driving pattern recognition using parameter-lite ... id-2420143.pdf · driving pattern recognition...

International Journal of Research in Advent Technology, Vol.2, No.4, April 2014 E-ISSN: 2321-9637

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Driving Pattern Recognition Using Parameter-Lite Clustering Approach

Ramakrishnam Raju BHVS1, Valli Kumari V2, Azad Naik3

Department of Information Technology, SRKR Engineering College, Bhimavaram- 5342041

Department of CS & SE, Andhra University College of Engineering, Visakhapatnam-530003 2

Department of Computer Science, George Mason University, Fairfax, USA - 220303 Email: [email protected]

Abstract- Advanced driver assistance systems (ADAS) in automobiles are tools that provide a driver with necessary information that lead to an overall increase in vehicle safety. Classification and understanding of driving maneuvers can help in safety systems. In this paper, we introduced an unique framework which make use of smart phone sensors (accelerometer, gyroscope, magnetometer, GPS) to record the driving patterns and a parameter-lite clustering technique to detect the abnormal driving patterns of a vehicle. The empirical results on real dataset revealed that the proposed framework distinguishes the cluster resolution.

Index Terms- Driving Patterns, Smart phone sensors, clustering.

1. INTRODUCTION

Today in the fast-paced society, people are paying attention on arriving at destinations as quickly and safely as possible. Drivers often overlook safety measures to reach destination in time. Drivers, while operating a vehicle, are not always vigilant of all the hazardous situations such as sudden vehicle maneuvers and harmful road environment. These situations frequently contribute into accidents [Ivan et al.(2014)] The purpose of ADAS [Mohamed et al. (2012)] is to prevent drivers from hazardous situations by controlling their vehicles or alert them with the current vehicle event information. Although many types of ADASs have been compensated [Javier el al.(2013)] , they still have some constraints. First of them is that the detection process is complicated, and the second is lack of information regarding orientation and speed of the vehicle. However, clustering the raw data based on the state (orientation and speed) of the vehicle is important to amend the safety and performance in a wide range of applications.

In recent years, there has been remarkable growth in smart phones embedded with various sensors such as magnetometers, accelerometers, gyroscopes, Global Positioning Systems (GPSs), etc [Derick and Mohan (2011), Boon and Wan (2012)]. Sensory data has been used effectively in many applications including intelligent transportation systems that can offer users with new functionalities [Amin et al. (2008)]. This type of driver assist is only meant to complement the driver but not to take full control of the vehicle. Providing constructive feedback to the driver is crucial in alarming bad driving behaviors. Analysis of external sensors data for vehicle events is an effective area of research. Some effort has been rooted in the form of theoretical research and in a

sensible design. The main idea of this paper is to focus on classifying different driving maneuvers.

2. RELATED WORK

Mitrovi´c [Mitrovic (2005)], proposed the Hidden Markov Models (HMM) to train the data from accelerometer only for recognition of simple driving patterns. As HMM was used, events involved in the driving maneuvers has been marked manually, which is inefficient. Yong [Yong el al. (2013)] presented Discrete Hidden Markov models (DHMMs) to classify driving patterns of lane changes and cornering. Wang [Wang et al. (2010)] introduced a dangerous-driving warning system that uses statistical modeling to mine the safe or dangerous driving patterns from time-series data with very limited labeling information. A Driver Monitor System was proposed in [Baldwin and Duncan (2004)] to monitor the driving patterns of the elderly. This system was used mainly for data recording and offline analysis over extended periods of time. Derick [Derick and Mohan (2011)] focused on a driver’s ability to perform on the road. They proposed a technique using a mobile smart phone to detect various driving patterns of an operator mimicking the habits of a drunk driver.

Liang [Lianng el al.(2007)] developed an in-vehicle system to detect driver distraction that employs SVMs. The above systems predict the driver vigilance index based on driving behavior and vehicle movement instead of driver actual condition in real-time. Nericell [Mohan el al.(2008)] is a system that detects bumps, vehicle braking and traffic honking, using external sensors, such as a microphone, GPS, accelerometer, and Global System for Mobile communications radio for traffic localization. Services proposed by Wang [Wang el al. (2011)] resented a


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Fig. 1 Axes for Smart Phone and Vehicle

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model that can be used to distribute driver and vehicle information utilizing popular cloud computing. Zhang [Zhang et al.(2010)] proposed a pattern recognition approach to characterize drivers based on their skill level. Using a high-end vehicle simulator, they compare driver behavior such as steering control, lane changes, and traffic levels with an expert driver to help with category resolution. Learning and classification algorithms are then used to predict the driver’s overall skill level derived from these conditions

Moreno [Moreno et al. (1996)] combined data relating to steering wheel movement, acceleration and braking of the vehicle for detecting the drowsiness of a driver. Dillies [Dillies (1997)] employed a real-time fuzzy pattern recognition process in a neural network. The input to the neural network is sensory data from the steering wheel, speed and the accelerator. Liaw [Liaw (2004)] uses Fuzzy logic for driving pattern analysis.

In this paper, a single measuring device rather than expensive external sensors placed in the vehicle is used, which ultimately decreases the infrastructure costs. The mobile Smartphone used in this work, contains gyroscope, accelerometer etc., offering flexibility in methodology and in identifying different driving maneuvers. In order to classify these driving patterns, an approach involving parameter-lite clustering algorithm is used to minimize the user intervention in finding the driving pattern recognition.

3. METHODOLOGY

Driving maneuvers are composed of many events, such as lane changes, aggressive left and right turns, sudden breakings and sudden accelerations etc. This research classifies these driving patterns because these

patterns arise habitually than other driving patterns. Furthermore, they are much related to dangerous conditions.

The latest smart phones are equipped with many useful inputs for research, such as Camera, 3-axis Accelerometer, 3-axis Gyroscope, Proximity sensor, Magnetometer, GPS, etc. These devices are inexpensive and adaptable to research platforms that provide vehicle data collection reachable to academia as well as the general public. The proposed system focused on the accelerometer, gyroscope and GPS (for event location and speed) sensors only. For motion detection, the axes of the mobile phone and vehicle are as shown in Fig. 1[Yan (2013)]. The phone position in a vehicle is as shown in Fig. 2.

The maneuvers of interest are safe, normal and abnormal maneuvers. The abnormal maneuvers indicate potentially-aggressive driving that would cause danger to both pedestrians and other drivers. The normal driving pattern is that where track surface is smooth and straight without road anomalies. The IPhone placed in the vehicle records data continuously while the car is in motion. The path of the vehicle is as shown in Fig. 3. The track is divided into 3 parts. Part 1: Consisting of smooth surface with a left-turn, Part 2: a straight road without any road anomalies and Part 3: consists of a left-turn and potholes. The original photos of the track are as shown in Fig. 4. The recorded data from accelerometer and gyroscope sensors are plotted and is as shown in Fig. 5 and Fig. 6 The sensory data collected from smart phone placed in a vehicle consists of 163 data points with 6 attributes (3 from accelerometer and 3 from gyroscope) for each data point.


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Fig. 2. Phone position on a vehicle dashboard

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Part 2

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Fig. 3. Map of road using GPS coordinates and Google Earth. Road conditions: pothole (Part 3) and plain road

surface (Part 1& Part 2).

The second step of the framework involves applying Parameter-Lite Minimum Spanning Tree PLMST [Bhupathiraju and Vatsavayi (2012)] Clustering algorithm, on the sensory data streams that are obtained from a moving vehicle. The PLMST algorithm in the first phase constructs a minimum spanning tree (MST) with the recorded data points and then the inconsistent edges from MST are deleted

to determine the rough estimate of number of clusters present in the dataset. During the second phase the algorithm the similar clusters are merged together to uncover the distinguishable groups inherently present in the dataset. The results of the proposed system are presented in Table 1.

4. RESULTS AND CONCLUSIONS

The driving pattern evaluation performed by the proposed framework is displayed in Table 1. The results revealed that the prediction of driving maneuvers is accurate for both normal and aggressive driving behaviors as depicted in Fig 7 and Fig. 8.

The driving state predictions of the proposed framework are considered satisfactory for aggressive turns and potholes, whereas the other predictions, such as those of lane changes, drowsy driving etc., are to be tested.

In this paper, the driving patterns were investigated in order to be aware of unusual driving situation, which is helpful while seeking driver safety.

A PLMST algorithm that is applied to classify the driving patterns resulted in precise categorization of different behaviors. The test data were acquired from a smart mobile phone. For better understanding of driving situations, an array of patterns that are related to safety and performance of a driving assistant system, should be classified.

Fig. 4. Original Photos of track surface.

(a) Part 1 (c) Part 3 (with potholes) (a) Part 2


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Fig. 6. Accelerometer Recordings

Fig. 5. Accelerometer Recordings

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Table 1. Clustering results

Data Points Class Index Class Description

1-52 1 Safe Driving (Part1)

53-109 3 Normal Driving (Part 2)

110-163 2 Aggressive Diving and/or track with potholes (Part 3)

Fig. 6. Gyroscope Recordings

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Indicates Aggressive left and also presence of potholes

Fig. 7 Accelerometer recordings indicating Aggressive left turn and presence of Potholes on the track

Fig. 8 Yaw motion recordings indicating left turns


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