RECOGNITION OF DRIVING MANEUVERS BASED ACCELEROMETER SENSOR

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 11, November 2018, pp , Article ID: IJCIET_09_11_149 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed RECOGNITION OF DRIVING MANEUVERS BASED ACCELEROMETER SENSOR Sarah K. Al-luhaibi and Aiman M. Said Department of Information System, University of Nizwa, Nizwa/ Oman Munaf S. Najim Al-Din Department of Electrical and Computer Engineering, University of Nizwa, Nizwa/ Oman 3 ABSTRACT Nowadays, Smart phones devices became more intelligent, easily accessible, low cost and very flexible. Those devices are equipped with intelligent sensors that are appropriate to collect data that can be employed for analyzing driving behaviors and recognition systems. All of those features make smart phones a suitable alternative to existing high price driving monitoring and recognition systems. In this paper, we used smart phone accelerometer sensor to collect real driving data. Four driving maneuvers patterns were extracted from our data including Left lane changing, Right Lane changing, Braking and Accelerating Maneuvers. Distinct features have extracted from each type of maneuvers based on accelerometer sensor only. Support Vector Machine was trained to classify maneuvers. Our classifier got an average total accuracy equal to 86.1 %. We improved that the Accelerometer sensor data in x axis is sufficient to recognize lane changing maneuvers while accelerometer sensor data in y axis considered adequate for Braking and Accelerating recognition. Keywords: Accelerometer, Maneuvers recognition, Sensor, Smartphone, Support Vector Machine. Cite this Article: Sarah K. Al-luhaibi, Aiman M. Said and Munaf S. Najim Al-Din Recognition of Driving Maneuvers Based Accelerometer Sensor, International Journal of Civil Engineering and Technology, 9(11), 2018, pp INTRODUCTION The development of Smart phones during the last ten years has enabled those whom own Smart phones to carry a significant computational and processing power at all times. All newly Smart phones are now equipped with a wide range of sensing devices such as Accelerometer, Gyroscope, Magnetometer and many other sensors. Simply, sensors are used to measure the forces acting on the smart phone devices and/or humidity and temperature and/or light falling on the device editor@iaeme.com

2 Sarah K. Al-luhaibi, Aiman M. Said and Munaf S. Najim Al-Din The widespread of smart phones have led to the development of numerous numbers of applications such as healthy applications and Environment sensing applications. Smart phones are now playing an important role in the field of Intelligent Transport Systems (ITS) such as driver behavior detection and vehicle monitoring [4]. Accelerometer Sensor played an important role in driving detection and monitoring systems. The aim of this paper is to illustrate how accelerometer sensor data are sufficient to extract driving maneuvers features. We proposed a classifier-based approach to recognize driving maneuvers. The organization of this paper is as follows. The paper consists of two major sections followed by a conclusion. Related works is discussed in section two of this paper. Section 3 provides an explaining for methodology in this paper. 2. LITERATURE REVIEW The emergence of advanced mobile-phones and the integration of the newly emerged sensors technologies have led to the resurgence of smart phones which is led to the development of numerous numbers of applications. Low cost, processing capacity, memory storage, Wi-Fi and navigational properties, capabilities to doing computational and intelligent processes all helped the smart phone to be a good and efficient platform for driver behavior recognition and monitoring systems. Various driving behavior systems have been proposed based on smart phone devices to avoid in-vehicle data recording systems problems. A novel driving behavior recognition system based on smart phone's sensors (accelerometer, gyroscope and magnetometer) data are developed to recognize normal and aggressive driver [2]. Detection system has been proposed to recognize driver maneuvers based on smart phone's accelerometer sensor [5]. Also, System for recognizing aggressive driver behavior has been proposed based on smart phone sensors including accelerometer, magnetometer and gyroscope sensors [6]. A detection system based on smart phone GPS and accelerometer sensors was presented to recognize driver distraction with mobile phone [7]. A mobile application based on smart phone's accelerometer sensor as well as OBS-II was presented to detect dangerous and inefficient driving, this application measure and alert driver about his dangerous driving directly [8]. Based smart phone's sensors including accelerometer and orientation, a system was proposed to detect unsafe and rash driving [9]. A system based on smart phone's accelerometer sensor proposed to detect car accidents in a timely manner, the system sends accident information directly over the internet to predefined server [10]. These systems showed that smart phones are viable source for data collection to recognize driving maneuvers. In this paper, accelerometer sensor built in Android smart phone was used to collect real data driving. 3. METHODOLOGY Most of the android sensors have a standard for coordinate which are three axes coordinate system (see Figure.1). If the device moved to right side the acceleration will increase (positive values), while accelerometer measure negative values when device move to the left side. Accelerometer values in X-axis considered enough to detect left or right lane change maneuvers [12]. If the device moved straight ahead or back, accelerometer Y-axis will change. The accelerometer changes in Y-axis considered sufficient to detect acceleration or braking events [13]. The accelerometer Z-Axis is used to measure the up/down movement of the device, which are typically caused by road anomalies [14] editor@iaeme.com

3 Recognition Of Driving Maneuvers Based Accelerometer Sensor Figure 1 Coordinate System of Vehicle and Smartphone 3.1. Data Collection Accelerometer sensor built-in smart phone is used as a tool to collect data. Three axis of accelerometer used to read raw data. Data was collected from 15 drivers. Visualization of the raw data shows how data are very noisy (see Figure.2 (a)). Data pre-processing was done in order to make signal suitable for feature extraction task as well as to get accurate results. Moving Average Filter was applied for all raw data to remove noise problem. Moving Average Filter (MAF) is the optimal and the easiest filter for reducing random noise. In spite of its simplicity, the moving average filter is effective for time domain signals. As its name suggests, the moving average filter operates by averaging a number of points from the input signal to generate each point in the output signal [15].A significant influence of Moving average filter (MAF) on the noise problem was observed. Very smooth and clear data have resulted (see Figure.2 (b)). Figure 2 (a) Raw Data (b) filtered Data 3.2. Driving Maneuvers Data Analyzing Based on time stamp ground truth of each maneuver, we segmented our data for maneuvers analyzing. Braking Maneuvers: The accelerometer values in y-axis for all Braking maneuver patterns shows a significant decreased with small fluctuations in x-axis for some time.all braking maneuver patterns shows approximately a unique shape like U shape or V shape regarding to y-axis with small fluctuations related to the noise. It is found that the Standard deviation of acceleration y-axis is large (see Figure.3) editor@iaeme.com

4 Sarah K. Al-luhaibi, Aiman M. Said and Munaf S. Najim Al-Din Figure 3 Braking Maneuver Accelerating Maneuvers: Analysis based on observation was done; Accelerometer values in y-axis for all accelerating maneuver patterns shows a significant increased with small fluctuations in x-axis for some time. Small standard deviation related to acceleration in y- axis. All accelerating maneuver patterns showed approximately a unique shape like or (see Figure.4). Figure 4 Accelerating Maneuver Lane change Maneuvers: it is noticed that a dramatically decreasing in Acceleration values for x- axis. Additionally, very small fluctuations were observed in y-axis in left lane change maneuver (see Figure. 5(a)). In Right Lane change Maneuvers was observed valuable increasing in Accelerometer values for x-axis with a small increasing or decreasing in y-axis (see Figure. 5(b)). Figure 5 (a) Left Lch Maneuver (b) Right Lch Maneuver editor@iaeme.com

5 Recognition Of Driving Maneuvers Based Accelerometer Sensor 3.3. Classifier Training Support Vector Machine (SVM) is a powerful learning method; it has made a significant progress in some applications such as prediction of traffic flow, traffic speed and travel time [16]. In this paper, we trained SVM classifier to recognize driving maneuvers based on statistical features for x and y axes of acceleration. 10-fold cross validation was used to face over fitting problem. 4. RESULTS We used Mat lab (R2017a) to train the classifier, using Classification learner Application built-in Mat lab Software. Mean and Standard Deviation provided the most classifier accurate. We used Confusion matrix to evaluate the classifier performance (see Fig. 8). Figure 8 Confusion Matrix The accuracy performance was achieved equal to 86.1 %; recognition rate equal to 85%, 87%, 79%, 92% for Accelerating, Braking, Left and right lane change respectively. Table-1 shows precision and recall metric for each one of manoeuvres. Table 1 Svm Performance Metric Maneuver Precision Recall Accelerate 0.9% 0.85% Brake 0.9% 0.87% Left Lch 0.79% 0.79% Right Lch 0.83% 0.92% REFERENCES [1] S. Abhijit, Orientation and Displacement Detection for Smartphone Device Based IMUs, IEEE JOURNAL, Vol. 5, n. 12, pp , [2] V. Nagpal, Android Sensor Programming By Example Book (1st edition, Packet Publishing Ltd, 2016). [3] M.Muharu, Enersave API: Android-based power-saving framework for mobile devices, Future Computing and Informatics Journal, Vol. 2,n.1,pp editor@iaeme.com

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