Maneuver-based Adaptive Safety Zone for Infrastructure-Supported

Automated Valet Parking

Valerij Schönemann

, Mara Duschek

and Hermann Winner

Institute of Automotive Engineering, TU Darmstadt, Otto-Berndt-Straße 2, Darmstadt, Germany

Computational Engineering, TU Darmstadt, Dolivostraße 15, Darmstadt, Germany

Keywords: Valet Parking, Safety Zone, Automated Driving, Infrastructure.

Abstract: One of the major challenges for the release of fully automated driving is the design of safe vehicle automation

systems. This work presents a structure to determine a maneuver-specific and adaptive safety zone for

collision avoidance. For this, the overall automated driving system is split into functional scenarios that occur

during the driving task in the operational design domain. Maneuvers are derived from the given scenarios and

car park layouts. Minimum safety distances are determined by injecting worst-case parameters into derived

maneuvers. The superposition of these safety distances leads to a new term: the safety zone. The safety zone

adapts its size according to the performed maneuver as well as the dynamic driving parameters of the engaged

traffic participants such as velocities, timing constraints and deceleration capabilities. The methodology is

applied on the example of cooperative automated valet parking (AVP).

1 INTRODUCTION

The Non-Traffic Surveillance (NTS) data indicate

that from 2012 to 2014 around 5,700 people were

killed and 277,000 were injured in non-traffic crashes

in the US (Singh, 2016). According to the National

Highway Traffic Safety Administration (NHTSA)

non-traffic crashes are classified as single-vehicle

crashes on private roads, two-vehicle crashes in

parking facilities, or collisions with pedestrians in

driveways. Thereby, an average of 42% of the

nonoccupants such as pedestrians and bicyclists were

killed by a forward moving vehicle, 35% by a backing

vehicle, 19% due to a rollaway and 94% of occupant

fatalities occurred in single-vehicle crashes.

New safety issues have to be targeted due to the

design of fully automated vehicles in the upcoming

future. The challenges lie in the release of safe

automated driving systems. A major problem is the

test coverage of the rapidly expanding parameter

space to approve the safety of the automated system

(Wachenfeld and Winner, 2016).

As indicated in Figure 1 automated valet parking

(AVP) provides the service of an autonomous parking

procedure starting at the entrance of a parking facility.

The responsibility of the driving task is shared

between the parking area management (PAM) system

Figure 1: Cooperation between parking area management

(PAM) and automated vehicle to provide an automated

valet parking (AVP) service. Manually driven, driverless

vehicles and pedestrians are present in the parking garage.

(Schönemann, 2018).

and the automated vehicle. The AVP service is

executed driverless and is classified as level 4 of SAE

International’s taxonomy of driving automation. The

authors assume the following pre-conditions for

AVP:

Schönemann, V., Duschek, M. and Winner, H.

Maneuver-based Adaptive Safety Zone for Infrastructure-Supported Automated Valet Parking.

DOI: 10.5220/0007689503430351

In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2019), pages 343-351

ISBN: 978-989-758-374-2

343

1. Parking management system and automated

vehicle manage the driving task in cooperation.

2. The procedure of handing the automated vehicle

over to and requesting it back from the PAM is

instructed via a terminal (human-machine

interface, HMI).

3. Manually and automatically operated vehicles

are allowed to enter the parking garage.

4. Pedestrians, animals (e.g. dogs), stationary

objects, etc. are present in the car park.

5. Drivers and passengers have to leave the

automated vehicle before AVP is activated.

Today’s automated systems are designed according to

the international standard for functional safety of road

vehicles known as ISO 26262 (ISO, 2011). In

previous work, we applied the design process of the

ISO 26262 on a distributed valet parking system. A

detailed hazard and risk analysis was performed and

corresponding safety requirements were elaborated in

order to provide an as yet uninvestigated safety

concept for valet parking. The safety analysis leads to

the conclusion that parameters such as pose,

dimensions, velocity, existence and the class of

membership have to be known in order to avoid a

potential collision. Figure 2 shows the correlation

between these parameters. According to Dietmayer

(Dietmayer et al., 2016), the following uncertainties

exist:

 State uncertainty: Represents the measuring

errors of the object’s dimensions (length, width,

height), the object’s pose and the object’s

velocity.

 Existence uncertainty: Uncertainties whether an

object mapped into the representation actually

exists. This concerns mainly false positives and

false negatives.

 Class uncertainty: Describes uncertainties in

classifying an object and predicting its behavior.

The classes pedestrians, stationary objects,

vehicles, or other are available. The degree of

granularity depends on the use case.

However, in our previous work we did not yet

investigate in which area these parameters have to be

measured. A maneuver-specific safety zone is

elaborated to avoid collisions with static objects,

pedestrians and automated or manually driven

vehicles.

2 RELATED WORK

Safety is crucial for the commercialization of

automated driving. Safe vehicle automation systems

Figure 2: Uncertainty domains in the environment

perception and parameters which has to be determined for

collision avoidance (the object’s position, orientation,

dimensions, velocity, existence and class of membership).

shall intervene in case of an upcoming accident and

release the driver from this burden. A major challenge

is to design distributed systems which share the

responsibility for the driving task. Fully automated

valet parking is such a distributed system.

Each complex automation system causes the issue

of testing. Up to now, there is no international

standard for approving the safety of an automated

driving system. The ISO 26262 only addresses a

systematic approach for designing functionally safe

electrical and electronic systems of road vehicles.

Neither a standard, nor a methodology is specified to

develop a safety concept specifically for automated

driving systems. However, the safety approval and

new testing methods are required for the release of

automated driving (Winner, 2015).

Reschka et al. (Reschka, 2016) examined various

safety concepts for autonomous driving without

driver monitoring. An automated driving system

requires safety mechanisms to transfer the system into

a safe state. For an AVP system, the authors

introduced a remote operator. An external mechanism

provides the possibility to stop a driverless vehicle in

case of an emergency. This requires a secure and

reliable communication between the vehicle and a

remote control station. Furthermore, the authors

surveyed safety concepts in other domains. Safety

mechanisms for railway are integrated into the

infrastructure: a monitoring system prevents a train to

enter a track that is already occupied. The stopping

distances for railway are relatively large compared to

vehicles and the complexity of scenarios is lower due

to the control mainly in longitudinal direction.

Sense-failure

State variables Existence

Class of

Membership

Pose Dimensions Velocity

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

344

Chellaswamy et al. (Chellaswamy, 2015)

introduces a system to identify crowded areas to

avoid collisions. The authors state that most accidents

occur in dense traffic areas. The system adapts the

vehicle velocity at various safety zones. Once a

vehicle enters a dense traffic area, a controller

automatically reduces the vehicle velocity. The

described safety zone is not realized vehicle-specific

but area-specific. The methodology reduces the

severity of accidents for traffic participants, but

automated driving at lower velocities becomes more

time consuming.

Bosch and Daimler (Automotive World, 2018)

recently developed a prototype for a cooperative valet

parking system. The driving task is shared between

vehicles and intelligent infrastructure during mixed

traffic. Manually driven and automated vehicles as

well as pedestrians are present in the parking garage.

Environment perception and trajectory planning is

performed by the parking area management system

whereas the lateral and longitudinal actions are

executed by the vehicle. The prototype marks the first

pilot of its kind. However, further information

concerning the safety concept is not provided.

Schwesinger et al. (Schwesinger, 2016) and Löper

et al. (Löper, 2013) focus on a functional

development of a valet parking prototype capable of

performing fully automated navigation, but a

specification of a safety relevant space is not part of

the investigation.

The state of the art reveals that a safety concept

for automated valet parking is missing. Areas of

interest for safety considerations are not yet addressed

for AVP. However, a definition of an area, in which

the perception of objects for collision avoidance is

mandatory, has to be given. Outside of this area the

perception of objects is not required. The magnitude

of this area is maneuver-specific and therefore an

investigation of occurring maneuvers in a parking

garage is required. Additionally, a specification for

the infrastructure support has to be given for a

cooperative valet parking service.

This work aims to specify areas of interest around

the ego-vehicle in which the traffic participant’s

parameters have to be determined for collision

avoidance. The safety zone provides a description of

the relevant space in the environment perception task

that is executed by the parking area management

system and the automated vehicle. The results of this

work can be used to increase the safety performance

of the overall system and optimize the system

accordingly.

Figure 3: Decomposition of the automated driving system

in functional scenarios and investigation of possible

maneuvers for each scenario. The classification, and the

moving behavior as well as worst-case constraints ensure

the calculation of required safety distances for collision

avoidance.

3 METHODOLOGY

As illustrated in Figure 3 the overall valet parking

system is split into functional scenarios that occur

during the execution of the valet parking procedure.

According to Ulbrich et al. (Ulbrich, 2015) a scenario

describes snapshots of the environment and the

interaction of entities while time is progressing.

Thereby, 6 major scenarios can be investigated:

vehicle handover to parking area management

system, automated driving to a point of interest,

automated maneuvering into the parking space,

automated leaving of the parking space, vehicle

handover to driver and aborting the valet parking

procedure. These scenarios are further described in

the following section.

Each scenario is examined according to specific

maneuvers that are instructed by the automation

system. Maneuvers are extracted from layouts of car

parks (Pech, 2009). The determination of the safety

distances depends on the object’s class which ideally

is known. If the class type equals a vehicle, it can be

distinguished whether the potential collision partner

is manually driven or driverless. This kind of

information could be provided by the parking area

management system or C2C. If the vehicle is operated

driverless, it was registered by the PAM during the

handover and tracked. If no object information is

Automated

Driving

System

Maneuver 1

Maneuver X

Adaptive

Safety Zone 1

Adaptive

Safety Zone X

Moving

Towards

Moving Away

Manually

Driven

Automated

Other

Obstacle

VehiclePedestrian

Worst Case

Constraints

Car Park

Layouts

Scenario 1

Scenario X

Decomposition

in Scenarios

Maneuver-based Adaptive Safety Zone for Infrastructure-Supported Automated Valet Parking

345

provided, it should be assumed that the potential

collision partner is a manually driven vehicle. The

assumption is valid since compared to an automated

vehicle, more conservative parameters will be

assigned to the collision partner. Even if the

assumption is false, a sufficient safety distance is still

provided. Furthermore, the moving behavior of the

potential collision partner can be examined in order

to check whether the object is moving towards the

ego-vehicle, moving away or neither moving away

nor moving towards.

Worst case constraints such as timing, maximum

allowed velocity and minimum required deceleration

are defined for the operational domain and serve as an

input for each maneuver to specify a minimum

required safety distance for collision avoidance. The

safety zone adapts its size parameter-dependent at

each time step.

4 DECOMPOSITION OF

SCENARIOS

In the previous work, the valet parking system was

decomposed into functional scenarios that occur

during operation. These scenarios are illustrated in

Figure 4 and are used in combination with layouts of

car parks to identify executed maneuvers within

AVP.

A. Vehicle Handover to Parking Area Management

System

The valet parking procedure starts with the drop-off

of the automated vehicle at the handover zone. The

system checks whether the vehicle is located in the

handover zone, is in standstill and, correctly oriented

and, whether all doors are closed and all persons have

left the handover zone. The PAM may transmit a

static map of the parking garage and a predefined

trajectory to the corresponding parking spot. After the

parking request is instructed, the vehicle is handed

over to the parking area management and the

automation takes over the responsibility for the

further steps of the driving task. The handover is

successful if the specified constraints are met and a

parking spot can be assigned.

B. Automated Driving to a Point of Interest

If the handover is successful, the system has to

navigate the vehicle to the point of interest. The point

of interest is defined as the desired location which

mainly includes the assigned parking spot, the pick-

up zone or the location after an emergency brake and

Figure 4: Scenarios which occur during automated valet

parking: (a) vehicle handover to parking area management

system and vehicle handover to driver after a handback

request, (b) automated driving to a point of interest such as

the parking spot or the exit, (c) automated maneuvering into

and automated leaving of the parking space.

full stop. Thereby, the system shall ensure that the

vehicle stays in the statically defined drivable area.

The environment is perceived via radar, lidar and

ultrasonic sensors. Several maneuvers have to be

accomplished: following the straight or curved lane,

turning left/right, crossing of an intersection and

driving on a ramp. The end state is reached if the

vehicle arrives at the desired point of interest without

colliding. This scenario does not include the

maneuvering into the parking space.

C. Automated Maneuvering into the Parking Space

When the automated vehicle arrives nearby the

parking spot, the parking maneuver can be executed.

Either the PAM has already checked the required free

parking space and/ or the vehicle takes over the

analysis of the parking spot to decide whether the

parking space is appropriate for parking. Thereafter,

longitudinal or lateral actions have to be executed to

place the vehicle properly. The maneuver driving

backwards is part of the scenario. The vehicle may

park forward or reverse. The parking spots are

arranged from 0° to 90° with respect to the lane.

However, reverse parking is recommended in order to

reduce the required range of the rear side sensors

when leaving the parking spot in reverse. The sensor

range requirements can then be shifted to the vehicle

front since the sensor range is already required for

intersection crossings. The end state is successfully

reached if the assigned parking spot is arrived

collision-free, the vehicle size does not exceed the

parking spot, the parking brake is set and the vehicle

is on standby.

D. Automated Leaving of the Parking Space

If the driver initiates a handback request, the

automated vehicle is triggered to leave the parking

space. The required trajectory to the pick-up zone is

either computed by the ego-vehicle or received from

Pick-up

Zone

Parking

Spot

(a) (b) (c)

Handover

Zone

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

346

the PAM. Maneuvering out of the parking spot is

possible in forward and reverse direction. However,

as already stated in scenario C, forward leaving is

recommended. The maneuvers accelerate/ decelerate,

maneuvering out of the parking spot, and driving

backwards are required. The execution is successful

if no traffic participant is harmed and the automated

vehicle left the parking spot until the maneuver

‘following the straight or curved lane’ from scenario

B can be performed.

E. Vehicle Handover to Driver

When the vehicle arrives at the exit of the parking

garage, the vehicle will be placed at the pick-up zone,

the parking brake has to be set, the vehicle engine has

to be turned off, and the valet parking function needs

to be deactivated. If the constraints are met and no

traffic participant is harmed, the scenario is

considered to be successful.

F. Aborting the Valet Parking Procedure

This scenario describes the abort of the valet parking

service, which is equivalent to an early initiated

handback request. The automated vehicle does not

drive to the assigned parking spot but instead directly

to the exit of the parking garage. Therefore, scenario

B and E still have to be executed. Once the vehicle is

located in the pick-up zone in standstill, the valet

parking procedure can be deactivated and the driver

is able to enter the vehicle.

5 EXAMINATION OF

MANEUVERS

The scenarios A - F serve as an input to derive

maneuvers for AVP. A stopping distance is required

for each maneuver in order to avoid a collision with

traffic participants. The superposition of these

maneuver-specific stopping distances leads to the

introduction of a new term: the safety zone. The safety

zone adapts its distances according to the performed

maneuver as well as dynamic driving parameters of

the engaged traffic participants such as velocities,

timing constraints and deceleration capabilities. The

following maneuvers were found:

 Following a straight or curved lane: This

maneuver includes the primitives accelerate and

decelerate for longitudinal control as well as lane

keeping/ steering for lateral control. The ego-

vehicle’s position is thereby kept at the lane

center.

 Driving backwards: This maneuver is executed

during the maneuvering into the parking spot.

Thereby, reverse parking is recommended in

order to reduce the system’s perception

requirements to the rear side.

 Turning left/right: A turn is required at

intersection crossings and when leaving the

parking space to the left or to the right for parking

spaces oriented in lateral direction.

 Crossing an intersection: If the vehicle arrives at

an intersection, turning left, turning right or

crossing the intersection is possible. The

maneuver addresses the crossing.

 Coverage during maneuvers: Coverage of

objects by other traffic participants or by parking

construction causes undetected objects inside the

ego-vehicle’s safety zone without the vehicle’s

knowledge.

6 WORST-CASE CONSTRAINTS

Before the safety distances are determined

systematically, the defined constraints used here

should be mentioned. These assumptions serve as

constraints to calculate the stopping distances. Once

worst-case safety distances are determined, they are

also valid for less critical situations and should avoid

collisions. Thereby, the parameters are defined as

velocity , system response time 



from the

plausibility check until the initiation of the brakes,

driver reaction time 



, response time of the brakes





, time delay of the brake until buildup of

deceleration 



, a minimum guaranteed deceleration





 



  given by the friction coefficient 

and gravity constant . In a parking garage, the

authors assume a maximum allowed forward velocity

of 



, a velocity in reverse 



and a maximum

allowed velocity at intersections 



. Additionally,

a safety margin 



is required to prevent a collision.

These rather conservative considerations are valid for

the operational design domain and are summarized in

Table 1.

7 DERIVATION OF AN

ADAPTIVE SAFETY ZONE

Based on the found maneuvers and worst case

constraints an adaptive safety zone is derived. As

described in the methodology it is necessary to

Maneuver-based Adaptive Safety Zone for Infrastructure-Supported Automated Valet Parking

347

Table 1: Pre-defined Constraints for Automated Valet

Parking.

Description

Value

C01

Maximum allowed

velocities: in forward





, in reverse





, at intersections









   







   







   



C02

Worst-case expected

time delays: system

response time from

the plausibility check

until initiating the

brakes 



, driver

reaction time 



lag time of the brake





given by the

response time of the

brake 



and the

time until buildup of

deceleration 







 0.3 s





 1.5 s





 















 

C03

Minimum expected

deceleration 









  for object-

and ego-vehicle





 







C04

Safety margin 







 

1) Breuer and Bill, 2008

distinguish between several cases which will be

explored for each maneuver in the following.

A. Following a Straight or Curved Lane

When the ego-vehicle follows the lane there are three

cases regarding the stopping distances as shown in

Figure 5:

 Case (A,a): The detected object is moving

towards the ego-vehicle. In this case, it is useful

to distinguish between two possibilities: A

collision of two vehicles and either both vehicles

are braking (A,a1) or only the automated vehicle

is braking (A,a2).

 Case (A,b): The object is moving away and





 



 Case (A,c): The object is neither moving towards

the ego-vehicle nor moving away.

For each of these cases different stopping distances

have to be considered. In case (A,a1), it is assumed

that both vehicles react at the same time. The object

vehicle can either be manually driven or driverless.

Thus, the worst case object’s reaction time 



has

to be taken into account. The overall required

Figure 5: Safety zone (yellow) for following a straight or

curved lane: (a) object is moving towards the ego-vehicle,

(b) object is moving away and 



 



, (c) object is

neither moving away nor moving towards.

stopping distance is given by the overlap of the single

stopping distances calculated by













 













 

















 













 





  



 



(1)

Equation (1) produces the maximum spanned safety

zone for the worst-case 



 



 



. This

can be seen as the minimum required perception

range 



to the front for AVP. Once the object is

measured in this area, the safety zone adapts its size

according to the object’s velocity and reaction

capability as presented in Figure 5.

For the manually driven vehicle the driver’s reaction

time has to be injected into the formula by 









, whereas for an automated vehicle as a collision

partner the equation simplifies by setting 









The case (A,a2) occurs if the automated vehicle has

to be in standstill for collision avoidance and only the

control of the automated vehicle is possible. The

required distance 



is then given by the stopping

distance of the ego-vehicle and the driven distance of

the manually operated or automated vehicle





 



 



 







 



























  



 



(2)

Case (A,b) can be approximated by assuming an

object that is not moving since stopping in front of a

standing object is always more safety critical

compared to objects that are moving away. When

considering this approximation, the object has no

impact on the stopping distance and therefore the

stopping distance is only influenced by the ego-

vehicle’s parameters. This is achieved by setting





  in equation (2). The same considerations

can be applied to case (A,c), since case (A,b) is

reduced to case (A,c).

req,f3

req,f1

req,f2

(a) (b) (c)

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

348

Figure 6: Safety zone (yellow) for intersection crossing

(left) which reveals similar characteristics to leaving the

parking spot (right).

B. Driving Backwards

This maneuver has similar characteristics to the

maneuver following a straight or curved lane.

Similarly, three cases occur while driving in reverse:

 Case (B,a): The detected object is moving

towards the ego-vehicle

 Case (B,b): The object is moving away and





 



 Case (B,c): The object is neither moving towards

the ego-vehicle nor moving away.

The stopping distances are calculated as described in

the maneuver following a straight or curved lane, but

considering that the ego-vehicle is driving in reverse

and an object is detected to the rear. The minimum

required perception range to the rear for AVP is given

for 



 



, 



 



and 



 



Once an object is measured in this area, the safety

zone adapts its size according to the object’s

parameters.

C. Turning Left/ Right:

In case of turning right at an intersection or when

leaving the parking spot as shown in Figure 6, traffic

participants coming from the left need to have at least

a minimum distance 



to the ego-vehicle in order

to be able to successfully brake in case of an

emergency. The required distance is dependent on

whether the object-vehicle is manually driven or

driverless.





 











 













  



 



(3)

For an automated collision partner approaching from

the side with a velocity 



, the required safety

distance is given by setting the reaction time 









. If no information is provided by the

infrastructure about the type of object, the system

assumes that the object is a manually driven vehicle.

The assumption is valid since rather conservative

parameters are allocated to the traffic participant.

Figure 7: Covered perception area at an intersection (left).

Relevant object which is not in the ego-vehicle’s sensor

view when driving on a ramp (right).

Even if the assumption is false, a sufficient safety

distance is assigned by 



 



The minimum required distance that has to be

checked by the ego-vehicle when entering the

corresponding lane is given for 



 



D. Crossing an Intersection

This maneuver includes the same safety distances as

described in the maneuver turning left/ right except

that no turn is actually performed by the ego-vehicle.

Same dependencies occur: either the vehicle-type has

to be known or a manually driven vehicle as a worst

case is assumed to provide a sufficient safety

distance.

E. Coverage

The system has to manage potential collisions for

each of the upper described maneuvers even if the

collision partner is covered for the ego-vehicle. The

issue can only be solved by C2I since top mounted

sensors will not be covered by traffic participants or

by parking construction. Therefore, the required

information from safety areas have to be transmitted

to the ego-vehicle. The covered area for the ego-

vehicle has to be determined by the parking area

management system and top-mounted sensors located

in this area have to replace the ego-vehicle’s sensor

view.

The case of driving on a ramp requires the system to

distinguish whether a detected object is a ramp. Here,

similar safety distances as described for following a

straight or curved lane have to be considered just that

the deceleration depends on the slope  of the ramp





 



    

(4)

These safety distances have to be provided by the

parking area management system as shown in Figure

Derivation of the Overall Safety Zone

The superposition of the derived maneuver-based

stopping distances shows that the overall safety zone

is created by the ego-vehicle’s and the object’s

Parking

Spot

req,i2

req,i1

req,ego

PAM

Maneuver-based Adaptive Safety Zone for Infrastructure-Supported Automated Valet Parking

349

travelled envelopes given by their widths and

stopping distances. A radius with the object’s

stopping distance can be spanned around the ego-

vehicle to the front and to the rear. Furthermore, the

ego-vehicle’s stopping envelope has to be added

when following a straight lane or driving backwards.

Once the object is oriented in a 90° angle to the ego-

vehicle such as at intersections, only the object’s

stopping envelope has to be considered. As a result,

the overall safety zone is given by the ego-vehicle and

the object’s travelled envelope as shown in Figure 8.

The main equation and overall maneuver specific

constraints are listed in Table 2.

Table 2: Main equation and maneuver-specific constraints

for determining the required safety zone.

Main Equation













 













 





 



 











 





  



 



Maneuvers

Safety Zone

Following a straight or curved

lane

Driving backwards

Coverage





 



Case (A/B/E,a1):





 



 



Case (A/B/E,a2):







  



 















Case (A/B/E,b/c):





 

Turning left/ right

Crossing an intersection

Coverage

Case (C/D/E):





 





 





 



8 CONCLUSION

Automated driving has revealed challenges for

functional safety. A safety concept for automated

valet parking was not yet targeted. Furthermore,

mandatory perception areas for collision avoidance

were not yet addressed in the state of the art for AVP.

The shapes of these areas are maneuver-specific and

therefore an examination of occurring maneuvers in a

parking garage was required. For this, the overall

system is decomposed in functional scenarios and

each scenario is investigated for the executed

maneuvers. Worst-case constraints such as timing,

maximum allowed velocity and minimum required

Figure 8: Overall safety zone (yellow) to the front and to

the rear given by the superposition of the ego-vehicle’s and

object’s travelled envelopes and corresponding adaptation

(red) due to the occurrence of moving traffic participants.

deceleration are derived for cooperative valet parking

in a mixed traffic environment. These constraints

served as an input to calculate minimum required

safety distances for each maneuver. The authors

investigated in which areas parameters such as pose,

dimensions, velocity, existence, and the class have to

be known in order to avoid a potential collision for

automated valet parking.

The superposition of these safety areas leads to the

term adaptive safety zone. The safety zone provides a

description of a safety-relevant space for the

environment perception. The collision partner’s

parameters are measured in a minimum required

perception zone. The adaptive safety zone is

determined for each maneuver by distinguishing

between the collision partner’s characteristic and it’s

moving behavior. The parking area management

system provides the safety zone for the automated

vehicle if coverage prevents the perception task or if

safety critical objects do not appear in the vehicle’s

sensor view. The results of this work can be used to

adjust the AVP system requirements for the

environment perception task according the

determined safety zone. The results illustrate which

areas top mounted sensors have to examine to

increase the safety performance of the overall system.

ACKNOWLEDGEMENTS

This work has been conducted within the ENABLE-

S3 project that has received funding from the ECSEL

Joint Undertaking under Grant Agreement no.

692455. This Joint Undertaking receives support

from the European Union’s HORIZON 2020 research

and innovation programme and Austria, Denmark,

Germany, Finland, Czech Republic, Italy, Spain,

req,i1

req,i2

req,obj

req,egoF

req,egoR

req,obj

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

350

Portugal, Poland, Ireland, Belgium, France,

Netherlands, United Kingdom, Slovakia, Norway.

REFERENCES

Automotive World, 2018. Daimler and Bosch jointly

premiere automated valet parking in China. From:

www.automotiveworld.com/news-releases/daimler-

and-bosch-jointly-premiere-automated-valet-parking-

in-china/. Accessed: November 2018.

Breuer, B., and Bill, K. H. 2008. Brake technology

handbook.

Chellaswamy, C., Rammohan, T., Swarna, Y., Bharathi, R.

R., & Saranya, R, 2015. Development of safety zone

detection and collision avoidance using wireless

network. In Smart Technologies and Management for

Computing, Communication, Controls, Energy and

Materials (ICSTM), 2015 International Conference on

pp. 112-118.

Dietmayer, K., 2016. Predicting of machine perception for

automated driving. In Autonomous Driving: Technical,

Legal and Social Aspects, Maurer, M., Gerdes, J. C.,

Lenz, B. and Winner, H., Eds.: Springer, pp. 407-424.

International Organization for Standardization, 2011. ISO

26262: Road vehicles - Functional Safety. Geneva,

Switzerland.

Löper, C., Brunken, C., Thomaidis, G., Lapoehn, S.,

Fouopi, P. P., Mosebach, H., & Koster, F., 2013.

Automated valet parking as part of an integrated travel

assistance. In 16th International IEEE Conference on

Intelligent Transportation Systems-(ITSC), pp. 2341-

2348.

Pech, A., Warmuth, G., Jens, K., & Zeininger, J., 2009.

Parkhäuser - Garagen: Grundlagen, Planung, Betrieb.

Birkhäuser. pp. 375-410.

Reschka, A., 2016. Safety Concept for Autonomous

Vehicles, In Autonomous Driving – Technical, Legal

and Social Aspects pp. 473–496, Springer Nature.

Singh, S. 2016. Non-traffic surveillance: fatality and injury

statistics in non-traffic crashes 2012 to 2014, No. DOT

HS 812 311.

SAE, 2016. Taxonomy and Definitions for Terms Related

to Driving Automation Systems for On-Road Motor

Vehicles (J3016). Society of Automotive Engineers.

Schönemann, V. and Winner, H. et al., 2018. Scenario-

based Functional Safety for Automated Driving on the

Example of Valet Parking. In IEEE Future Information

and Communication Conference, Singapore.

Schwesinger, U., Bürki, M., Timpner, J., Rottmann, S.,

Wolf, L., Paz, L. M., ... & Heng, L., 2016. Automated

valet parking and charging for e-mobility. In Intelligent

Vehicles Symposium (IV), pp. 157-164.

Ulbrich, S., Menzel, T., Reschka, A., Schuldt, F., Maurer,

M., 2015. Defining and Substantiating the Terms

Scene, Situation, and Scenario for Automated Driving.

In IEEE 18th International Conference on Intelligent

Transportation Systems (ITSC), pp. 982–988.

Wachenfeld, W. and Winner, H. 2016. The Release of

Autonomous Vehicles. In Autonomous Driving:

Technical, Legal and Social Aspects. Maurer, M.,

Gerdes, J. C., Lenz, B., and Winner, H. , Eds.: Springer.

pp. 425–449.

Winner, H., Graupner, M. and Wachenfeldt, W., 2015. How

to Address the Approval Trap for Autonomous

Vehicles (Keynote). In IEEE 18th International

Conference on Intelligent Transportation Systems

(ITSC).

Maneuver-based Adaptive Safety Zone for Infrastructure-Supported Automated Valet Parking

351