Nericell: Rich Road and Traffic Monitoring using

Mobile Smartphones

Prashanth MohanVenkat PadmanabhanRam RamjeeMicrosoft Research India, Bangalore

Presented byAli Khodaei

Traffic Monitoring Developed Countries

GPS based tracking is adequate

Infrastructure support exists

Developing Countries Bangalore ≠ <your

favorite US city>

What’s Different?

PotholesRoad bumpsVaried vehicle typesLiberal honkingChaotic intersections…

Why Rich Monitoring?

Find least stressful route

Widespread distribution of mobile phones

Road and Traffic Monitoring

Without deployed infrastructure

Using existing mass of mobile phones

Mobile Phones

~3 billion phones worldwide

~300 million phones in India

115 million of 1 billion phones sold worldwide in 2007 were smartphones

7 million added in India each month

Mobile Phones

•Mobility•Computing + communication + sensing•Far more capable & ubiquitous than special purpose sensors

Nericell Overview

Using sensors individually Accelerometer

drive quality

Microphone honking

GSM radio and GPS localization

Using sensors in combination

Virtual reorientation of accelerometer Distinguishing pedestrians from stop-and-go traffic Triggered sensing

Energy Challenge

Measurements made on HP iPAQ hw6965 9

Accelerometer-based Sensing

Advantage: low energy cost Challenge: “disorientation” Analyses:

braking detection bump/pothole detection pedestrian versus stop-and-go traffic

Braking Detection

Braking impacts drive quality Two approaches:

GPS: high energy cost (600 mW on iPAQ hw6965)

Accelerometer: much cheaper (2 mW + 30mW)

Accelerometer-based braking detection:

Braking Detection braking would cause a surge in aX

because the accelerometer would experience a force pushing it to the front.

To detect the incidence of braking compute the mean of aX over a sliding

window N seconds wide. If the mean exceeds a threshold T,

that event is declared as a braking event

Virtual Reorientation

Virtual Reorientation Euler Angles:

Any orientation of the accelerometer can be represented by Z-Y-Z (and other equivalent) rotations

Three Unknowns (angles): pre-rotation (φpre ), pre tilt (θtilt), post-rotation (ψpost)

Knowns: Gravity along Z Braking along X

Euler Angles

Virtual ReorientationUsing Gravity

Using Braking

Automatic Virtual Reorientation

Results: Virtual Reorientation

Braking detection with Virtual Reorientation

Differentiating pedestrians from stop-and-go traffic

Road bump detection

Locating a Road Bump Accelerometer is cheap, so keep on

continuously When bump is detected, use GSM tower

matching to estimate location median error: 130 m, 90th percentile: 610 m

Send bump report to server If several reports in same vicinity, server

triggers GPS on other phones for location fix

Sample result: GPS turned on only 3.2% of the time on a 20 km drive with one point of interest

Pothole Detection

Pothole Detection

High speed (≥ 25 kmph)z-peak: look for significant spike

Pothole Detection

Pothole Detection Low speed (< 25

kmph) z-sus: look for

sustained dip

Results: Pothole Detection Training data: 5km long drive with

44 bumps Test data: 35km long drive with

101 bumps

Microphone-based Sensing

Advantage: ubiquity Challenge: energy, privacy Analyses:

honk detection: triggered when accelerometer indicates a lot of braking

vehicle type: exposed versus enclosed vehicle

Honk Detection Efficient detector suitable for mobiles

discrete Fourier transform on 100 ms of audio

look for spikes in the 2.5-4 kHz range spikes at least T times the mean,

where T ranges between 5 and 10

Performance: 5.8% of CPU on the HP iPAQ

Honk Detection

Triggered Sensing Use cheap sensors to trigger the activation

of expensive sensors when needed

Examples:

Virtual Reorientation: accelerometer -> GPS

Localization: GSM -> GPS

Honk detection: accelerometer -> audio

Resources

Trafficsense: Rich monitoring of road and traffic conditions using mobile smartphones P Mohan, VN Padmanabhan, R Ramjee -

SenSys'08 http://research.microsoft.com/

research/mns

Questions?!