A System Design for Teleoperated Road Vehicles

Sebastian Gnatzig, Frederic Chucholowski, Tito Tang and Markus Lienkamp

Institute of Automotive Technology, Technische Universit

at M

unchen,

Boltzmannstr. 15, 85748 Garching b. M

unchen, Germany

Keywords:

Teleoperation, Autonomous Driving.

Abstract:

The possibility to provision road vehicles unmanned and on demand will have an important inﬂuence on the

development of new mobility concepts. We therefore present the teleoperated driving of road vehicles. This

paper outlines the basic concepts, including a static multi-camera design, an operator interface with a sensor

fusion based display, and a cellular network based video transmission and communication architecture. We

also show how we manage to fulﬁll the system’s technical requirements with our hard- and software design and

point out the occurring problems due to communication limitations and lack of situation awareness. Finally,

we propose solutions to guarantee driving safety.

1 INTRODUCTION

We are living in a world with a rapidly growing pop-

ulation. The number of megacities with more than

ten million people will probably double in the next

decades (United Nations, 2012, pp. 5 f.). This will

have an immense effect on daily mobility. Climate

change and ﬁniteness of fossil fuels enforce this trend.

But there is deﬁnitely no sign of a post-automobile so-

ciety (Canzler and Knie, 2009, pp. 11 f.). The future

of inner-city mobility will be integrated mobility con-

cepts, in which the automobile is one means of trans-

port among others and used in a collaborative way in

form of car-sharing (Canzler and Knie, 2009, pp. 16

ff.). This leads to an essential issue: How will the cars

be provided and distributed?

The automation of car-sharing distribution would

be the most efﬁcient and comfortable way. The

optimal use-oriented vehicle would be driven au-

tonomously to the customer. After usage, the au-

tonomous car would park itself or drive to the next

customer. Despite the progress achieved in the re-

search of autonomous vehicles in the last thirty years,

the machine perception has not reached the human

perception skills, by far. Competitions, such as the

DARPA challenges, demonstrate the achievements of

modern robotic research (see (Thrun et al., 2006),

(Fletcher et al., 2008), (Kammel et al., 2008)). These

challenges suggest that autonomous driving in urban

environments is nonetheless possible. However, the

results of these challenges are more applicable in mil-

itary purpose. A fail-safe driving in public mixed traf-

ﬁc is not yet feasible (Stiller, 2005, p. 5).

In contrast, teleoperation is a suitable solution to

achieve the automated distribution of automobiles.

Since the human performs quite well in the driving-

relevant skills, due to the large amount of experi-

ences and the ability to anticipate (see (Abendroth and

Bruder, 2009, pp. 13 f.), (Stiller, 2005, p. 9), (Dick-

manns, 2005, p. 204)) it is reasonable to keep him in

the loop.

2 TELEOPERATION

Teleoperated robots are commonly used for explo-

ration or surveillance tasks on ground, in the air,

and underwater. Current applications are underwa-

ter maintenance of oil platforms or reconnaissance

in conﬂict areas by drones, for example (see (Fong

and Thorpe, 2001)). Despite the different applications

of telerobotics, there are three characteristic elements

that are part of every teleoperated system (Winﬁeld,

2000, pp. 148 f.):

Robot: The teleoperator is very application-

speciﬁc. Generally, it consists of at least the com-

munication hardware to receive the control signals.

Furthermore, a camera is often essential. The addi-

tional actuator and sensor equipment depends on the

application and the needed on-board autonomy (see

231

Gnatzig S., Chucholowski F., Tang T. and Lienkamp M..

A System Design for Teleoperated Road Vehicles.

DOI: 10.5220/0004475802310238

In Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2013), pages 231-238

ISBN: 978-989-8565-71-6

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

(Winﬁeld, 2000, p. 149)).

Communication Link: The transmission of sen-

sor and control signals is done via a communication

link. Ground and air vehicles usually need a wireless

connection. In contrast, underwater vehicles are of-

ten tethered because of the low communication band-

width of acoustic links. Besides bandwidth, latency

is the most important criterion for the choice of the

communication architecture (see (Sheridan and Ver-

plank, 1978, pp. 69 f.), (Farr et al., 2010), (Winﬁeld,

1999)).

Operator Interface: The operator controls the

robot from an operator workstation. The interface is

usually multi modal, consisting of at least a display

visualizing live sensor data from the robot. The main

element is commonly a video image from the robot’s

perspective. Steering wheel and pedals but also joy-

sticks or touch devices are commonly used as control

inputs (see (Scribner and Gombash, 1998, pp. 4 f.),

(Fong and Thorpe, 2001), (Fong et al., 2001), (Kay

and Thorpe, 1995)).

3 DRIVING TASK

Designing a teleoperation system for remotely driv-

ing road vehicles requires the analysis of inﬂuenc-

ing variables of human driving. A human driver uses

mainly four senses. Most important are the visual

sense and the aural sense, but the haptic and vestibu-

lar system are also partially involved during driving

urgensohn and Timpe, 2001). The visual sense and

the aural sense are the two far senses that allow a time-

and position-related prediction. This makes these two

senses the most important for the high dynamic of the

trafﬁc environment (Negele, 2007, p. 7).

The visual sense is not only used to perceive the own

position and environment, but also to predict and an-

ticipate the behavior of other trafﬁc participants (see

(Abendroth and Bruder, 2009, p. 6), (Negele, 2007,

p. 10)).

Furthermore, visual stimuli have the greatest inﬂu-

ence on velocity perception and choice of adequate

speed (see (Lank, 2010, pp. 54 ff.)). Correct veloc-

ity estimation is essential for vehicle driving. Driving

with inadequate speed is the main cause of accidents

in Germany (Statistisches Bundesamt, 2011, pp. 25

f.).

Visual information can nowadays be well perceived

by camera systems and can be realistically rendered

in modern imaging systems (Negele, 2007, p. 10).The

transformation of this sensory information to the op-

erator interface is necessary to achieve the same driv-

ing performance of an on-board driver. This process

equals the design of a driving simulator.

4 SYSTEM DESIGN

The overall concept is illustrated in Fig. 1. The opera-

tor who remotely controls the car needs to have an un-

derstanding of the car’s surroundings. As explained,

the most important sense for driving a car is the visual

one. Therefore, we need to capture the surroundings

with cameras. According to EU directives, a horizon-

tal view angle of at least 180 degrees is required in

the front to steer a vehicle. The side mirror views

should at least provide about 12 degrees each. There

should also be a rear view with about 20 degrees (The

European Parliament And The Council, 2004), (The

European Parliament And The Council, 1977).

To obtain a smooth video playback, the frame rate

is set to 25 frames per second (fps), like in the Euro-

pean PAL video standard. Transmitting the raw im-

ages in color over a cellular connection would be im-

possible due to the huge amount of data. A single

camera with 640x480 pixels, 3 bytes per pixel and 25

fps would require a bandwidth of about 184 megabits

per second (Mbps). To reduce the data rate, the video

is video-encoded.

On the operator side, the images are decoded and

displayed on a wide angle screen with a ﬁeld of view

similar to the vehicle’s. The reactions of the operator

are captured using a suitable input device and trans-

mitted to the vehicle. The controllers in the vehicle

then compensate the errors between demanded and

current values.

The following sections give a detailed overview of

the concept structured by the classiﬁcation introduced

in Chapter 2.

Figure 1: Data transmission scheme for teleoperated road

vehicles.

4.1 Vehicle Architecture

The experimental vehicle, an Audi Q7 equipped with

sensors and actuators, can be seen in Fig. 2. The sys-

tem design differentiates between the hardware and

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software architecture of the experimental vehicle. The

hardware architecture describes the used sensors and

actuators that are necessary to fulﬁll the primary and

secondary driving task. The primary driving task con-

sists of steering, accelerating, braking and shifting.

The secondary driving task includes mostly the ma-

nipulation of the lighting system and the windscreen

wiper.

To fulﬁll the requirements on the ﬁeld of view, the

camera system in the vehicle consists of eight cam-

eras. Each camera provides a resolution of 640x480

pixels. Fig. 3 shows the ﬁve industrial GigE cam-

eras mounted on the front of the vehicle, which cover

a ﬁeld of view of approximately 240 degrees. Three

further USB cameras cover the side mirrors and the

rear view (see (Diermeyer et al., 2011)). In a sin-

gle camera setup with restricted ﬁeld of view pan-

ning would be necessary to help the operator to ne-

gotiate tight corners and avoid obstacles (McGovern,

1987, p. 4). But camera panning must be fast enough

(Kay, 1997, p. 131 ). Hence, we decided to use a

static multi-camera setup which permanently covers

the complete surrounding. A software pan then dis-

plays the relevant detail of our complete surrounding.

This has the advantage that it is much faster and more

reliable than any mechanical panning system. Since

color displays have a huge effect on the perception of

objects, compared to gray scale displays (Kay, 1997),

all cameras provide color images.

A long range radar scanner and three single layer

laser scanners are used to measure the surroundings.

The radar scanner has a horizontal opening angle of

16 degrees. Each of the laser scanners has a 180

degrees ﬁeld of view. This hybrid sensor network

is combined by a competitive and complementary

sensor fusion approach (see (Durrant-Whyte, 1988)).

The competitive aspects are increasing the reliability

and accuracy in front of the vehicle. The complemen-

tary fusion results in 360 degrees surround view.

In order to process the data from the different

Figure 2: Experimental vehicle equipped with sensors and

actuators.

sensors, two central processors are used. The video

streams coming from the cameras are processed and

encoded by an automotive-suitable CarPC running a

data processing framework on a windows operating

system. It also handles the complete communica-

tion between the vehicle and working station. Along-

side the CarPC, a rapid prototyping unit capable of

real-time processing undertakes the tasks of reading

the data from sensors, processing its information, and

sending appropriate control signals to the actuators

according to measured data and the driver’s inputs

coming from the working station. This rapid proto-

typing unit is basically responsible for the primary

driving task.

To achieve the steering functionality, a hollow

shaft motor has been built in the steering column di-

rectly behind the steering wheel. This motor can be

overridden anytime if a safety driver is sitting in the

vehicle.

The acceleration and braking functionality is

achieved through an electronic interface to the motor

control unit to communicate the desired motor torque

and deceleration. According to Isermann, ”For au-

tomobiles, (usually) a safe state is stand still (or low

speed) at a nonhazardous place.” (Isermann, 2006). In

order to guarantee a safe state, it is necessary to en-

sure the ability to brake at any moment. Therefore, an

additional pneumatic system is incorporated to the ve-

hicle, which makes use of a loaded air tank to directly

press on the braking pedal. A further redundancy is

implemented on a separate microcontroller. In case

of a malfunction of the rapid prototyping unit, it will

trigger a full braking emergency stop using the pneu-

matic valves.

To change the direction of driving, a shift-by-wire

system was implemented in the experimental vehicle,

which replaces the original Audi shifting mechanism.

The secondary driving tasks are also manipulated

by the rapid prototyping unit. These functions are al-

ready electronically controllable in a today’s vehicle

Figure 3: Front camera system covering a ﬁeld of view of

240 degrees with ﬁve cameras.

ASystemDesignforTeleoperatedRoadVehicles

233

Figure 4: Comparison of video compression with high (640x480, 25.0 crf) and low (320x240, 30.0 crf) quality.

via CAN bus communication.

The software architecture consists of three lay-

ers. The network layer establishes the IP-based data

communication to the operator interface. It contains

a connection-oriented TCP connection for managing

and initializing the communication link and a connec-

tionless UDP connection for data transmission. The

connectionless data transmission leads to less over-

head and latency. In the data layer the sensor and

control data processing is performed. The validity of

the data ﬂow and access management is veriﬁed. The

lowest level is the hardware communication layer.

Here, the different sensors and actuators are accessed

by CAN and Ethernet protocols.

4.2 Communications

The vehicle is wirelessly controlled. With respect

to the possible travelled distance of the teleoperated

vehicle, the communication infrastructure needs to

cover a wide area. To avoid a proprietary solution,

mobile Internet is a sufﬁcient way of using an already

established infrastructure. Fortunately, the network

coverage for cellular connections is constantly grow-

ing and the available transmission speeds are increas-

ing. The current release 8 for 3G mobile networks

speciﬁes a nominal peak downlink rate of 42 Mbps

and an uplink rate of 11 Mbps for HSPA+ (3GPP -

3rd Generation Partnership Project, 2013). In newer

releases, the data rates will be even higher. For 4G

networks, the currently speciﬁed data rates are 300

Mbps for down- and 75 Mbps for uplink. The next

evolutionary step, LTE Advanced, is expected to pro-

vide peak data rates of even 3 gigabits per second for

down- and 1.5 gigabits per second for uplink. How-

ever, as shown in (Tenorio et al., 2010), actual band-

width highly depends on signal strength and the num-

ber of users in a network cell and can be much lower

than the nominal value. The fastest currently sup-

ported 3G network standard in Germany is DC-HSPA

with a nominal downlink data rate of 42 Mbps and 10

Mbps uplink, which is provided by Telekom Deutsch-

land GmbH. With 4G, the company even offers 100

Mbps down- and 10 Mbps uplink. Since the video

transmission requires much more bandwidth than the

control input transmission, the upload bandwidth is

the limiting factor for our system. Therefore, we cur-

rently use an Internet connection over 3G and, where

available, a 4G network as communication channel.

Since 10 Mbps is the highest nominal peak uplink

data rate, one can assume that the actual data rate will

usually have about 1 to 3 Mbps. This is not sufﬁ-

cient to transmit all video images with best quality

settings but good enough to get an overview of the

whole scene and transmit speciﬁc parts of the image

with higher quality. The three rear cameras for in-

stance are only necessary for lane changes or reverse

driving. Due to the restricted bandwidth, the video

images are encoded using the state-of-the-art H264

video codec. The compression parameters - e.g. con-

stant rate factor (crf) - and image size are continu-

ously adapted to the available bandwidth to ensure a

smooth transmission. If necessary, some cameras can

even be omitted totally. To select the best parameters,

a heuristic logic is used, which depends on the driv-

ing situation and the camera. The rule-based approach

starts reducing the quality at the outer cameras. The

driving relevant front camera gets the best quality set-

ting. Fig. 4 shows a comparison of an encoded 24

fps video with 640x480 pixels and a constant rate fac-

tor of 25 and the encoded video with 320x240 pixels

and a constant rate factor of 30. Compared to the un-

compressed video with about 177 Mbps, the bit rate

could greatly be reduced to 1678 Kbps for the high

respectively 222 Kbps for the low resolution video.

According to (Krenik, 2008), the transmission

time with a mobile 3G HSPA connection is about 50

milliseconds in each direction. For 4G networks, the

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0 10 20 30 40 50 60

200

400

600

800

1000

1200

1400

Time [s]

Transmission delay [ms]

Average delay: 120.8192 ms

Minimum delay: 65 ms

Maximum delay: 1299 ms

Figure 5: Measured transmission delays during test drives

using a 3G network.

time delays could be reduced to 5 milliseconds in each

direction for small packets (Krenik, 2008). Fig. 5

shows an example of the round trip time (RTT) for

video and control data in a 3G cellular network that

we measured. The RTT states the time that it takes

to transmit a video image to the operator and trans-

mit a control packet back to the vehicle. This does

not include processing times on the CarPC or the op-

erator PC. While the minimum time delay resulting

from the round trip time is about 65 milliseconds, it

sometimes takes longer to transmit the packets. Even

peaks of over 1 second are possible. In this case, the

average time delay for the transmission was about 121

milliseconds. The measurement complies with the

measurements in (Prokkola et al., 2009), where the

average RTT in a 3G HSPA+ connection was about

72 milliseconds. To keep the time delay of the video

transmission as low as possible we use the connec-

tionless UDP-based RTP protocol.

4.3 Operator Interface

The working station with which the operator drives

the vehicle is shown in Fig. 6. It can be compared to

a static driving simulator.

To realize a high feeling of presence, the work-

station is assembled as a vehicle cockpit. The work-

station is equipped with conventional driver inputs as

a consumer force-feedback wheel, pedals and gear

shift. The operator sits on a usual driver seat. To

achieve an adequate ﬁeld of view, three 24” monitors

are arranged next to each other. The place is sufﬁcient

to show all ﬁve front cameras in parallel. The side and

rear mirror cameras are overlaid in the corners when

necessary.

The operator interface again consists of three lay-

ers. The network layer is the counterpart to the net-

work layer of the vehicle. Here, the operator sends the

request to take over control via the TCP-based com-

munication. The data layer mainly consists of decod-

Figure 6: Operator workstation with three monitors, control

inputs, and driver seat.

ing and displaying the images and requesting the con-

trol inputs. As described above, the quality and the

number of the displayed cameras are adapted to the

actual communication bandwidth. Furthermore, the

operator can choose different camera setups. Addi-

tionally, the operator interface shows extra informa-

tion regarding the driving task as vehicle speed or

turning signals. In order to enhance the information

available to the operator, we use a sensor fusion dis-

play (see (Fong et al., 2001)). Especially, depth per-

ception which is important for orientation and veloc-

ity perception (see (Goldstein et al., 2008, pp. 185

ff.)) is difﬁcult in a tele-environment. To improve

the distance estimation of the operator, we use the

environmental sensor perception of the vehicle. The

data of the lidar scanners are combined in a grid based

approach. The occupancy grid gives a top view per-

spective of the obstacles in the surrounding to the op-

erator. This map is especially relevant during slow

parking maneuvers and can be displayed on an ad-

ditional monitor. During driving, the fusioned lidar

and radar scanner data is perspectively overlaid in the

video image to highlight obstacles. According to lit-

erature, operators of teleoperated vehicles often lose

their orientation (see (Kay, 1997, pp. 10 f.)). Thus,

we use a navigation map that can be shown on the

separate monitor. The hardware layer is the lowest

level of the software architecture and provides access

to the control devices.

5 RESULTS

We did several teleoperated test runs on our test track.

The test track is 650 meters long and consists of a

single lane road with 4.5 meters width. There are no

explicit road markings. The shape of the track is an

s-curve with minimal radius of 200 meters (Fig. 7).

ASystemDesignforTeleoperatedRoadVehicles

235

Figure 7: Birds view on section of the test track. White

lines are the tracked road boarders by the top lidar scanner,

the red line is the driven path (satellite image by Google

Earth).

During experiments the operator saw the videos with

an artiﬁcial constant time delay of 500 ms. The total

round trip time was about 550-600 ms. The average

speed during the experiments was about 30 km/h. The

speed was controlled by cruise control and the oper-

ator kept the settings constant for most of the time.

Under these conditions the operator was able to safely

keep the vehicle on the track and he was even able to

react on dynamic objects as pedestrians. The stan-

dard deviation of the lateral offset was about 0.4 me-

ter. This shows that the operator was able to follow

the lane with only little error in lane keeping. The

lateral offset was measured by the top laser scanner

that tracks the road borders. Under the described con-

ditions, the operator tends to steer the vehicle with

periodic inputs of 0.25 Hz. This still leads to a stable

behavior, but could become critical, if the speed was

increased and thus the phase margin reduced.

6 DISCUSSION

A variable time delay greatly inﬂuences the driving

performance. It was determined that a smooth im-

age sequence is rather helpful for the operator. The

artiﬁcial time delay of 500 ms showed to be unprob-

lematic for a driving speed of 30 km/h. However, test

drives have shown a reasonable speed compared to

other teleoperated road vehicles, such as (Appelqvist

et al., 2010). Due to time delays, highly dynamical

maneuvers are not possible. Speeds up to 50 km/h

should be possible with our experimental platform un-

der stable time delay conditions, 35 km/h had been

reached in the preceding tests.

Using current cellular networks, a maximum of

three cameras could be simultaneously transmitted

because of bandwidth limitations. This is not a limi-

tation, since experience showed that not more than an

horizontal ﬁeld of view of approximately 135 degrees

is needed for normal straight drive. This corresponds

to three cameras transmitted simultaneously. Us-

ing gray-scale images instead of color images would

slightly reduce the required data rate but would make

it difﬁcult to drive e.g. through shadows.

7 OUTLOOK

During our test drives, we identiﬁed several problems

that need to be solved for safe remotely controlled

driving. In (Lutz et al., 2012), we give an overview of

the legal requirements that must be met and show pos-

sible solutions. We also experienced that even with

25 images per second it is difﬁcult to estimate the

vehicle’s speed just by relying on the video stream.

To guarantee a safe drive, two important aspects are

being investigated. First of all, the lack of situation

awareness produces an effect of nonrealistic driving

feeling, which causes the operator to perform differ-

ently compared to sitting directly inside the vehicle.

To solve this problem, methods are being studied and

implemented, where visual, aural and haptic channels

are used.

Moreover, emergency strategies during connec-

tion loss need to be developed. The implemented

approach is the so-called free corridor (Diermeyer

et al., 2011), in which the trajectory of a full brak-

ing is shown to the operator and he is responsible for

keeping it free from obstacles all the time.

One of the biggest challenges for the teleopera-

tion are time delays caused by the sensors and signal

processing as well as the transmission. While it is

possible to slightly decrease sensing and processing

times, there will still be a certain time delay in the data

transmission which depends on the wireless transmis-

sion technology and the constitution of the Internet

connection. Since this is beyond our reach, we elabo-

rated two strategies to reduce the delay effects on the

vehicle control which will further be elaborated in the

future. By predicting the vehicle’s position and the

positions of outside trafﬁc participants, we can mod-

ify the captured video images and give the operator

a preview of the trafﬁc scene (Chucholowski et al.,

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236

2013). A different approach is to replace the direct

control of the vehicle by an indirect control strategy.

Instead of directly passing steering wheel, accelera-

tion and brake pedal inputs, we use a shared control

approach and generate high-level goals which are au-

tomatically achieved by the vehicle as described in

(Gnatzig et al., 2012b) and (Gnatzig et al., 2012a).

8 CONCLUSIONS

In this paper we presented the requirements to achieve

unmanned vehicles through teleoperation, showed our

solutions to meet those and outlined the problems that

we faced so far. With the presented system we could

successfully realize a teleoperated road vehicle sce-

nario. In the near future, teleoperation could be a

solution for upcoming demands resulting from new

mobility concepts.

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