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The use of mobile phone is increasing during driving in China. Due to the mobile ... Keywords: Driver stress; Driving simulator; Accidents; Safety. 1. Introduction.
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Using Mobile Phone Sensors to Detect Driving Behavior Pushpendra Singh, Nikita Juneja, Shruti Kapur Indraprastha Institute of Information Technology (IIIT-D) New Delhi, India
A mobile phone application that uses combination of in-built sensors (GPS, micro-phone and accelerometer) to detect driving behavior: • Supports Android Operating System 4.0 and above • Modular Architecture to allow further extensions Application Design • Two phases – Data Collection and Data Analysis • Data collection runs as a service in background • Implemented on Android devices Data Collection • Accelerometer Data – detects lane changes, turns, sudden acceleration and deceleration. • GPS and Network-Provider Data – detects location of vehicle. • Microphone Data – detects honking and indicator sounds.
Detecting Driving Behavior Number of honks, speeding, sudden brakes can be inferred directly from the data. Audio data and accelerometer data is correlated to further find new patterns of rash driving: • A turn or lane change not accompanied with indicator sound • Combining frequent honking with slow speed to indicate congestion.
Future Work Using advanced machine learning techniques to find out patterns from the collected data. Enabling crowd-sourcing, to collect mass data and analyze other subtle reasons behind rash driving. For example, under what conditions traffic, rush hours, etc. - a person drives rashly.
Data collected by doing multiple experimental runs in New Delhi, India.
Send a ‘How did you drive?’ report to the sources.
Data Analysis • Using collected data, patterns (stated at extreme right column) were jotted down in accelerometer and microphone data which signify honking, indicator sound, lane changes, turns, sudden acceleration and deceleration. • Verified using ‘Ground Truth’.
Report Visualization The driving anomalies were plotted on a Google Map (using the location data collected through GPS and network-provider) showing the entire path of journey along with rash driving behavior.
Collected Ground Truth by developing another application which was handled by a co-passenger.
A ‘HIGHSpeedBreaker’ here signifies that vehicle wasn’t slowed down enough before speed breaker, hence vehicle experienced an abovethreshold jolt. 12:49:38 is the time when this jolt was experienced.
Concluded Patterns from Audio Data Used audio finger printing techniques to detect patterns in collected samples.
Ideal patterns for horn and indicator
Horns detected in microphonerecorded data Concluded Patterns from Accelerometer Data • Speed Breaker - Peak in z-axis. • Left Turn - Sudden decrease in x-axis acceleration towards negative side. • Right Turn - Sudden decrease in x-axis acceleration towards positive side. • Left Lane Change - Initial decrease in x-axis acceleration followed by increments. • Right Lane Change - Initial increase in x-axis acceleration followed by decrements. • Sudden Deceleration - Sudden decrease in y-axis acceleration followed by a nearly straight line. The acceleration values of the x and the z axis also experience curves due to a sudden jolt. • Sudden Acceleration - Sudden increase in y-axis acceleration. The acceleration values of the x and the z axis also experience curves due to a sudden jolt.
