System Interface of an Integrated ISS for Vehicle

Application

M. A. Hannan, A. Hussain and S. A. Samad

Department of Electrical, Electronic and Systems Engineering

Faculty of Engineering and Built Environment, National University of Malaysia

43600 Bangi, Selangor, Malaysia

Abstract. This paper deals with the system interface of the integrated

intelligent safety system that involves the vehicle applications of airbag

deployment decision system (ADDS) and tire pressure monitoring system

(TPMS). Lab Window/CVI in C interface program is developed for prototype

implementation. The prototype implementation is the interconnection between

the hardware objects of the intelligent safety devices such as load cell,

Logitech web-camera, Cross-bow accelerometer, TPM module and receiver

module, data acquisition card, CPU card and touch screen. Integration of

several safety subsystems decision such as the image processing module,

weight sensing module and the crash detection module are fused for intelligent

ADDS. The integrated safety system also monitors the tire pressure and

temperature and analyze the TPMS data for tire condition classification. The

system interface developed the integrated system prototype performance that is

evaluated through several test runs. Their result proven the embedded

intelligent safety system is unique, robust and intelligent for vehicle safety

application.

1 Introduction

The term “intelligent safety” does not literally mean that intelligence resides inside

the vehicle. The “intelligence” implies here as an active essential part of the vehicle

that contribute to safety, security and driving comfort. Intelligent safety system

provides operation to ensure the safety and comfort level of the occupant in the

vehicle [1]. However, due to occupant expectation of high level of control and safety,

a large number of individual safety systems are needed [2]. This increased concern for

safety issues has resulted to design accurate integrated intelligent safety system that

involves features technologies, characteristic of safety issues and providing solutions

by monitoring, detecting, classifying, impending crash or unsafe driving conditions,

warning the driver, improving his or her ability to control the vehicle and prevent the

accident [3].

Many individual researches have been focused on the safety issues such as

occupant detection, classification and position, vehicle crash detection and its severity

analysis, TPMS etc. For example, occupant detection and characterization are the

fundamental importance [4] to improve the safety and comfort features of the

Hannan M., Hussain A. and Samad S. (2009).

System Interface of an Integrated ISS for Vehicle Application.

In Proceedings of the 3rd International Workshop on Intelligent Vehicle Controls & Intelligent Transportation Systems, pages 106-119

 SciTePress

occupants. However, it is a difficult task, despite the success of some of these

systems, the occupant detection such as human, non-human object and its

classification still pose a number of challenges with respect to real time

implementation and operation [5].

The vehicle crash detection is very helpful for preventative safety, preventing

accidents and collision for minimizing human injury when an accident occurs [6].

However, in past it has been a rather seldom discussed on research and theoretical

analysis of crash in the field of traditional engineering [7]. It is therefore NHTSA and

other safety concern made rule that vehicle crash detection and analysis are

mandatory for safety issue [8].

Similarly, the safety issue on TPMS is a

significant factor in the driving experience and

vehicle

performance [9]. Accordingly, the NHTSA made a legislation known as the

TREAD act in which after 31 October 2006, all vehicles in United States should have

the option of TPMS [10].

The system interface for the prototype implementation of intelligent safety system

is the most important. It is therefore, this paper pursue the interface program that are

used for the development of an innovative integrated intelligent safety system to

identify major hazards and assess the associated risks in an acceptable way in various

environments where such traditional tools cannot be effectively or efficiently applied.

The safety device provides data to the intelligent safety system that is useful for the

development of ADDS and TPMS. The objective of this paper are met by integrating

and developing the advance solution of the innovative safety issues likewise occupant

detection, classification and position, vehicle crash detection and its severity analysis,

TPMS and other detrimental issues.

2 System Integration

There are a number of limitations with the conventional architecture of the vehicles

intelligent safety systems. The safety measures are challenging and increasing

awareness of the automotive companies, due to customer expectation of high level of

control and safety. However, because of the wide variety of individual safety system,

it needs a large library of programs and much expensive individual platforms. This

wide variety of safety is continuous increasing the complexity that would lead to a

physical maximum and interdependence between the systems. So as to extract the

most of the individual demands, the platform need to be integrated, addressed a robust

algorithm and calibration process to optimized and validate the vehicle integrated

safety system. The principle motivation behind the system integration is to reduce

individual systems safety device management cost in the performance domain. The

integrated intelligent safety system aims to provide heterogeneous workload

management concepts and functions to the safety issues and validate them based on

performance diagnosis of collected monitoring data in a developed platform.

The intelligent vehicle safety platform identifies the hardware and software

execution environment of a system. The hardware platform identifies a set of

hardware objects associated with processors. The system interface provides a high

level of interface between software objects running on different processors that

control the hardware. The proposed integrated safety system deals with the safety and

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comfort issues in the modern vehicle such as TPMS, occupant detection, detection

and position and vehicle crash detection. This integrated safety system gathers data

through a set of sensors, collected the data through acquisition processes and

eventually reacts through a CPU and finally, safety issues are monitored in a LCD

display unit.

3 Method and Algorithm

We have developed the algorithms for ADDS and TPMS. In ADDS, we have

developed the individual algorithms for occupant detection, classification and position

based on weight sensing and image process as well as for vehicle crash detection. In

weight sensing, in order to classify, the weight measurement data are used with logic

combination. We consider that less than 10 kg as a non-human object, while the child

setting is ‘10 kg<child<35’ and the adult setting is 35kg<adult<100kg. For example,

when an adult occupant is on the seat, the adult logic is true, and child and non-human

object logics are false, which the dynamic output classifies as adult and displays its

decision on the monitor. In position detection, we have calculated the centroidal

distance Fx

and Fy are as follows.

)4321(

FFFF

xFx

+++

−

(1)

)4321(

FFFF

yFy

+++

−

(2)

where F1, F2, F3 and F4 are weight forces of the four sensors, while x and y are the

distances from the centre to the sensor in x and y directions, respectively. These

calculations of Fx and Fy provide the appropriate position of the occupant.

In image processing, the algorithmic approach for detection and classification of

occupant, non-human object and non-object as shown in Fig. 1. The proposed system

is functioned with the combination of fast neural network (FNN) and classical neural

network (CNN), in which the FNN is trying to extract any positive detection

including false detection. Post-processing strategies are applied to converts

normalized outputs and for adjusting intensity histogram equalization or lighting

correction function are also applied to solve false detection. The output of FNN is

then feed to CNN to verify which region is indeed the system detection. This

proposed combined network is quite robust on detecting accuracy and computation

efficiency rather than single network, which is unable to fully eliminate false

detection problem.

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Fig. 1. Neural network algorithm for the occupancy detection.

This variable, Δv(t) is an essential parameter for crash detection that can be found

in the algorithms development. The change in vehicle velocity, Δv(t) is obtained by

computing the integration of the acceleration signal [hannan elsevier] as shown in

below.

)(cos)()(

δωω

+−==Δ

∫

tAdttatv

(3)

Selecting a threshold value of the vehicle velocity, V

, is therefore required to

facilitate the decision making of whether or not an effective crash has occurred. Such

threshold V

value can easily be determined from the lowest speed of an effective

crash defined by NHTSA i.e. 22.54 km/h. In order to detect crash, the developed

algorithmic steps are as follows,

i) If Δv(t) ≥ V

, then output =‘1’; DECISION: Effective crash is detected.

ii) If Δv(t) < V

, then output =‘0’; DECISION: Effective crash is not detected.

An increase in vehicle speed during crash increases the crash severity factor. The

change of velocity, Δv(t), over a period of time, T, at the detection state can be

computed since the integral over the noise component is approximately zero. The

circuit for computing Δv(t) can be designed using systolic architecture to achieve the

real-time speed. The output of the detection state is fed into a data acquisition card for

system development.

In TPMS methodology, there are two ways to acquire data from the sensor

likewise, using a successive approximation algorithm or by a threshold check. A

successive approximation provides an accurate conversion of the sampled temperature

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or pressure reading into an 8-bit value. In the threshold check, the DAR is preloaded

with a threshold value during standby/reset mode to detect whether the pressure or

temperature has crossed a particular level. The receiver module is capable of

receiving both OOK and FSK through UHF receiver that communicates with the CPU

by a SPI. The UHF receiver detects and demodulate the signal through Manchester-

encoded bit stream, sending out the important data to the CPU that is monitor in the

display unit. The TPM and receiver module is loaded with a simple software program

to effective the functionality of the hardware. The assemble code for TPM tire module

is written using the “WIN IDE” integrated development environment and

programmed into RF2 through the programmer board that transmitted data to the

receiver module. Receiver module communicates with the UHF receiver using the

Turbo C Borland C compiler run under DOS. A function “TPMReceiverModule”

created in the main interface program is called in UKM.dll to monitor pressure and

temperature that is extracted from TPM receiver through SPI connection to CPU.

4 Prototype Structure

The hardware prototype is a vital representation of final design of integrated safety

system. It is also the basic tool to find deep bugs in the hardware. This is why; it has a

crucial step in the whole hardware structure design of embedded system.

This system implementation is developed through the physical interconnections

between hardware objects using standard hardware design technique. The whole

developed system structure consists of the following hardware objects such as sensors

as tire pressure monitoring modules, load cell weight sensor, Logitech web-camera

(WC) and Cross-bow accelerometer (AC) crash sensor, data acquisition card for

analog to digital conversion, CPU card, touch screen for deploying result and ATX

switch mode power supply (SMPS) as shown in Fig. 2.

Fig. 2. Hardware structure of integrated prototype system.

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5 System Interface

In this interface program, data are acquired from the weight sensor inside the

passenger seat and crash accelerometer sensor fixed on the vehicle bumper through

AXIOMTEK AX10410A acquisition card. This weight and crash sensor provides

analog signal that received by CH0 to CH6 of the A/D converter of DAQ card from

the 4 weight sensors and a 3 axis of crash sensor. A web-camera is connected to the

CPU through USB. The program firstly determined whether the seat is empty, human

or non-human something is on it according to the logic combination of measured

weight sense and image on the seat. Then classify the occupant size based on weight

measurement data with logic combination. For example, when an adult occupant is on

the seat, the adult logic is true, and child and non-human object logics are false, which

the dynamic output classifies as adult and displays its decision on the monitor. The

interface system also determined the centriodal position of the occupant to find

whether the occupant is in good position or not. Occupant detection, classification and

position along with vehicle crash detection decision provides decision to the ADDS.

TPM receiver module is connected to the CPU through SPI that extracted pressure

and temperature for monitoring and provide warning system to the display unit.

Details interfacing connection among the external sensors and power supply to the

data acquisition card and CPU are shown in Table 1 and Table 2.

Table 1. Connection the external sensor to the DAQ and CPU.

DAQ and

CPU

Sensor T

pe Wire Color

Channel 0 Weight Senso

Blue (Left

Bottom)

Channel 1 Weight Senso

Violet (Left Top)

Channel 2 Weight Senso

Orange (Right

Top)

Channel 3 Weight Senso

White(Right

Bottom)

Channel 4 Crash Senso

Violet (X-axis)

Channel 5 Crash Senso

Yellow (Y-axis)

Channel 6 Crash Senso

Black (Z-axis)

USB Web-Camera USB Por

SPI TPM Receiver

Module

Serial Por

Table 2. Power connector on the power supply and CPU card.

Wire Colour Wire T

Yellow 12 vol

5 vol

Blac

Groun

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The displays are shown on a touch screen. The touch screen has three different

connectors likewise USB, VGA and 12 volt regulated power supply.

Table 3. Touch screen display unit connectors.

Connector on the Touc

Screen

Connection to the

Other End

USB connector

Connected to the

USB connector on

the CPU card.

VGA connector

Connected to the

VGA connector on

the CPU card.

Power connector, Red wire

– 12 Volt Black wire –

Ground

Red wire goes to

the Yellow

connector on the

SMPS and the

Black wire goes to

the black

connector on the

SMPS.

6 Interface Program

The system interface between the software and hardware is developed based on Lab

Window/CVI in C programming language. The Low level driver called

“c:\cvinterface\UKM.dll” is written as a Win32 DLL file where the functions inside

the DLL are called by the Lab Window/CVI C program. In this DLL file, the function

called “Func1” processes the analog signal received by CH0 to CH6 of the A/D

converter of DAQ card from the 4 weight sensors and 3 axis of crash sensor. The

function “HumanDetection” provides the decision based on weight sensing whether

the occupant is adult, child or empty. The function “ImageProcess” is called inside the

UKM.dll to perform the face detection. This function returns a 1 if the image captured

by the web-cam is detected as “human” else if it detects a “Non Human” the function

returns a 0.

This 1 and 0 is fused with the logic combination of weight sensor to detect

occupant as adult, child, non-human object or empty. The function “CrashSensor” is

responsible for whether crash is generated or not. The function for position detection

“PositionDetection” calculates the centroidal distance of x and y axis from the

UKM.dll that display is the GUI and provides the decision for occupant position.

Finally, the function “ABagParm” provides the airbag deployment decision upon

fusing logic combination of occupant classification, position and vehicle crash

detection decision. The function “TPMReceiverModule” also called in UKM.dll to

monitor pressure and temperature that is extracted from TPM receiver through SPI

connection to CPU. The Fig. 3 shows the details program flowchart diagram of the

UKM.dll.

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

Fig. 3. Interface program flowchart of the integrated system.

7 Experimental Results and Discussion

The experimental results and graphical user interface on the touch screen graphs

representing real-time field applications of the integrated intelligent safety system.

The results contain for network interface processing, image and signal processing for

occupant detection, classification and position, vehicle crash detection and its severity

analysis towards ADDS and the performance monitoring of TPMS. Usually the real-

time constraints vary up to 1 minute. However, in our prototyped hardware, the

execution vectors of whole system are derived from the experimental measurements

within 50 ms. The results in decision and graph on the GUI, the resource library is

assumed that contain CPU, data acquisition card, sensors and their interface between

sensor and CPU. Details of experiment results and its analytical discussions are given

below.

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7.1 Results and Discussion

Occupant detection, classification and position is confirm based on the logic fusion of

image and signal processing data. In image processing, the intelligent safety system is

used two-data sets of images in the experiments to test the detection performance of

human face and non-human object, which are distinct from the training sets. The first

sets consist of 253 test images, whose have a wide variety of complex background in

the various environment and scale changes for the object of interest along with some

occlusion and variations in lighting. 25 human face image of interest is taken for a

total of 253 test image. The second data sets contain 112 test images that have been

collected from 7 non-human object of interest is taken. The systems undergo the

bootstrapping cycle with ending up 4500 to 9500 zero samples, to evaluate the

performance of true detection of the test images and the rate of false detection from

the image of natural scenes that do not contain human face or non-human object.

Table 4 shows the performance of human face detection results of various

methods on the test set 1 and compare with other systems in term of the number of

detect faces, miss faces, false detection and computation time. The successful rate of

the proposed method is 97.6 %, with 6 false alarms. It should be noted that the

number of false alarms is quite small in compared to methods Yacoub et al. [11] and

Fasel et al. [12] which is 347 false alarms. This may show the capability of the

combination of two networks to highly separate human face from non-object

examples. The higher performance of Rowley et al. 1998 [13] is likely due to the size

of training data. We used a 7344 human face images and 8000 non-object examples,

while Rowley et al. 1998 trained with 16000 face images and 9000 non-faces images.

However, the technique is less efficient than our techniques in term of the false

detection and response time. On the other hands, Yacoub et al. [11] shows a very fast

time processing but have a drawback of higher false alarms.

Table 4. Detection Rate of Set 1 on Different Methods.

Method

Human

Detect

(%)

Miss

Human

(%)

False

Detec

Proces

Time

FNN+CN

97.63% 2.37% 6 2.3s

Rowley et

al.

97.86% 2.14% 13 0.013M

Yacoub et

al.

84.31% 15.69% 347 0.7s

Fasel et

al.

96.8% 3.2% 278 3.1s

Similarly, Table 5 shows the summarized results of non-human object on the test

set 2 and compare with other systems. We found that non-human object detection rate

is 96.42%, which mean 108 out of 112 numbers of non-human objects are detected.

The false detection rate is 3.58%, which is lower that Agarwal et al. [14] and others

methods [15]. However, the average process time is almost same with others method

providing additional calculation on CNN. Based on the results shown in Tables 4 and

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5, we can concluded that both human face and non-human object detection system

make acceptable tradeoffs between the number of false detection and detection rate.

Table 5. Detection rates of set 2 on different methods.

Method

-Human

Object

Detect

Miss N-

uman

Object

No.

False

Det.

Proces

Time

FNN+CN

96.42% 3.58% 4 2.9s

Agarwal

et al.

94% 6% 30 3.6s

Mahmud

& Hebert

82% 18% 187 4.0s

Viola &

Jones

95% 5% 71 0.7s

Once the image processing part completed, the “ImageProcess” function provides

1 for human and 0 for non-human object. This 1 and 0 is fused with the weight sense

of the sensor situated inside the vehicle seat to provide accurate occupant detection

and classification for the integrated intelligent safety system. To illustrate the

performance, some exemplary results obtained from the prototype system are

demonstrated for instances such as, when the seat is occupied or empty. If occupied,

the occupant is classified as an adult, child or non-human object. Usually, human in

the seat provides its weight with positional variation. However, non-human object like

grocery bag is static and provides its weight without positional variation. It also

demonstrates that the position of the occupant can be determined for consideration of

safety issues in airbag application or for measuring the comfort level.

Figure 4 shows the centroidal position of y_centroid vs. x_centroid of the vehicle

front passenger seat of size 50x50 cm that indicates various position of the occupant

such as standard, good and bad with marking blue, green and violet lines,

respectively. Figure 4 (a) shows that occupant in the seat is good position with relax

mode aligning to the back of the seat. Figure 4 (b) shows that the occupant is in bad

position i.e. the occupant is aligned very much right to the seat. On the other hand,

Figure 4 (c) indicates that occupant is in bad position and very close proximity to the

airbag. In that case, our safety device will not provide decision for airbag deployment.

Similarly, Figure 4 (d) also indicates that the occupant is in extreme left of the of the

vehicle seat.

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Fig. 4. Occupant centroidal position calculation.

Next, we have implemented the experiments of the frontal static crash using crash

generating device and with the interface program. The experimental result of the crash

reaction force being applied to generate crash is shown in Fig. 5. We have tried to

obtain the reaction force during the repeated crash conduction at a time 51 sec to 80

sec. Figure 11 shows, during repeated crash, it gains a huge force of ~ 1000 N/m to ~

5800 N/m and definitely the crash velocity immediately before the crash is greater

than the 22.54 km/h. The reaction force depends on the crash velocity of the system.

It is stated that as velocity increases the reaction force also increases, which in turn

increases the crash severity. This is a situation that put the occupant at a higher risk.

Fig. 5. Vehicle crash reaction forces.

For analysis of tire data, the ‘Goodness of Fit’ statistical analysis for the model is

tested with all the variables. Figure 6 shows the ‘Goodness of Fit’ of the principle

component analysis with polynomial fitting. The fitting result of parametric model of

SSE is 0.1358 with 95% confidence bounds, which is close to 0 and indicates that the

data fits well. The value of the multiple correlations R

coefficient is 0.8452, whilst

the adjusted R

value is 0.8267. Both reveal about 85% and 83% match in the

outcome which indicates a good fit. In addition, the RMSE value of 0.161, which is

close to 0, also implies that the data fits well. In Fig. 6 (b), residuals of the

polynomial fit appear to be randomly scattered around zero, which again indicates the

model perfectly fits the data under the study. In short, we have statistically proven

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that TPMS can play an important role to enhance tire safety, performance and

maintain reliable operation in combine with vehicle intelligent safety system.

Fig. 6. Principle component analysis and goodness fit.

The Fig. 7 shows the display unit of experimental results and decision monitoring

operation of the implemented integrated intelligent safety system towards ADDS and

TPMS. The ADDS is involved on occupant detection, classification and position,

vehicle crash detection and its severity analysis. The safety feature functions are

activated by pressing start button.

Fig. 7. Display of integrated intelligent safety system.

The display unit successfully achieved the decision application of safety features.

Several safety issues related decision performances are fused using logic combination.

In Fig. 7, its shows that when the system detect adult occupant and keeping good

position, the airbag will not be deploy, provided there is no crash detected by the

system. All the decisions in the display unit are shows as green mark. Accordingly,

the icons of occupant total weight, centroidal position, axis wise crash data and

position graph are displayed. For TPMS, the display unit also shows that all four tire

acquiring the real-time temperature and pressure data, respectively. Therefore, the

integrated prototype provides the optimum and their fused decision on various safety

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issues that is very useful related to the safety issues for vehicle driving assistance

system.

Similarly, when adult occupant is detected and keeping good position, airbag is

deployed only whenever the vehicle crash is detected.

However, if the occupant position is bad i.e. occupant is very close to the airbag

unit, the airbag would not deploy though the vehicle crash is detected. This is because

of the huge reaction force would hit the occupant due to close proximity between

occupant and airbag unit. This is a situation that put the occupant at a higher risk.

8 Conclusions

In this paper, the system interface of the integrated prototype implementation of

vehicle intelligent safety system has been presented. Lab Window/CVI in C interface

program is used for the real-time intelligent safety systems prototype implementation.

The safety system such as TPMS, occupant detection, classification and position,

vehicle crash detection and its severity analysis are integrated. The prototype is made

by developing the algorithms and methodologies of the hardware platform and system

interface program. The application of the embedded intelligent safety system has

resulted in successful real-time working device, which will provides the validation of

the performance diagnosis of the safety system. The contribution of the interface

system of this prototype are performance characterization, problem determination

and real-time work load data monitoring of a distributed safety issues and provides

safety warning, whose are applicable in successfully operation.

Acknowledgements

The authors would like to thank the Malaysian Ministry of Science, Technology and

Innovation (MOSTI) for funding this work through IRPA research grant 03-02-02-

0017-SR0003/07-03.

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