SoundNet 1

SoundNet – Vehicular ad hoc communication system

Sunny Gandhi

Department of Computer Science, Troy University

SoundNet 2

SoundNet 3

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Abstract

Cars are pervasive in today’s environment. And, as the distance are getting longer day by day, road traffic is everyday reality accidents are becoming a leading cause of death by injury globally.The SoundNet presents, a technique that leverages the sound source localization methods in outdoor, using microphones to reduce the number of road traffic accidents in non-line sight vision scenarios or at Intersections.

To do this we generate the sound from the source which gets frequency shifted when it reflects from any moving objects passing through the lane. We measure the shift with the microphone to infer the positions of the object.SoundNet will describe the phenomena and the detection algorithm, demonstrate how it can be used for the other vehicle, which is non line of sight to detect its presence to stop the accident.

We checked the accuracy of SoundNet for speed it is 97% accuracy in term of finding the speed of the car passing by the microphone

1. Introduction

Each year in the United States, motor vehicle crashes account for about 40,000 deaths, more than three million injuries, and over $130 billion in financial losses. The pursue of advanced vehicle collision warning system is one of many efforts by auto makers and national highway traffic safety administration to reduce the crash rate [1], [2], [3], [4], [5], [6], [7]. Preliminary results have shown that the introduction of collision warning systems could dramatically reduce crash fatalities, injuries and property damage [1]. Studies carried out by Daimler-Benz and National Highway Traffic Safety Administration

(NHTSA) suggest that additional one second warning could reduce the rear end and intersection accident rate by 50 to 90% [8], and Eaton reported that the actual truck fleet accident frequency was reduced by 73% after the fleets being equipped with the VORAD Forward and Side Collision warning systems by Eaton [8]. Despite the fact that intersection collisions account for almost 30% of all crashes, intersection collision avoidance systems received less attention than the forward collision avoidance systems [2], [3], [6]. It is because the intersection collision problem is more complicated than rear-end crash and the limitations of the radar technology, the most widely used object sensing method in vehicle collision avoidance systems. Most radar systems require line-of-sight for object detection. Yet in most intersection crash cases, the principle other vehicle (POV) is hidden from the line of sight of the subject vehicle (SV) until the last second before the collision. This renders ineffective most collision warning/avoidance systems that require line-of-sight for threat detection.

SoundNet is process of designing and developing the system capable of intersection collision warning using a new approach. SoundNet is based upon the sound energy emitted by the vehicles.Threat detection is detected by mapping sound frequency emitted by vehicles, understand its positions, relative speed and direction. By sharing the information between peers, each vehicle is able to predict potential hazards.

This system does not require a support of expensive infrastructure. Research under way to get the accuracy in their actual location with change of speed The result shown here were generated using MATLAB [The reason for choosing MATLAB as the analysis and simulation tool is that it has more flexible choices to support the simulation and is easy to do modification or data recording], two general

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purpose microphone to emulate an actual intersection scenario. As more research under this topic, we envision a near future when this type of system will demonstrate its advantage in public use, complementing the function of

other driver

assistance systems.

Section – II Research Objectives.

All moving vehicles makes some kind of noise (Sound); the noise can come from the vibrations of the running engine, bumping and friction of the vehicle tires with the ground, wind and many more sources. Vehicles of the same kind and working in similar conditions

(Here class means cars, trucks etc.) will generate similar noises, or have some kind of noise signature. This noise pattern gives a clue to detect a vehicle and recognize its class. Our research goal is to characterize noise patterns and use them to detected sound is from a vehicle of known type so the information can be transferred to the other vehicle in non-line of sight vehicle to avoid collisions. When travelling at different speeds, under, a vehicle emits different noise patterns. These noises can be sampled or digitized and grouped in a series of time slices (frames); then if the spectrum changes with time, it can be de- scribed in the frequency domain as the change of frequency spectrum distribution over frames. SoundNet uses frequency modulation techniques to extract sound signature from the frequency distribution of the noise produces by the vehicle. SoundNet was also able to find and provide solution to other related questions of direction and speed of the vehicle with change in time.

Section – III Related Work

Localization has been extensively studied in many research papers. Early works tried to build an empirical RF propagation model to estimate the location by received signal strength from multiple known access points [9]. This method suffers from meters of localization error and the model is very complex in order to take the dynamics of RF environment into account. Recent works focus on giving a quantitative description of the indoor environment, i.e. to create a unique fingerprint for a given room. Various types of fingerprints have been developed, mainly RF fingerprints and ambient fingerprints. VANET (Vehicular as hoc networks) represents a rapidly emerging research field and are considered essential for cooperative among vehicles on road. VANET uses moving cars as nodes in a network to create a mobile network. VANET turs every participating car into a wireless to create a mobile router allowing cars approximately 100 to 300 meters of each other

How many vehicles in the Lane

Location of vehicle

Change of speed

Direction of the

vehicles in action

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to connect and, in turn, create a network with a wide range . As cars fell out of signal range and drop out of the network other cars ca join in connecting vehicles to one a other so that a mob eke internet is created. It is estimated that the first systems that will use this technology are police and fire vehicles to communicate it with each other. [10]

VANET

Section – IV SoundNet

Vehicle noise is a kind of stochastic signal. A stochastic signal is defined as a stationary signal if its stochastic features are time-invariant, otherwise it is called a non- stationary signal. A vehicle that is making some noise of interest may be idling, or moving towards or away from an observing point (where the recording microphone is set); meanwhile it may be accelerating or decelerating etc. Over an extended observing time, the signal will generally not be stationary. But usually the recording microphone is fixed, and the vehicle's running conditions usually do not change very often if it is not moving; if it is moving, then a fairly short sound duration can be recorded. So vehicle sound signals can be reasonably treated as stationary, or as segments 0of stationary signal.

To treat the moving vehicle noise as a piece-wise stationary signal, besides the engine's running conditions, one important effect that has to be considered is the acoustic Doppler

Effect. The maximum Doppler Effect occurs when the recording microphone is set in the vehicle path.Let Delta v be the Doppler frequency shift, _ be the original frequency, _V be vehicle travelling speed, and V be sound propagation speed; then we have __=_ = _V=V . If the vehicle is travelling at 30 mph and the speed of sound is 343.4 m/s, the maximum Doppler Effect will cause about _4:2% change at the frequency component _. As the vehicle noise generally has a frequency spectrum with large low frequency components, and the recording microphone usually is set o_ road, the resulting Doppler shift, less than 5%, is not very conspicuous compared with the unpredictable changes in recording conditions. Experience shows that taking the sound as a stationary signal is reasonable.

A. Microphone settings – Microphone plays an important role in getting the data to calculate and get the modulation of frequency. The frequency set at 44 KHz range to get the noise bandwidth.

B. The Direction of Car is fixed in this project. It is fixed as parallel to the location of microphone .As shown in the diagram.

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Due to variations in hardware as well as filtering in sound and microphone systems. SoundNet requires some initial calibration to get the right frequency bandwidth. The SoundNet starts it will scan the frequency between 17-19 KHz and start recording the sound of incoming vehicles.

Here the sampling frequency is set at 8000 Hz and the channels (number of microphone is set at 2) SIGNAL PROCESSING – As the signals are processed in multi reflection path and full of noise, we need to pass the incoming frequency into low pass filter. Low A low-pass filter is a filter that allows signals below a cutoff frequency (known as the passband) and attenuates signals above the cutoff frequency (known as the stopband). By removing some frequencies, the filter creates a smoothing effect. That is, the filter produces slow changes in output values to make it easier to see trends and boost the overall signal-to-noise ratio with minimal signal degradation. We use moving average method of filtering.

MOVING AVERAGE METHOD

HAMMING WNDOW - After applying the filters, we need to apply hamming window for signal processing for better bin calculations.[11]

TIME DOMAIN the time domain information of the recorded sound frequency and its FFT function are recorded. To get the dominate frequency we used FFT transformation to the input signal.

FAST FOURIER TRANSFORM Using this important tool we can get out the frequency spectrum out of the input signal , this is important to get the dominate frequency from the input signal and to use that to get the location of the vehicle.

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The above shows the Fast Fourier Transformation of the original and filtered signal in MATLAB.

SPEED Calculation – Informal tests with 2 Microphone connected to laptop on which it is recording sound on MATLAB is done and the input signal graph is plotted in MATLAB to check the accuracy of the test.The test is done with 4 friends, 2 laptop and 2 microphone connected to same laptop so long connector wires are needed initially.

The experiment is performed in early morning hours where there is change of getting the maximum accuracy as less number of interface from other subjects [12]

The settings are as follows1. Microphone are at 10m distance from

each other.2. The car was driven at a speed of 20

mpg.3. Two reading were taken.4. One in going from A to B in forward

direction.

5. Other is going to A to B in reverse order the speed in reverse order was set at 10 mpg.

The figure shows the frequency in MATLABThe green part shows the matrix represent by the microphone close to the car and blue is the matrix information of the microphone which is far away at location B

The 2 spike represent the time when the car passes the microphone at point A and B

This is a typical time amplitude graph in MATLAB and this is represented the time the microphone started to listen to the sound of the car and till certain second, this goes it the duration of the event (till 12 sec), but we can set it to record the sound signal for more than that with minor change in the MATLAB recording process, but that will make the process more slow and heavy for the system development and deployment of the speed chart.

The chart showed the range speed response pattern generated in MATLAB. The chart displayed the range around 10 mpg, the reason of the error are not studied here and remain to be open problem for the final paper.

Time difference of arrival (TDOA) -

Time delay estimation (TDE) between signals received at two microphones has been proven

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to be a useful parameter for many applications recently. Five different time delay estimation methods are tried and tested in MATLAB for the purpose of getting the location of the vehicle. These methods are cross-correlation (CC), phase transform (PHAT), maximum likelihood estimator (ML), adaptive least mean square filter (LMS) and average square difference function (ASDF)[13].

Out of all method of TDOA we choose cross correlation (CC) methods to get the result. The CC methods cross-correlates the microphone outputs and considers the time argument that corresponds to the maximum peak in the output as the estimated time delay. To improve the peak detection and time delay estimation, various filters, or weighting functions, have been suggested to be used after the cross correlation [14].

TDOA is effected by the following elements of the surrounding

1. Reflection from the surroundings.2. Background noise from other vehicles.3. Observation time interval.4. Reverberation

This effects the Sound to Noise ratio (SNR) and decrease the performance of the time delay estimators. Out of above the background noise and reverberation effects the [15]

We did the TDOA in MATLAB and get the input from 2 microphone in time domain

SIMULATION IN MATLAB - Firstly, the simulation is carried out in simulated noisy environment, where the additive noises and in equation (1) are assumed to be Gaussian. They are uncorrelated and have zero-mean. ) (1tn) (2tn)In MATLAB, a zero mean Gaussian signal can be generated by using command ‘randn’, whose variance is one. Choose the signal-to-noise ratio (SNR) as 16.36dB and the time delay as 243T seconds, where T is the source signal sampling period ( in MATLAB, the sampling frequency of signal ‘mtlb’ is 7418Hz, T is 1.3481x10-4s). The cross-correlation results using CC, PHAT and ML are shown in Figure below

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. The x-coordinate denotes the time-lag, and the y-coordinate denotes the resulted cross-correlations. Choose the value 243T as the actual time-delay, using ASDF algorithm, the simulation result is plotted in Figure below

And the SNR is calculated as 16.38dB, where the insignificant SNR value difference is due to that the Gaussian noise is generated randomly in MATLAB. It is worth noting in Figure 7, the y-coordinate yields the error square of the two difference noisy signal instead of their cross-correlations. It is apparent that the time lag corresponding to the minimum error is the same as the actual time delay.

The actual output of MATLAB is shown below

Conclusion

The pursuit of advanced vehicle collision warning system is one of many efforts by auto makers, national highway traffic safety

administration, and U.S. department of transportation (Intelligent Transportation System), etc., to reduce the accident rate. Most of the existing collision warning and avoidance systems are designed for forward and side collision warning. Very recently, NHTSA did a detailed study of intersection collision scenarios, and developed a prototype for intersection collision warning. Yet, the function of their system is limited by the use of radar as the only threat detection tool, as most of other collision warning systems do. Observing the limitations of present systems, we designed and implemented a new low cost peer-to peer beacon-based collision warning system. This new system doesn’t have the limitation of requiring line-of-sight to operate properly, thus it can handle the hidden vehicle problem. We could answer most of the answers of the research objective , the problem is to get the accuracy of location with varied pace of the vehicle movement. This can be done by doing some further research into the TDOA methods and studying some other related papers. From this topic we studied important topic related to the TDOA and how to use different method of object triangulation to get the location of the vehicle.

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Reference:

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[2] J. Pierowicz, E. Jocoy, M. Lloyd, A. Bittner, and B. Pirson, “Intersection Collision Avoidance Using ITS Countermeasures,” Tech. Rep. DOT

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HS 809 171, NHTSA, U.S. DOT, September 2000, Final Report.

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[12] Vehicle Sound signature recognition by frequency vector principle component analysis. Huadong Wu

[13] G. C. Carter: “Coherence and time delay estimation: an applied tutorial for research, development, test, and evaluation engineers”, Piscataway, NJ: IEEE Press, 1993.

[14] C. H. Knapp and C. G. Carter: “The generalized correlation method for estimation of time delay”, IEEE Trans, Acoustic, Speech, Signal Processing, vol. ASSP-21, pp. 320-327, August 1976

[15] M. Jian, A. C. Kot and M. H. Er “Performance Study of Time Delay Estimation In A Room Environment”, IEEE, Circuits and Systems, vol.5, pp.554-557, June 1998