JSSSJournal of Sensors and Sensor SystemsJSSSJ. Sens. Sens. Syst.2194-878XCopernicus PublicationsGöttingen, Germany10.5194/jsss-8-19-2019A validation sensor based on carbon-fiber-reinforced plastic for early activation of automotive occupant restraint systemsA CFRP-based validation sensor for predictive automotive safety systemsSequeiraGerald Joysequeira@thi.dehttps://orcid.org/0000-0002-2302-8503LugnerRobertJumarUlrichBrandmeierThomasResearch and Test Center CARISSMA, Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, GermanyFaculty of Electrical Engineering and Information Technology, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, GermanyGerald Joy Sequeira (sequeira@thi.de)10January201981193523August201826November201814December2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://jsss.copernicus.org/articles/8/19/2019/jsss-8-19-2019.htmlThe full text article is available as a PDF file from https://jsss.copernicus.org/articles/8/19/2019/jsss-8-19-2019.pdf
In the automotive industry, sensors and sensor systems are one of
the most important components in upcoming challenges like highly automated
and autonomous driving. Forward-looking sensors (radar, lidar and cameras)
have the technical capability to already provide important (pre-)crash
information, such as the position of contact, relative crash velocity and overlap (width of
contact) before the crash occurs. Future safety systems can improve
crash mitigation with sophisticated vehicle safety strategies based on this
information. One such strategy is an early activation of restraint systems
compared with conventional passive safety systems. These integrated safety
systems consist of a combination of predictive forward-looking sensors and
occupant restraint systems (airbags, belt tensioners, etc.) to provide the
best occupant safety in inevitable crash situations. The activation of the
restraint systems is the most critical decision process and requires a very
robust validation system to avoid false activation. Hence, the information
provided by the forward-looking sensor needs to be highly reliable. A
validation sensor is required to check the plausibility of crucial information
from forward-looking sensors used in integrated safety systems for safe
automated and autonomous driving.
This work presents a CFRP-based (carbon-fiber-reinforced plastic) validation
sensor working on the principle of change in electrical resistance when a
contact occurs. This sensor detects the first contact, gives information on
impact position (where the contact occurs) and provides information on the
overlap. The aim is to activate the vehicle restraint systems at near T0
(time of first contact). Prototypes of the sensor were manufactured in house
and manually and were evaluated. At first, the sensor and its working principle
were tested with a pendulum apparatus. In the next stage, the sensor was tested in
a real crash test. The comparison of the signals from the CFRP-based sensor
with presently used crash sensors in the vehicle highlights its advantages.
The crash event can be identified at 0.1 ms after the initial contact. The
sensor also provides information on impact position at 1.2 ms and enables a
validation of the overlap development. Finally, a possible algorithm
for the vehicle safety system using forward-looking sensors with a validation
sensor is described.
Introduction
Predictive safety systems have the potential to deal with European Union aims
to halve the road casualties by 2020, compared to the baseline year of 2010,
and move close to the vision of zero fatalities from road accidents by 2050.
The validation of life-critical information from forward-looking sensors in
upcoming integrated safety systems and systems for safe automated and
autonomous driving demand a reliable and robust sensor concept. The
automotive industry is focusing on powerful forward-looking sensors like
radar, lidar, cameras, etc. that are presently used in advanced driver
assistance applications like lane assist, automated emergency braking, etc.
.
Many researchers are working on using these sensors not only to predict the
later vehicle state (collision can be avoided or collision cannot be avoided)
but also to predict the crash parameters like impact position, overlap,
crash velocity etc. in the case of an inevitable collision
. Based on
these predicted crash parameters, the thresholds for activating the passive
safety systems such as airbags and belt tensioners can be adjusted. Thus, the
time gained compared to conventional crash sensors can be used to deploy
larger airbags in order to cover the dashboard completely. The design of the
airbag system can be less aggressive to provide smoother retention and
avoid injuries to the neck and head. This increases the protection of
out-of-position occupants . The early information on the
position and overlap of the collision can be used to classify the crashes and
their crash severity. In combination with the information about the
occupants, it is possible to determine the best restraint strategy for the
actual crash scenario.
Theoretically, activating restraint systems based on forward-looking sensors
before contact (before T0) is possible. But these sensors face many
uncertainties like angular errors, weather influences, ghost objects,
reflections, etc. .
Applications critical for safety require the highest Automotive Safety
Integration Level (ASIL-D). One approach to achieve ASIL-D is by validating
the information from the forward-looking sensors using another sensor, which
works on a different physical principle . The validation
will also provide the required robustness against the false activation due to
the uncertainties of forward-looking sensors. This paper describes a concept
of a contact-based validation sensor that gives additional redundancy to
activate the passive safety systems with the first contact at T0. In Germany,
frontal collision is one of the impact scenarios with the highest percentage of
fatalities, at 21.27 % in 2016 .
Therefore, the research work is primarily focused on, but not limited to,
frontal collisions.
The significant technological breakthroughs in the research area of automated
driving, driver assistance systems and integrated safety systems have strong
potential to drastically improve vehicle safety
. According to the 2017 Road Safety
Statistics, around 25 300 fatalities occur due to road accidents in Europe.
This is about 6200 fewer fatalities compared to the statistics in 2010
. However, there is only little improvement when
compared with the statistics of the previous year (just 2 %). In order to
achieve the target of halving the number of road fatalities from 2010 to 2020
and to fulfill the Vision Zero Program, additional efforts and new sensor systems are
required.
Present crash detection sensors for frontal crash
Present cars are installed with a number of passive safety sensors working on
different physical principles for detecting different impact scenarios. Some
cars are equipped with only one central sensor, while premium cars are
equipped with additional sensors distributed in the proximity of the
crash zone. Figure shows a generic
architecture with the most common types of sensors used for detecting the
crash severity during frontal collisions. Acceleration sensors are typically
used to measure the sudden and harsh deceleration of the car caused by the
collision.
Different crash sensors used to detect frontal crash.
Sensors working on structure-borne sound (SBS) principle are a more recent
development, which measures the sound generated by the deformation of the
crash management structure transmitted through the car structure. This
technology has the advantage of improved distinction between soft and hard
crashes, and it is comparatively fast. Some cars are additionally equipped
with a pressure-hose sensor for pedestrian protection. This pressure-hose
sensor is installed on the rear surface of the energy absorber foam towards
the bumper beam side. It consists of a flexible tube with pressure sensors at
both ends and is specifically designed for pedestrian protection in low-impact energy and lower-speed scenarios.
Table shows the comparison of the various crash sensors
presently used in cars. The time needed to detect the crash severity depends
on the crash velocity, with less time for a higher crash velocity. The present
technology sensors take about 10–50 ms to detect crash events, depending on
the type of crash and sensor technology . Some crash detection systems employ two to
four sensors over the vehicle width to approximately detect the position
(middle, left or right) of the collision . Present crash
sensors can neither detect the exact position of the collision nor provide
the information about the overlap of the collision.
Comparison of various sensors used for detecting frontal crash.*
Sensor typeResponse time to detect crashAdvantagesDisadvantagesAcceleration sensors15–50 ms
Rigid
Low cost
Relatively slow compared to other sensors
SBS based10–30 ms
Fast in some crash scenarios
Better discrimination between hard and soft crash
Different car bodies have different SBS
Pressure hose10–15 ms
Low cost
Holes or blockage of tube cannot be detected
* Some values are derived from curves in
, , and .
Predictive crash detection concept
Forward-looking sensors can give information about the geometry of detected
objects. These parameters are compared with a database to find the matching
object class (truck, small car, tree, etc.). If there is no match found, an
unknown object is registered. This sub-algorithm can be called object
classification. Next, the crash avoidance system calculates possible
trajectories for the object and scans the neighboring regions. The possible
trajectories can be estimated using motion tracking, physical motion models
and other constraints. With this information, it decides whether the
collision can be avoided. If the collision is inevitable, the pre-crash
parameter prediction system is activated. In this phase, the position, overlap
and impact velocity are estimated using the information from the various
forward-looking sensors. Assumptions of vehicle and driver behavior are part
of the trajectory calculations and lead to additional uncertainty.
Predictive crash detection timeline.
Since the predicted values from forward-looking sensors might be inaccurate,
a validation system is required. The proposed sensor validates the occurrence
of the collision event. This validation gives the system robustness against
ghost objects detected by the forward-looking sensors. False activation
due to other sensor problems can also be avoided. For example, light and soft
items with large radar cross section might mislead a radar-based system but
would actually cause no harm to the occupants. Additional information from
the validation sensor can further increase safety. Shortly after T0, the
validation sensor also measures the position and overlap of the collision. These
values are then compared with the ones estimated by the forward-looking
sensors. Validated crash parameters are used for further calculations. If the
classification of the object is available, the object mass can be estimated
based on a worst-case assumption for the class. Alternatively, the object
information can be provided via vehicle-to-vehicle communication. This mass,
along with validated crash parameters, is used for crash severity prediction.
Based on the crash severity information, the decision on activation of
passive safety systems is taken. For example, in a frontal crash scenario
with the impact position on the driver side, with a small contact area of
approximately 25 % of vehicle width (so-called small overlap) and a crash
velocity of 64 km h-1, there is a high possibility of the driver
hitting the A-pillar of the car. Hence, the A-pillar airbag must also be
triggered in addition to the frontal airbags. Similarly, other crash
scenarios would require the triggering of some other combination of passive
safety systems. This paper is concerned with a concept for the validation
sensor and its investigation for checking its potential.
Figure shows the CFRP-based validation sensor integrated in the
front bumper of the car. Integrating such a sensor in the bumper material
ensures detection of the relevant crash parameters just at the time of
contact T0. In order to work under harsh conditions and for a broad range of
collision scenarios, the sensing element, system and subsequent algorithms
must be robust and reliable. With the critical crash scenario parameters of
less overlap and extreme angles, the sensing element must cover most of the
vehicle's front.
Validation sensor integrated in the vehicle front bumper.
Concept of CFRP-based validation sensorConstruction
The sensing element consists of three layers: a low resistive layer and a
high resistive layer separated by a soft material as shown in
Fig. a. Initially, a five-layer carbon-fiber composite and
a two-layer glass-fiber composite were manually fabricated. After the curing
of the composite plates, the top epoxy surface of the carbon-fiber composite
was removed to make it electrically conductive by a hand-sanding process. After
removing the epoxy layer, the top surface can be used as a low resistive
layer. For a high resistance layer, a resistive thread was stitched on the
glass-fiber composite using a sewing machine. The resistive thread consists
of 65 % silk and 35 % stainless steel (AISI-316L grade) material by
mass. In the end, both the layers were joined together using a general silicone
sealant. The silicone sealant was applied to form the hollow rectangular
shape on carbon-fiber composite as shown in Fig. a. Then
the high resistive layer was placed over it and allowed to cure under the static
load at room temperature. Figure b explains the
manufacturing process used for preparing the sensor prototype.
CFRP-based validation sensor: (a) construction, (b) manufacturing process and (c) resistance diagram of sensor at different contact points.
For testing purposes, thin plate-like sensor prototypes were manually
prepared in the laboratory. The final goal of the research work is to
integrate the sensing concept in the vehicle parts like the front bumper, which is
the final product, as shown in Fig. .
Working principle
Figure shows the electrical schematic diagram of
the sensor. A constant voltage is applied across the high resistive layer in
series with a shunt resistance. The shunt resistance is connected to measure
the current flowing through the sensor. The voltage across the resistive
layer Uin, the current through the circuit Iin (which can
be calculated from Ushunt and Rshunt using Ohm's law)
and the measured voltage drop Um over the known resistance
(Rknown=1 MΩ) are continuously measured with respect to
time.
Electrical schematic diagram of sensor: (a) no crash, (b) single-point contact and (c) multi-point contact.
During the normal driving condition, there is no electrical contact between
the high resistive layer and the low resistive layer. Therefore, the measured
voltage drop Um is almost zero. If a crash occurs, the resistive
layer is deformed, which causes the two layers to come in contact with each
other. As soon as the contact occurs, Um and Iin
change depending on the position and the width of the contact (overlap).
There are two types of contact which can take place during the crash event:
single-point contact (Fig. b) and multi-point
contact (Fig. c).
Single-point contact
When the high resistive layer touches the low resistive layer at only one
point along its length, it is called single-point contact. During a single-point contact, Um increases, while the current in the sensor
stays same as in the no-contact condition. This is because the overall resistance
of the sensor Ro does not change and is equal to the resistance
of the complete length of the resistance element. The overall resistance of
the sensor can be divided in two parts, as shown in
Fig. b:
Rl is the part of the overall resistance on the left side of the contact point.
Rr is the part of the overall resistance on the right side of the contact point.
Applying Ohm's Law, Um is equal to the part Rr
multiplied by the current flowing through the sensor. The right side
resistance part is given by
Rr=UmIin.
Based on the resistivity equation, the position of the contact point is given by
P=UmIin×Aρ,
where the following apply:
P is the position of the contact point.
ρA is the resistance per unit of length of the resistance element (resistivity divided by cross-section area).
Multi-point contact
In multi-point contact, the high resistive layer touches the low resistive
layer at multiple points along its length. The midpoint of the two extreme
contact points (leftmost and rightmost) is considered the contact point
for position determination. During a multi-point contact, Um
increases similarly to single-point contact. The overall resistance of the
sensor reduces, which causes Iin to increase. The overall
resistance of the sensor can be divided into three parts, as shown in Fig. c:
Rl is the part of the overall resistance on the left side of the leftmost contact point.
Rr is the part of the overall resistance on the right side of the rightmost contact point.
Rc is the part of the overall resistance between the leftmost and the rightmost contact point.
Since Rm depends on the resistivity of the high conductive layer,
which is very low, it can be neglected. Therefore, the overall resistance of
the sensor (Ro) is given by
Ro=Rl+Rr=ρA×(L-O),
where the following apply:
L is the total length of the sensor.
O is the distance between the leftmost contact point and rightmost contact point, which is the overlap.
Considering Ohm's Law, the overall resistance of sensor Ro is
given by
Ro=UinIin.
From Eqs. () and (), the overlap can be calculated by
O=L-UinIin×Aρ.
The position of impact in multi-point contact is given by
P=UmIin×Aρ+O2,
and by substituting Eq. () in Eq. (),
P=UmIin×Aρ+L-UinIin×Aρ2.
During the actual crash event, it is expected that, first, a single-point
contact will occur followed by multi-point contact after some milliseconds
resulting in the development of an overlap. An important aspect of the
sensor is electrical contact resistance. The contact resistance depends on
various factors such as contact pressure, surface roughness, material
property, environmental conditions, etc. . The
contact resistance can be neglected if it is very small when compared to the
overall resistance of the sensor. For measuring the contact resistance, a
point of contact was established by pressing with a finger at different
lengths,
and the measured resistance was calculated by dividing Um by
Iin, which is shown in Fig. c. The contact
resistance for the sensor is about 10 Ω (intercept of ordinate) and
can be neglected. Another important aspect is the robustness of sensor
against vibrations, which can occur in normal driving conditions as well as
during the crash. This can easily be achieved by a relatively stiff design of
the sensor, so only impact forces during the crash can overcome the
stiffness. Also the silicone sealant used in the sensor functions as a damper
to keep the fluctuations of the contact points (intermittent contact) within the required limits. In addition, the silicone sealant ensures high sensor
reliability by shielding the electrical contact surfaces against adverse
conditions (e.g., dust and de-icing salt).
Pendulum experiments
Pendulum tests were carried out to investigate the behavior of the sensor
under low-energy and low-speed impact conditions. This test setup was chosen
since it can simulate impact conditions, and at the same time, it requires low
costs, very little effort and is simple for carrying out different tests.
Experimental setup
Figure shows the schematic of an experimental setup
used for the pendulum tests. It consists of the pendulum structure, the
pendulum rod with the additional impacting mass, trigger (light gate), data acquisition system (LTT24, Labortechnik Tasler GmbH) and power supply. The
sensor prototype was fixed on the fixed beam of the pendulum structure. The
pendulum rod was attached with an additional mass of 11 kg to ensure that
the impact energy is high enough to deform the prototype. A wooden block was
fixed on the pendulum arm which acts as an impacting surface. The pendulum
rod, along with the wooden block and mass, was locked at an angle and was
released to impact the sensor. A light gate was used to trigger the
data acquisition system, when the wooden block first touches the sensor.
Pendulum experiments: (a) test setup and (b) test configurations.
The sensor was tested with impactors (wooden block) of two different widths
(10 and 20 cm) at three different impact positions each. The impact speed
for all tests was approximately 4.2 m s-1. Figure b shows the different tests
performed using the pendulum equipment.
Results and discussion
The raw signals Uin, Iin and Um are
measured with data acquisition system with a sampling rate of 10 kHz, which
is triggered using the light gate. These raw signals are converted into real-time values of position P and overlap O using Eqs. () and
() respectively. The position and the overlap signals are
illustrated in Fig. .
Results from pendulum tests: (a) different impact position (with 10 cm impactor) and (b) different overlap (at impact position 37.5 cm).
Results of pendulum tests.
Position results Overlap results TestActualActualEstimateResponseRelativeEstimateResponseRelativeno.position in cmoverlap in cmin cmtime in mserror in %in cmtime in mserror in %130.01028.83.94.011.011.040.0237.51039.84.06.16.09.810.0344.51046.24.73.89.711.13.0444.02049.05.011.412.81536.0537.520385.21.311.51142.5630.72032.45.35.510.912.745.5
The tests where the sensor impacted at different positions show the capability
of the sensor to determine the position of the impact as demonstrated in
Fig. a. The actual position (corresponding to the midpoint
of impactor) and actual overlap values were measured with a measuring scale
before the test. In addition to the pendulum motion, the pendulum arm also
had minor twisting motion. This caused an initial contact on the edge between the
impactor and the sensor, followed by a surface contact (as seen by the
signals in Fig. ). Hence, for the estimate of the position
value, an average value of the position signal over a time period of 3 ms was
considered. As soon as the contact occurs, the position value rises very
steeply (400–1000 m s-1) from zero until the position of the contact
point is reached. Then the slope of position signal depends on the contact
width or overlap development (which is comparatively low at about
100–150 m s-1). Therefore to select the starting point of average
time period, a time instance with considerable change in the slope of the
position signal was chosen. For the overlap estimation, the maximum value of
the overlap signal was considered. During the rebound phase (when the
impactor travels back after the impact), it was observed that the overlap
values are negative. The negative values of overlap are realized due to the
drop in Iin, which is caused by the increase in resistance of the
sensor because of tension loading on the resistive thread. When the high
resistive layer is in contact with low resistive layer, the above effect is
negligible, because the tensed part of resistance thread is in contact with
the CFRP-layer (low resistive layer) of the sensor. As this effect occurs
during the rebound phase, this effect is not of importance for a real crash
scenario because a safety decision should be taken within the first few
milliseconds. The values of actual position and overlap for different tests
are displayed in the Table . The table also lists the
corresponding sensor-estimated values for different tests. The relative error
for both position and overlap was calculated using the following equation:
Relative error in %=|actual value-estimate|actual value×100.
Table summarizes the results from the different pendulum tests
performed. It can be seen that the position values can be determined with a
relative error below 12 % at 3.9–5.3 ms after T0.
Figure b displays the results from two tests with
different overlap, which demonstrate the potential of the sensor to provide
information on the overlap. The average relative error of overlap estimation
is about 30 %. The estimation of overlap requires some time for the
deformation of the sensor until the width of the contact is equal to the
width of the impactor. Hence, the overlap can be estimated at about 9–15 ms
after T0 (at 4.2 m s-1). This time will reduce with increased impact
speed and therefore faster deformation. In a real crash with higher energy
and the sensor installed directly on the bumper, it might be that the
sensor is (partially) broken before the complete overlap is measured. Also
the development of the overlap will be based on the curvature of the vehicle
bumper. But some information is still better than no information, and the
available information can be used to validate the overlap development for
some samples, as explained in Sect. .
Crash test
After the proof of concept by the pendulum tests explained above, it was
decided to test the sensor in an actual vehicle crash test. The goals of the
crash test were the following:
to verify the sensor under the test conditions similar to a real vehicle crash,
to compare the working of the sensor with other sensors.
Crash test: (a) test setup and (b) sensor positions and measurement system (SAE reference for position values is the center of the vehicle width).
Results from crash test: (a) raw and filtered signal,
(b) sensor and vehicle reference system, (c) position
signal, (d) overlap signal, (e) important events during crash, (f) velocity
signal calculated from acceleration sensor, and (g) signals from pressure-hose
sensor (pressure measured is gauge pressure, i.e., reference is atmospheric
pressure).
Crash test configuration
Figure a shows the crash test setup, and
Fig. b displays the positions of the different sensors
installed in the vehicle during the crash test. The vehicle used for crash
test was a Volkswagen Golf IV. The crash test was carried out at
64 km h-1 against a rigid wall with 40 % overlap (40 % of vehicle
width will come in contact against the barrier). The vehicle was aligned in
such a way that the impact occurs on the driver side. Two types of
acceleration sensors were used. A triaxial acceleration sensor (Type-M0053A,
Kistler Instruments GmbH) was mounted in the passenger compartment of the
vehicle at the airbag control unit. The signals in the x direction alone
are of importance for comparison. The other type of acceleration sensor
(Early Crash Sensor, Continental) was installed near the crash zone, shown as
an additional sensor in Fig. . Two such
additional sensors were installed, one on the driver side and the other on
the passenger side. The positions of both sensors were close to the crash
boxes. They measure signals in one direction only (uniaxial) and were mounted
on the vehicle structure to measure in x direction. In addition to the
acceleration sensors, a pressure-hose sensor was installed in the
energy-absorbing foam on the bumper beam. The prototype of the validation
sensor manufactured was 130 cm in length (this was limited by the size of
the carbon-fiber roll), while the width of the vehicle was 173.5 cm. Hence,
it was decided to glue the prototype in such a way that the complete
impacting side (driver side) was covered. The
prototype was glued on the bumper using superglue (Loctite 401), as shown in
Fig. b. Since the CFRP-based sensor is glued to the bumper,
it measures position and overlap signals in terms of arc length (curved
distance) instead of straight distance.
The crash test data were recorded using two data acquisition systems. One
system was sampled at a rate of 100 kHz (M=bus Pro Analog module, Messring
GmbH), while the other was sampled with a rate of 1000 kHz (LTT24,
Labortechnik Tasler GmbH). The systems, along with the power supply box and
cable trailing system, were installed in the vehicle trunk, as shown in Fig. b. In addition to the data acquisition systems, three
high-speed cameras were installed to film from top, left and right views to
monitor the crash. The data acquisition systems and the camera system were
triggered by a standard trigger sensor used in a crash test. The trigger
ensures all the systems have the same zero time (T0) at the first contact of
the test vehicle with the rigid barrier.
Results and discussion
The vehicle crash management structure absorbed the energy of such a critical
collision, with some deformation caused to the A-pillar on the driver side. The
maximum deformation was measured to be about 95 cm (in the longitudinal
direction or x direction as per Society of Automotive Engineers – SAE – norms). It was observed that the raw
signals (Uin, Iin and Um) measured during
the crash test contained a noise of 50 Hz. This noise was induced by the DC
power supply and the cable system of the crash test facility. A 1-D median
filter (“medfilt1” function with n=300 in MATLAB software
) was used to remove the noise from the raw signals
as shown in Fig. a. After filtering the 50 Hz
interference, the position and the overlap signals were calculated as
explained in the pendulum experiments. Figure b
describes the reference points used for the crash test. As per the SAE norms,
the center of the vehicle width is considered the zero position or
reference for position values. For the CFRP-based sensor, this reference or zero
position is the end of the sensor at the passenger side. This end is at
40 cm (arc length) from the vehicle center, hence for calculating the
relative error, this value is subtracted from the position value. The actual
value for calculating the relative error is 17.38 cm, which is the
arc length corresponding to the offset 17.35 cm (i.e., the distance between
the edge of rigid barrier and vehicle center) and bumper radius of 150 cm
(see Sect. 6.3.2 for the calculation of the radius). The position and the overlap
values are shown in Fig. c and d, respectively. It can
be observed from the position signal that the crash event can be detected
within 1 ms of the crash. In Fig. e frames
extracted from the high-speed video at three important time instances are
displayed in frame no. 1–3. At about 1 ms after T0, the high resistive layer
contacts the low resistive layer at one point, displayed as point 1 in
the position signal. As the time progresses, the contact length of the high
resistive layer and the low resistive layer increases, which can be observed
in both position and overlap signals. The front of the car has a round
profile, which is the reason for the increase in overlap with time. At about
6.3 ms, the maximum contact width comes in contact with the barrier. At
about 10 ms the partial breakage of the resistive layer starts (the part of the
resistive thread in between the contact end points is broken). This was
observed by a drop in the current signal. At about 24.95 ms, the bumper is
deformed considerably. The passenger-side end of the bumper is detached from the
vehicle, causing bending of the sensor. At this time instance, the resistive
layer (the unbroken part of the resistive thread) connects with the
CFRP layer again and can be observed by the overlap and position signals. One of
the contact end points (passenger side) shifts to the other side of the
center (caused by the deformation and bending of the bumper). This can also
be seen by a drop in the position signal, since it is the midpoint of two
contact end points.
Three criteria were considered for comparison of the sensor signals. The first
criterion of comparison is the sensor response time for crash detection. A
crash event can be identified by a considerable change in the signal, which
should be higher than the signal noise. For all the sensors, the time when
the threshold of 10 % of the maximum value of the signal from the same
sensor is exceeded was considered as the time at which a crash event is
detected. The second criterion was whether the sensor can provide additional
information about the position of impact. The third criterion was whether the
sensor can provide information about the overlap.
Most of the algorithms for activating restraint systems use velocity signals
from the acceleration sensor for airbag activation .
Hence, the acceleration signals from the sensor are filtered with a low-pass filter
with a cutoff frequency of 100 Hz and then integrated to get the velocity
signals. The derived velocity signals were used for comparison. The central
acceleration sensor can only detect a crash event when the value is greater
than the threshold (10 % of 20.6 m s-1 is 2.06ms-1). The
acceleration signal from the additional sensor on the crash side (driver side) has higher amplitude compared to the non-crash side (passenger side)
sensor. For additional sensors, the maximum of velocity signal of passenger-side sensor was considered for calculating the 10 % threshold for both the
sensors. In addition to crash event detection, these sensors can provide
information, such as driver-side, central or passenger-side impact.
The pressure-hose sensor also displayed higher pressure on the driver side
than passenger side. The maximum pressure for driver-side sensor was about
144 kPa, while for the passenger-side sensor, it was about 25.3 kPa. For the threshold
the lower value of the two maximum values was selected (10 % of
25.3 is 2.53 kPa). Since the impact was on the driver side, the pressure
signal of the driver-side sensor crosses the threshold (2.53 kPa) earlier
(at about 3.40 ms) than the passenger-side sensor (at about 4.48 ms). The
position of impact can be calculated using the time difference of the two
sensors when the value crosses the threshold pressure, represented as
Position of impact=Tright-Tleft×c2,
where the following apply:
Tright is the response time when the passenger-side pressure sensor crosses the threshold pressure.
Tleft is the response time when the driver-side pressure sensor crosses the threshold pressure.
c is the speed of sound in air.
The position of the impact calculated with the above equation is with reference to the center of the pressure hose, which is same as the center of
the vehicle width (zero position). The pressure-hose sensor cannot provide
information on the overlap during the crash.
Comparison of different sensor types.
Crash event detection Position of impact Sensor typeThresholdResponseAbility to measurePositionResponseAbility totime in msrelative errortime in msmeasure overlapCentral acceleration sensora2.06 m s-120.72Cannot be measured––Cannot be measuredAdditional sensorb1.65 m s-114.59Only left, right or central–14.59Cannot be measuredPressure-hose sensorc2.53 kPa3.40Can be measured6.7 %4.48Cannot be measuredCFRP-based sensor7.3 cm0.10Can be measured0.8 %1.20Overlap development untilsensor partially breaks
a measured at vehicle tunnel, b measured near crash zone (additional sensor) and c pressure-hose sensor incorporated in the foam behind the bumper. Please see
Fig. .
Table summarizes the comparison of the sensor signals. It
describes the temporal advantage of CFRP-based validation sensor compared
to the other sensors. Since the CFRP-based sensor breaks partially before the
complete development of the overlap or contact width, the complete overlap
value cannot be estimated. But it provides the development of overlap until
the sensor breaks partially. The table also shows that CFRP-based sensor can
provide all the information which no other sensor can provide.
Overlap development: (a) vehicle bumper outline up to 30 ms
(red line represents overlap), (b) example frame showing the procedure for
drawing vehicle outline, (c) comparison of overlap development of CFRP-based
sensor with video analysis results, (d) comparison of overlap development of
CFRP-based sensor with predicted with upper and lower limits, and (e) geometrical
schematic for predicting overlap development.
Overlap developmentOverlap development from video analysis
To verify the overlap development measured by the CFRP-based sensor, a video
analysis was performed. The frame rate of the video was 1000 frames per
second. For video analysis, frames from 0 up to 30 ms were extracted and
analyzed. A vehicle outline was drawn on each frame extracted from the video
(as shown in Fig. a and b). The red line shows the
overlap for each frame. The length of this line was multiplied by the scaling
factor (real vehicle width or width of vehicle in frame) to find the actual
overlap. The comparison of the overlap development given by the video
analysis and by the CFRP-based sensor is shown in
Fig. c. It shows that the measurement is in
agreement with the test until 10 ms, after which the partial breakage of the
sensor occurs.
Prediction of overlap development
A vehicle crash against a rigid barrier is considered. The curvature of the
bumper can be assumed as a circle with a large radius R, as shown in
Fig. e. Point C is the center of the assumed
circle. B0 and B1 are the two contact end points. For the
prediction of the overlap development, the lateral movement of the vehicle
during the crash is neglected. Hence, the distance of point B0 from the
center line is constant and equal to the position of impact at the first contact
point (PT0= 17.35 cm). The distance of point B1 from
the center line varies with time, which is dependent on the vehicle
deformation x(t) during the crash.
Consider △CA0B0 in Fig. e,
where
α=sin-1PT0R.
Consider △CA1B1, where
β(t)=cos-1(Rcosα-x(t))R.
The overlap (O(t)) is given by the length of the arc B0B1:
O(t)=R×(β(t)-α)=R×cos-1Rcosα-x(t)R-sin-1PT0R.
The deformation of the vehicle during a crash can be calculated using
simplified mass-spring vehicle crash models . An
offset vehicle crash can be modeled by a simplified two-mass-spring model, as
explained by section frontal offset impact in . The mass of
the impacting side was considered to be 60 % of vehicle mass (vehicle mass
is 1090 kg), while the stiffness of the impacting side was assumed to be
16 538 N m-1. The remaining 40 % of the vehicle mass was attached
to the impacting-side mass by a stiff spring (spring stiffness of
165 380 N m-1). This two-mass-spring model was simulated. The
displacement response of the impacting-side mass is the deformation x(t) of
the vehicle for the offset vehicle crash scenario. Using Eq. () and
the deformation calculated from the simplified vehicle model, the overlap
development can be predicted. This predicted overlap development can be
compared with the measured overlap development using the CFRP-based contact
sensor for validation.
For the crash test performed, the radius that fits the test vehicle bumper
curvature is 150 cm. Two conditions (time shift and maximum limit) were
included in the predicted overlap development. Since the CFRP sensor requires
some time after T0 for deformation of the sensor (about 1.2 ms) to determine
the position at the first contact point, a time shift of 1.2 ms was added in
predicted overlap signal. The overlap development is limited by the maximum
contact width, which is the other condition. For the crash test performed, the
theoretical maximum overlap is the maximum possible length of arc
B0B1 (58.1 cm). This can be calculated based on the geometry and
considering point B1 at a distance of 55 cm from the edge of the rigid
barrier as shown in Fig. e as the maximum limit for
the overlap. Figure d shows the comparison of the
overlap development calculated using equations and the overlap development
measured using the CFRP-based sensor. As discussed in
Sect. , considerable deformation and bending of
the sensor at 24.95 ms can lead to contact at locations where the sensor
bends. Hence, for validation methodology a comparison of the overlap signals
until the sensor partially breaks (from 1.2 to 8 ms) should be considered.
This is also in line with the time requirements for crash detection and
airbag deployment.
As observed by the pendulum experiments, the measured overlap value is always
smaller than the actual overlap value. Hence, the lower limit is critical as
compared to the upper limit. Two conditions were considered for setting the
limit from the predicted value. The first condition is the relative change of
the predicted value, which should be considered when the overlap values are
low. The other condition is the maximum allowable change from the predicted
overlap, which should be considered for large overlap values. For the lower
limit at different time instances, the larger value of 50 % of predicted
overlap or predicted overlap minus 15 cm was chosen. Similarly for upper
limit, the lower value of either 1.25 multiplied by predicted overlap or predicted overlap
plus 10 cm was chosen. If for the 90 % of the time instances (between
validation time period, 1.2 to 8 ms), the overlap values measured by the
CFRP-based sensor are within the upper and lower limit, then the validation is
considered positive, otherwise it is considered negative. The conditions for
limits are assumed, and these should be optimized based on the requirements of
the complete safety system and the possibilities of the restraint system.
Generic restraint system activation algorithm.
A rigid barrier test represents a vehicle crash against a rigid wall.
Similarly, the equations for a crash with other objects (opponents) like
another vehicle, tree, truck, etc. can be calculated using the geometrical
simplifications. Forward-looking sensors can also provide information about
the geometry of the opponents . Considering this information along with the other crash
parameters (position, velocity, etc.), a particular equation for predicting
the overlap development can be selected. Using this methodology, the overlap
development can be predicted based on the information of forward-looking
sensors.
Algorithm for activation of restraint systems
Figure describes the algorithm for activating the occupant
restraint systems using a predictive crash detection system with a validation
sensor. This algorithm focuses only on the part with the validation sensor.
The pre-crash system provides different inputs which are to be validated. The
raw signals Uin, Iin and Um are measured
with the help of a microcontroller at high sampling frequency (at least
10 kHz) to ensure the required response time. The controller checks
regularly for the Iin≈0 condition. A failure of sensor
functionality by mechanical influences like breakage can be detected by
monitoring Iin. If the value is approximately equal to zero, it
signifies a breakage of the current path in the high resistive layer. In this
case, a message to visit the service center should be displayed. The
controller performs a conditional check on the measured value of
Um at predefined time intervals. If this value is above the
threshold value Uth, it signifies the contact of the high resistive
with low resistive layer. This correlates with the crash event. As soon as
Um crosses the threshold, the microcontroller calculates the
value of position P and overlap O. The position value P measured at the
time instance corresponding to the first considerable change in slope after
T0 (shown by point 1 in Fig. c) is considered for
validation. If the difference between the measured position value and
predicted value is within acceptable limits, then the validation of the position
value is considered positive, otherwise it is considered negative. The
overlap O development curve from T0 up to some fixed time Top
is compared with the predicted overlap development (an example method for
comparison is explained in Sect. ). If this
comparison is within the acceptable limits, then the validation is positive.
The sensor design should be optimized in such a way that the sensor should
not break in a worst-case crash scenario (for example, a crash with highest
possible velocity against a rigid object) up to the above fixed time
Top. If the validation for both position and overlap development
is positive, the safety strategy I (see Fig. 11), based on the validated crash parameters
from pre-crash sensors, can be activated. If the difference for either
the position, overlap or both is not within the acceptable limits, the
information cannot be validated. A sensor-break check for the current values
at timestamp near predicted T0 is performed. If the sensor-break check is
positive, either one or both sensors (forward looking and validation) could
be faulty, in which case it is not possible to decide which sensor to trust.
Hence, the safest solution is to use a safety strategy II, i.e., the use of a
fallback system, and wait for the response from conventional crash sensors to
detect and classify the crash event. When the sensor-break check is negative,
the safety strategy III could consider the confidence level of the signals.
As illustrated by the results from the tests, the validation sensor has high
accuracy in position estimation, while overlap cannot be estimated with high
accuracy. Hence, a high confidence level can be given to the position signal
of the validation sensor when compared to forward-looking sensors. Similarly,
different confidence levels can be allotted to the signals from different
senors, and a respective safety strategy can be planned.
Summary and outlook
The pendulum experiments and crash test
performed prove the concept, functionality and robustness of the CFRP-based
validation sensor. The following points can be concluded:
The results from the crash test demonstrate the capability of this sensor to
detect the crash situation at T0, which fulfills the requirement for the ultimate
aim to activate occupant restraint systems at T0.
The sensor also provides information about the position. The accuracy of the
position estimation is better than the pressure-hose sensor (the only
presently used sensor which can give information on position).
In addition, the sensor gives information about the overlap. The average
relative error of the overlap estimation is comparatively high. The overlap
information is intended to be used to verify the classified objects. This
task does not require high accuracy of overlap estimation for the validation
of the object class from the object classification algorithm of the pre-crash
system.
One of the important features of this sensor is the ability to implement
a fail-safe check. This function is one of the most important for meeting the ASIL-D
requirement.
The measuring principle of the validation sensor is relatively simple, and the
computation requires very few resources.
The prototype was manufactured manually in the laboratory. One of the
challenges of the concept discussed above is mass-scale production for
incorporating the validation sensor in the vehicle components. For the
realization of the sensor in a series application, the design of the sensor
along with the manufacturing process has to be optimized. The other challenge
is the material selection, in that the sensor functions for the desired
lifespan. The materials chosen, carbon fiber and stainless steel are
oxidation and corrosion resistant. The study of the carbon-fiber contact
under the temperature-cycle dither test and steady-state humidity and elevated-temperature dither test shows a negligible effect of ageing on carbon-fiber
contact over a period of 10 million cycles of operation
. However, the authors feel it is important to investigate
the effects of ageing on the sensor over a longer period in order to optimize
its functionality. These investigations are planned as part of the next
development phase for series application. In this paper, a conceptual
algorithm for predictive crash detection with validation is described. Some
of the parameters of this algorithm, like the confidence level of sensors and
methodology to utilize overlap information and its validation, for example, should be
studied in detail. The aspects mentioned above are planned by the authors to be future research
work.
The data that support the findings of this study are
available from authors, but restrictions apply to the availability of these
data, which were used under license for the current study and are thus not
publicly available. Data are, however, available from the authors upon
reasonable request.
The main concept of the validation sensor was a result of the discussions between GS, UJ and TB. As the main author, GS manufactured the prototypes,
planned and performed all the experiments, carried out data processing and
analysis, and drafted the paper. RL helped in deriving the conclusions from
the data and with structuring and writing some sections of the paper. UJ and
TB discussed experimental results and supervised the equations derived and
drafting the paper.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Sensors and
Measurement Systems 2018”. It is a result of the “Sensoren und Messsysteme
2018, 19. ITG-/GMA-Fachtagung” conference in Nürnberg, Germany, from
26 June 2018 to 27 June 2018.
Acknowledgements
The authors are grateful to Duy-Giap Tran (laboratory engineer) and
Christopher Ruzok (crash engineer) for their support. This work is supported
under the funding program Forschung an Fachhochschulen of the German Federal
Ministry of Education and Research (BMBF) under contract number 13FH7I03IA
(SAFIR IP3). Edited by: Eric
Starke
Reviewed by: two anonymous referees
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