Stage 2 presentation

Street Traffic
Estimation System
Ashish Dhar
Motivation
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Traffic jams & congestion prevalent in most cities
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What will it do?
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A traffic monitoring system required
Estimate the state of traffic on roads
What will be its benefits?
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Will enable quick reaction measures
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Change time of traffic light signals
Commuters take alternative routes
Planning a better road network
The Key Difference
Unstructured Traffic
Our Focus
Structured Traffic
Previous Work Focus
The Indian Traffic Scenario
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Traffic is unstructured, lack of lane discipline
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Chaotic & dense traffic, Lot of maneuvering Difficult for video, radar, infrared & laser based systems  These assume some structure in traffic
Magnetic loops & sensors require traffic in lanes
 Impossible to count vehicles & estimate their speeds
Numerous type of vehicles
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Different types vehicles move together on the road
 Thinly spaced
Difficult to detect & classify vehicles Problem Statement
To estimate traffic patterns in India scenarios
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Low cost, easily deployable
Specifically,
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Volume of traffic (traffic density)
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Percentage of road occupied by vehicles
Movement pattern
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Stationary, stop & go, slow or fast moving
System Overview
Sensing Unit
Maximum ADC count without any external magnetic field !
Shifting the Mid-point Voltage
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Resolving out of range voltage issue
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Stepped down mid point voltage to 1.25V
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Inserted a voltage divider
Need to Improve sensitivity & detection range
Set/Reset Circuit Integrated with
Micro Controller
Testing & Verification of Hardware
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Objective: To verify the working of our sensing unit
Test signals for different vehicles at different distances, speed Vehicle Signature
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Sampling rate = 20Hz, Distance ~ 1.75m, vehicle = passenger car
It is possible to get vehicle signatures at standoff distance
Additivity Test
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Objective: To verify additive nature of magnetic signal
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Distance of sensor from: ­
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Can we employ amplitude as a measure of traffic density?
Minibus ~ 3.5 meter
Autorickshaw ~ 1 foot
Sampling rate = 20 Hz
Additivity Test
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Magnetic signal of a moving vehicle is additive to static one
Stationary vehicle results in static magnetic sensor value
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Does stationary traffic also give static readings?
Experiment to Study Correlation Between
Traffic Pattern & Sensor Readings
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Sampling rate = 40Hz, height = 1m, distance = 1 foot
Readings timestamped to one second resolution
Video camera used to record ground truth (state of traffic)
Methodology
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Two experiments of duration 14 & 11 mins
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All samples in 1 sec time window averaged
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Magnetic sensor readings logged at base station
Removed quantization error & smoothened plots
Ground Truth
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Determine traffic patterns by watching videos
Manually annotated traffic patterns
Group plots of similar traffic pattern for analysis
Magnitude= Magnetic Sensor Signal
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Magnitude=  ADC0 2  ADC12 
Static Traffic Characteristics
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Stationary traffic results in constant magnitude value at a deviation from base reading
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Different level of deviations with varying type and position of vehicle
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Cannot use signal amplitude as a measure of traffic density for static traffic
Partially Stationary Traffic
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Small vehicle close to the sensor gives significant deflection
Can result in false negative for stationary traffic if vehicles (2, 3 wheelers) keep crawling ahead
Far Lane Effect
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Large vehicles on far side of the road give same magnetic signal as small vehicles that are close to the sensor
Moving Traffic Characteristics
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Inter vehicle spacing & speed impact moving traffic magnetic signal
Observations
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Can identify no traffic condition
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Can identify stationary traffic
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Stable magnitude values at base readings
Stable value at some deviation from base readings
 Small vehicles close to the sensor distort stable waveform
 Maximum amplitude not an indicator of traffic density for static traffic
Moving traffic patterns difficult to capture with 1 magnetic sensor
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Distinct peaks can be seen as a measure of inter vehicles spacing
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Maximum amplitude as a measure of traffic density for moving traffic
Width, or rate of change of prominent waveforms as a measure of traffic speed
Conclusion
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Built & tested traffic sensing unit
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Interfaced wireless sensor node with magnetic sensor
Conducted experiments to establish correlation between sensed data & actual traffic
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Determined relationship between static traffic & plot characteristics
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Identified problem of near & far lane effects
Identified some characteristics of sensor signal that can be used to estimate moving traffic patterns
Future Work
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Benchmarking experiments
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Vehicle speed v/s signal amplitude & width
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Inter vehicle spacing v/s signal waveform
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Additivity Tests (both vehicles moving)
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Vehicle distance v/s signal amplitude
Hardware up­gradation
Conduct experiments for more varied traffic conditions
Build & test traffic estimation module
Extra Slides
“Composite” Signal Effect
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Our magnetic sensor is at standoff distance
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Picks up signals from multiple vehicles at different distances and moving with different speeds
Received signal is “composite” of several signals
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Deformed magnetic signal waveform
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Need to map “composite” magnetic signal to specific traffic patterns
Utility
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SMS based services that alert users about congestion
Automatic traffic light timers
GIS systems that suggest less congested paths Analysis tools that help manage traffic and plan extensions to the road network
Why Current traffic Technologies
Do Not Solve Our Problem
Technology
Traffic parameters observed
Infrastructure requirements
Drawbacks
Magnetic Loops
Count vehicles moving on lanes
Requires digging up of roads
Count vehicles moving on lanes
Expensive intrusive
Camera Based Systems
Microwave Radar
Laser based systems
Infrared based system
Detect, count & classify vehicles
Can be mounted on poles along Low performance in poor visibility
Do not require laned traffic
roads
Needs to be secured as chances of theft higher
Large vehicles obscure smaller ones
Cannot detect stationary vehicles
Needs overhead structures
Overestimating speed & occupancy Estimate speed
Digging up the road not needed
values
specialized expensive equipment
Laned traffic
Count classify estimate speed of vehicles
Needs overhead structure
Assumes structured traffic
Digging up the road not needed
specialized expensive equipment
Can only detect vehicles
Expensive (cheaper than loops)
Fog, Snow and rain degrade performance
Assumes structured traffic
Ultrasonic Detectors
Anisotropic Magneto Resistive Sensors
Detection only. Speed detection ones are expensive
Overhead mounting points needed
Detect Classify and estimate speed of vehicles
Little digging
Assumes structured traffic
Expensive
Cannot work in dense, varied traffic AMR Magnetic Sensor
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Able to detect disturbance in earths magnetic field on their sensitive axis
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Stationary or moving vehicle create this disturbance
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Commercial AMR Sensors have 1, 2 or 3 axis
Advantages
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Depending on its ferrous content
Can manufactured in bulk at low cost
mounted in commercial IC packages
Small in size, highly sensitive, immune to noise and reliable
Exponential drop in detection ability with distance
AMR Magnetic Sensor for Traffic
Standoff
Flux Density Monitoring
Distance (foot)
(milligauss)
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Detecting Vehicles
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Estimating Speed
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Use simple threshold based mechanism
Use two sensors in quick succession at a fixed distance
Time difference between peaks used to calculate speed
1
270
3
75
5
10
10
2
12
<1
A typical automotive magnitude (flux density) versus sensor standoff distance
Classification ?
Ferrous object disturbance in uniform field
Source: Vehicle detection and compass applications using amr magnetic sensors How is AMR Sensor Used for
Vehicle Classification
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A vehicle can be considered to be a composite of dipoles
Forward
Depending upon where the ferrous content is present
Vehicle
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Each vehicle has a unique signal on the magnetic sensor
This is exploited for vehicle classification
Magneti
c SensorX
Y
Z
1 ft. above the ground
A Typical magnetic Sensor deployment & its orientation
Vehicle Classification Using AMR
Sensors
Earth’s field variations for a van driving over an AMR magnetic sensor
Earth’s field variations for a car driving over an AMR magnetic sensor
Source: Vehicle detection and compass applications using amr magnetic sensors
Traffic Density
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Percentage of a road area occupied by vehicles
Higher traffic density implies congestion
Map sensor deflection to approximate traffic density
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Deflection proportional to vehicle’s iron content
Iron content proportional to vehicle’s size