-

tooth to access the raw data, this is limited to 30 m.

Future Parking Applications: Wireless Sensor Network Positioning for Highly Automated in-House Parking

713

4 ROBUST POSITIONING FOR

IN-HOUSE PARKING

4.1 Problem Formulation: Multipath

Environments

The quality of the distance measured using radio-

frequency techniques is highly dependent on the envi-

ronment and subject to certain propagation phenom-

ena, including Line-of-Sight (LOS), NLOS or multi-

path reception, leading to different effects in the mea-

surement domain. The effects of these occurrences

highly differ between possible technologies used for

obtaining distance information between anchors and

mobiles. An example for the variety of possible mul-

tipath reception paths in an parking garage obtained

by a radio wave simulation tool is depicted in ﬁgure 5.

The mobile tag is located between two parking rows

and is in a NLOS relation to one anchor mounted on

the ceiling.

Figure 5: Selective multipath reception between two park-

ing rows.

Without loss of generality, a ranging measurement R

is given as the sum of the true distance d

between a

tag t and an anchor a deﬁned as d

− x

and

occurring measurement errors ε:

R = d

+ ε (1)

with x

= [X ,Y, Z]

and x

= [X , Y, Z]

representing

the three-dimensional tag and anchor positions in a

local cartesian coordinate system.

For ToF based distance measurements and with

respect to the previously mentioned types of signal

reception, ε includes the following possible distance

measurement errors:

• Gaussian (white) noise for LOS,

• (positive) ranging errors caused by NLOS,

• positive and negative ranging errors caused by

multipath interference and

• gross outliers.

These effects are tied to speciﬁc occurrence probabil-

ities, which differ depending on employed technolo-

gies as well as surroundings (e.g. UWB vs. narrow-

band technology or open space vs. indoor), but gen-

erally lead to non-gaussian measurement residuals.

These arbitrary or sometimes even multi modal mea-

surement distributions strongly degrade the perfor-

mance of state estimation approaches which rely on

normally distributed errors, such as Least Squares Es-

timation (LSE) or Extended Kalman Filtering (EKF).

To represent any non-gaussian and heavy-tailed

ranging residuals resulting from the occurences of the

described error types, we use Gaussian Mixture Mod-

els (GMM), which can approximate any type of arbi-

trary distribution using a variable amount of C Gaus-

sian components associated with different weights w,

means µ and variances Σ (2).

P ∼

∑

· N (µ

, Σ

) (2)

Figure 6 shows an exemplary heavy-tailed ranging

residual histogram as well as an approximation of the

underlying probability distribution using a GMM.

Figure 6: Histogram of heavy-tailed ranging residuals

(green) in an indoor environment, including a GMM

approximation of the underlying probability distribution

(blue).

4.2 Probability Grid Positioning

Based on the presented distance measurements, we

want to present a possible approach to robust position-

ing using a classical Markov Localization approach.

This method is a special kind of Recursive Bayes Fil-

ter (RBF), more speciﬁcally a Discrete Bayes Filter

(DBF), as it uses a discrete, ordered sample proba-

bilistic state space representation for state estimation.

Essentially, the presented Markov Localization real-

ization is a two-dimensional Histogram Filter (HF),

which is also referred to as PGP. The utilization of

a discrete state space representation over a Gaussian

(Kalman Filter) or a random sample (Particle Filter,

ICINCO 2020 - 17th International Conference on Informatics in Control, Automation and Robotics

714

PF) representation was explicitly chosen for the fol-

lowing reasons:

• NLOS or multipath reception is inherent in the de-

scribed parking garage scenario, leading to non-

gaussian observation errors. These drastically de-

grade the accuracy of conventionally used state

estimation approaches like LSE or EKF.

• The state space only has to cover the inside park-

ing spaces, leading to a bounded state space, fa-

cilitating DBF applications.

• As described in section 2, positioning for all vehi-

cles is performed at a central instance. Unlike PF,

PGP samples are immovable, which means the de-

ﬁned state space can be used for all participants.

The corresponding likelihoods or probabilities for

all grid cells are then stored individually.

Like any RBF respectively DBF implementation, the

presented approach follows a prediction-correction

structure as shown in ﬁgure 7. This structure esti-

mates the current state x

at timestep k with respect to

the last given state information x

k−1

based on Markov

assumption and Bayes’ Rule (Thrun et al., 2005).

Initialization

Prediction

k,i

Observation

k,i

Measurements

Estimation

ˆx

MAP

k = 0

k = 1 : N

Figure 7: Structure of PGP framework.

The simplest form of a sorted sample state space is an

equidistant grid (cf. ﬁgure 8b) (Burgard et al., 2011).

The predeﬁned grid cells represent possible realiza-

tions of the state and their size constitutes the trade-

off between computational complexity and achievable

accuracies of the ﬁlter.

The DBF prediction step p

k,i

for the i-th grid cell

at k can then be formulated as:

k,i

∑

P(x

k,i

k−1

k−1,i

∀i = 1, 2, . . . , n (3)

The probability density from (3) is then combined

with the observation z

Likelihood function P(z

k,i

)

and multiplied with a normalization factor η follow-

ing the Bayes’ theorem, resulting in the overall prob-

ability density representation at time-step k:

k,i

= ηP(z

k,i

(4)

(a) (b)

Figure 8: Visualization of a 2D distance measurement from

a known ﬁx point (yellow): (a) Explicit circular position

line. (b) Sample based representation given noise.

This is also reffered to as the current belief Bel about

the current state. The ﬁnal estimation of the current

vehicle position is obtained by maximizing p

k,i

, fol-

lowing the Maximum A-Posteriori estimation:

ˆx

= argmax

Bel(x

) (5)

Implementational details for PGP can be found in

(Thrun et al., 2005).

4.3 Performance Results

In this subsection we want to provide qualitative per-

formance results for both the introduced WSN tech-

nologies as well the performance of the proposed PGP

approach compared to a Gauss-Newton LSE. For this

purpose, several test drives in a parking garage in

Leipzig, Germany were performed (cf. ﬁgure 2). The

test run we are presenting went from the entrance of

the car park at the barrier at the top right side of ﬁgure

9 to parking space number 11, located at the middle

of the left side.

For the presented data set, two signiﬁcant results

are observable:

• UWB generally provides higher ranging accura-

cies and less environmentally induced outliers,

leading to overall higher positioning accuracies.

• The proposed PGP approach manages to mitigate

outliers for both technologies, leading to overall

smoother trajectories compared with LSE.

The achieved accuracy of UWB depends on the high

bandwidth and thus the higher temporal resolution to

distinguish the direct path from the others. Despite

this fact, the narrow-band technology from Metiri-

onic GmbH is the focus of our future research work.

Due to the higher coverage (cf. subsection 3.3) the

infrastructure devices and thus costs can be reduced.

One way to compensate the distance-dependent loss

of accuracy of the ranging measurements is the use

Future Parking Applications: Wireless Sensor Network Positioning for Highly Automated in-House Parking

715

(a) UWB. (b) Narrow-Band 2.4 GHz.

Figure 9: Comparison of UWB (a) and narrow-band 2.4 GHz (b) ranging technologies, including ﬁxed anchors points (green),

PGP estimation results (blue) and Gauss-Newton estimation (golden).

of beamforming antennas. In general, NLOS mea-

surements as well as multipath reception in a parking

garage (cf. Figure 5) can be compensated during po-

sition estimation with the PGP.

5 CONCLUSION

In this paper, we introduced a novel approach for

a highly automated parking for already existing in-

house parking areas, enabling efﬁcient parking garage

management for operators and time saving park space

search for customer. A core component of this system

is robust indoor localization. We have discussed the

demands for accessible and vehicle independent sen-

sor information, leading to the application of WSNs.

For these, we have discussed and introduced several

localization and measurement principles. Lastly, a ro-

bust probabilistic state estimation approach for range-

only localization is introduced and qualitatively eval-

uated for in-house parking usage.

For further work, we want to put more empha-

sis on necessary performances as well as system ar-

chitectural to build a foundation for fully automated

parking. Additionally, we will conduct a comprehen-

sive quantitative validation for different setups, tech-

nologies and test drives. This will also include more

details on method implementation and a comparison

with other state-of-the-art positioning methods. The

overall goal is to qualify WSN based localization as a

key enabler for automated in-house parking.

ACKNOWLEDGEMENTS

This work has been supported by the European Union

and the State of Saxony within the project ”IVS-

AMP”. Additionally, the authors would like to thank

all involved cooperation partners, especially Metiri-

onic GmbH.

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