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Transcript of Signal Processing Strategies for a Multi Sensor Pre · PDF fileThe 24 GHz radar and the PMD...
Institut für Nachrichtentechnik
Automobil-Radar
Fax: +49 (40) 42878-2281
Eißendorfer Straße 40
D- 21073 Hamburg
Version 09.02.2006
Signal Processing Strategies for a MultiSensor Pre-Crash ApplicationStephan Müller, Henning Ritter, Hermann Rohling; TU Hamburg-Harburg
Marc-Michael Meinecke, Mark Gonter; Volkswagen AG
Introduction
System Setup
In today's cars a crash is detected by observing the signals of the mi-
cro-mechanical acceleration sensors (so-called crash-sensors) of the
car. If the analysis of the deceleration signal (e.g. by using signal inte-
gration and neural networks) exceeds a defined value a crash is as-
sumed and the safety measures like activating passenger airbags
are triggered. Standard airbags are triggered normally 10ms-30ms
after the first contact with the obstacle and they are fully blown up
after further 30ms. Due to the fact that the collision has already hap-
pened there is only a short time frame available for triggering actua-
tors. In order to improve this situation, the car's surrounding can be ob-
served by sensors like radar or cameras. This gives the opportunity to
get an overview of the situation around the car and therefore detect
certain crash situations even before the car is hitting the obstacle.
The automotive industry spends for the time being a large amount of
effort for developing pre-crash systems. The gain of such systems is
obvious. In case the system can detect a highly risky situation some
milliseconds in advance of a collision, active safety measures (so-
called actuators) can be triggered early and the safety for the car
driver and occupants can be increased.
An additional and very important future application may be the ac-
tive protection of vulnerable road users, like pedestrians or cyclists.
This could increase road safety; however, such system is not feasible
with sensors available today.
To detect a situation of an inevitable accident in time, a sensor ob-
servation of the car environment is needed. The sensor based on its
ability to analyze the situation will make a decision whether an acci-
dent is about to occur and whether some actuators have to be trig-
gered or if the driver can still avoid the accident by starting an eva-
sive maneuver.
Since even non-reversible safety measures are taken into account
the situation analysis and the decision procedure of the pre-crash sys-
tem requires an extremely high reliability and therewith very low false
alarm rates. This is not easy to achieve with today's technology. And
even with extensive testing it is difficult to cover and simulate all the
different kinds of situations which a large number of vehicles
equipped with the system may be subjected to when driven on the
road.
To test and validate the developed pre-crash system on a quantita-
tive basis a research car Audi A8 has been built up which is
equipped with three different sensor systems. A 77 GHz long range
radar with 160 m maximum range (standard ACC sensor), a 24 GHz
radar sensor (narrow band) with 70 m maximum range and a PMD
camera (3D camera) with a maximum range of 40 m are integrated
into the Audi A8. Fig 1 shows the test vehicle with the mounted sen-
sors.
The observation area of the three sensor types is much larger com-
pared to the safety area where the decision (trigger for actuators)
about an unavoidable accident situation must be taken. Within the
limits of the system all targets inside the observation area are sup-
posed to be detected early enough so that a reasonably reliable situ-
ation analysis can be processed. The tracked targets will show a rela-
tively high measurement accuracy which is an advantage for the ac-
cident situation analysis.
Due to the different physical measurement principles each sensor
has its individual measurement characteristics and accuracy as well
as limits. Each sensor has its own individual performance which is in-
vestigated in this paper. But nevertheless a data combination can in-
crease the system performance. The radar sensors have advantages
in terms of being nearly independent of weather conditions and
have a relatively high accuracy in range and radial velocity mea-
surement. The PMD camera has the advantage to measure the tar-
get details like target extension or size in the three dimensions width,
height and distance. With a data combination procedure it is possi-
ble to combine these sensor advantages and improve the situation
analysis in general, measure the target positions more accurate and
finally improve the accident detection procedure.
Fig1: Research car Audi A8 equipped with 24 GHz and 77 GHz radar sensors and PMD camera
Institut für Nachrichtentechnik
Automobil-Radar
Fax: +49 (40) 42878-2281
Eißendorfer Straße 40
D- 21073 Hamburg
Version 09.02.2006
Signal Processing Strategies for a MultiSensor Pre-Crash ApplicationStephan Müller, Henning Ritter, Hermann Rohling; TUHH
Marc-Michael Meinecke, Mark Gonter; Volkswagen
Tested Sensors for Pre-Crash Situations
The 24 GHz radar sensor
The 77 GHz radar sensor
The PMD Camera
Three different types of sensors were investigated during the project. A
24 GHz near field radar sensor, a 77 GHz long range radar sensor
and a PMD camera have been installed in an Audi A8 experimental
vehicle. As shown in Fig 1 the radar sensors are mounted behind the
front bumper and the camera is mounted behind the front shield.
The 24 GHz radar and the PMD camera have a very broad beam
compared to the 77 GHz long range radar sensor. These two sensors
are covering the important area in front of the car, as to be seen in
Fig 2, whereas the 77 GHz radar sensor has a very long beam.
The 24 GHz radar sensor is able to measure the target range and
angle very precisely. Because of its FMCW (Frequency Modulated
Continuous Wave) waveform it can also determine the radial com-
ponent of the target speed.
Since the measurement frequency is
24 GHz the radar is highly independent
on weather conditions and very reli-
able. In addition this sensor uses only a
small bandwidth of about 150 MHz
within the 250 MHz ISM band and
therefore no restrictions of the so-
called “SARA package solution” are ap-
plied. In Table 1 the technical details of the sensor are listed.
The 77 GHz radar sensor from Bosch is a stan-
dard ACC long range sensor. Like the 24 GHz
radar sensor it is capable of measuring the
target position and speed. In Table 2 the
technical specifications of the sensor are
given.
The PMD (Photonic Mixing De-
vice) camera shown in Fig 5 pro-
vides a gray scaled image like a
mono camera. In addition in in-
tensity modulated infrared light
from two external light sources is
used to measure the range and
angle for every camera pixel. By combining the two measurement
principles it is possible to measure not only the target's position but
also its extension in width and height. In Table 3 the technical details
for the camera are shown.
Fig 3: 24 GHz radar sensor
Fig 2: Sensor field of view
Fig 4: 77 GHz radar sensor
Fig 5: PMD camera
Observation area 50° in azimuth
Angular accuracy 2°
Maximum range 70m
Range accuracy 0.20m
Velocity range > ±22m/s
Velocity accuracy 0.3m/s
Table 1: 24GHz radar sensor parameter
Observation area ± 8° in azimuth
Angular accuracy ±0.1° 0.4°
Maximum range 150m
Range accuracy 0.50m
Range resolution 2m
Velocity range -60m/s - +20m/s
Table 2: 77GHz radar sensor parameter
Observation area 55° in azimuth
Resolution H x V 64 x 16 Pixel
Angular accuracy 2°
Maximum range 40m
Range accuracy 0.02m @ d=0m
0.40m @ d=25m
Table 3: PMD camera parameter
Institut für Nachrichtentechnik
Automobil-Radar
Fax: +49 (40) 42878-2281
Eißendorfer Straße 40
D- 21073 Hamburg
Version 09.02.2006
Signal Processing Strategies for a MultiSensor Pre-Crash ApplicationStephan Müller, Henning Ritter, Hermann Rohling; TUHH
Marc-Michael Meinecke, Mark Gonter; Volkswagen
Pre-Crash Theory
Stationary targets
Moving Targets
Currently there are a lot of different safety measures in discussion;
starting with seatbelt pre-tensioners, up to active hood concepts
and innovative airbag technologies. From a deployment point of
view they can be divided into two large categories: First reversible
and second non-reversible actuators. Since the developed system
is intended to increase safety special care has to be taken to se-
cure deployment reliability.
To detect an upcoming crash it has to be known if the car is still able
to avoid the collision either by braking or by a steering maneuver or
not. Since for relevant speeds and small target sizes the braking ma-
neuver always consumes more time than the steering maneuver
here only the steering maneuver is considered. In case the collision
cannot be avoided the reduction of crashworthiness is of high inter-
est as well.
If the driver wants to avoid the collision with a stationary target he
can drive to the left or to the right hand side, as shown in Fig 6. Nev-
ertheless a certain area will be overdriven by the car regardless of
the driver's maneuver.
This area is called the crash area and is marked in red in Fig 6.
Under the assumption of a single track model the crash area is only
dependent on the speed of the car and the static friction between
the tires and the ground which includes also the condition of the
road (dry, wet, icy, etc.). This assumption gives a good approximation
of the vehicle behavior, while the complexity of calculations is still rea-
sonable.
Since any obstacle inside the crash area will be hit by the car (crash
is unavoidable) it is now possible to identify a pre-crash situation
under the assumption that the situation around the car is known.
When the pre-crash situation is detected the corresponding time to
crash (Time to collision) at this moment can be estimated by equa-
tion (1).
The model of the safety area in the chapter above is only valid for sta-
tionary targets and has to be extended for trigger decisions with mov-
ing objects. The prediction that an object inside the safety area will
definitely be hit by the car is not sufficient because the object might
move away from this zone in the time the own car is reaching the de-
picting point.
Therefore the trigger algorithms for moving targets not only have to
predict the positions of objects but also the superposition in time do-
main. This extension of the theory by the time domain can be con-
sidered from two different points of view:
A moving target (regardless if extended in x and y or a point target)
has to be at certain point of time on a finite area if the accelera-
tion is limited. This area can be calculated by using the equation of
motion for a given maximal acceleration (positive and negative).
For an extended (in x and y) and moving target it is possible to cal-
culate the period of time in which this object is placed over a cer-
tain point in the x-y-plane. This time period gets smaller the higher
the assumed dynamics of the considered object.
One major difference arises in the data processing for both points of
view. In the first case a trigger decision would mean to test if the
whole set of whereabouts for the object is inside the safety area. If
this is the case a crash is inescapable. This test is admittedly of a high
calculation complexity and therefore not feasible for multi target situ-
ations. The second case is to be evaluated more easily.
At first the movements for the own car as well as for the adversarial
object have to be predicted and possible intersection points are cal-
culated which could end up in an accident. A second step is to cal-
culate the time periods in which the own car is placed over the set of
intersection points and the certain points of time when the detected
object is to be there. If an overlap in time of the position of the own
car and the object's position occurs a crash will definitely happen
and can be predicted by this method.
In either case the knowledge of the velocity components in direction
of x and y is indispensable for the data processing algorithms as they
are the basis for the prediction of the movements. A radial velocity di-
rectly to be measured by a radar sensor is not sufficient, but the ve-
locity in direction of x and y can be estimated by tracking algorithms.
�
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Fig 6: Geometrical view of the maneuver crash area
(1)target distance
ttcrelative targetspeed
=
Institut für Nachrichtentechnik
Automobil-Radar
Fax: +49 (40) 42878-2281
Eißendorfer Straße 40
D- 21073 Hamburg
Version 09.02.2006
Signal Processing Strategies for a MultiSensor Pre-Crash ApplicationStephan Müller, Henning Ritter, Hermann Rohling; TUHH
Marc-Michael Meinecke, Mark Gonter; Volkswagen
Performance of single sensor and combined
sensor set-up
Safety on public roadsConclusion
Each individual sensor provides a certain performance in terms of
correct deployment probability and false alarm rate. In case that
more then one sensor is available in a car the idea comes up to
combine this entire individual trigger signal to increase reliability of
the predictions. Both concepts are analyzed in this chapter.
For the acceptance of the system by the customer an extremely low
false alarm rate depending on the concrete actuator is required. It
varies from approximately 10-6 /km for reversible actuators to approx-
imately 10-12 /km for non-reversible actuators. These numbers can
be derived from the acceptance by the driver as well as from prod-
uct liability constraints of the manufacturer.
To maintain this false alarm probability for the reliable deployment of
non-reversible actuators the detected pre-crash situations from each
sensor are combined to a single alarm trigger. The architecture of the
deployment algorithm is as follows. All targets seen by the individual
sensors are tested if they are present in the crash zone. If so, a trigger
is send by the risk assessment/ deployment algorithm of the individual
sensor. All the outputs of the individual deployment signals are com-
bined in a statistical manner. The combined deployment signal is
connected to the (virtual) actuator in the experimental car.
The first approach in combining the individual trigger signals is the
conjunction. In this case the alarm signal is only generated if all sen-
sors have detected a pre-crash situation. The combined false alarm
probability of the system is also called the false decision probability
because it describes if the situation is interpreted correctly. Addition-
ally this also leads to a reduced detection probability.
For testing the performance of the sensor and the trigger algorithms
selected standard road situations were taken. In one of these situa-
tions the test car was driving straight against an obstacle. Taking this
as an example the system performance shall be shown. To test if the
algorithms are detecting a crash, the car is hitting the obstacle. With
this test also the time to collision at the moment when the alarm is is-
sued can be measured. To test if the algorithms are not triggering an
alarm in near crash situations now the car is making an evasive ma-
noeuvre such that it is not hitting the obstacle but passing it at a dis-
tance of a few centimetres.
These numbers should be handled with care since the data base is
very small (only 33 trials in this example) and an artificial situation is
considered.
Another important figure is the time to collision at the moment when
a pre-crash situation has been detected. The time to collision gives
the maximum time that an actuator has to react to the detected situ-
ation. In Fig 9 the achieved TTCs for the front crash situation are
shown as a histogram for the single sensor and the combined alarm.
In this diagram it was assumed that the given actuator has an activa-
tion time of 100 ms.
As it can be seen in Fig 9 in the most situations the deployment time
abides with the requirement of 100 ms. There are only two exceptions
in which a lower deployment time occurred. This was caused by ex-
treme driving manoeuvres on a proving ground which is not typical
for an ordinary driver. In these situations the target entered the safety
area from the side and the therefore the time to collision was not suf-
ficient for the actuators described above. Nevertheless the majority
of the alarm triggers have been sent with sufficient time.
It can be seen that under “typical” public road situations the system
still has a level of false alarm rate which is too high in the context of a
system intended to increase the safety. Nevertheless it is capable to
detect a pre-crash in standard situations reliable. Furthermore the ma-
jority of the alarm triggers were sent at the required time and there-
fore the safety actuators could have reacted in a sufficient way.
The future work will emphasis on the reduction of the false alarm rate
by optimizing the single sensor tracking and analysing the combina-
tion of the single sensor alarms to improve the robustness of the
alarm decision.
Fig 9: Achieved TTCs in a front crash situation for the combined alarm.