Upload
duyen
View
41
Download
3
Tags:
Embed Size (px)
DESCRIPTION
The Pothole Patrol: Using a Mobile Sensor Network for Road Surface Monitoring. Jakob Eriksson, Lewis Girod, Bret Hull, Ryan Newton, Samuel Madden, Hari Balakrishnan MIT Computer Science and Artificial Intelligence Laboratory. Outline. Introduction Architecture Data Acquisition Algorithm - PowerPoint PPT Presentation
Citation preview
The Pothole Patrol: Using a Mobile Sensor Network forRoad Surface MonitoringJakob Eriksson, Lewis Girod, Bret Hull, Ryan
Newton, Samuel Madden, Hari Balakrishnan
MIT Computer Science and Artificial Intelligence Laboratory
Outline
Introduction Architecture Data Acquisition Algorithm Performance Related Work Discussion
P2 : A mobile road surface monitoring system
Hazardous to drivers and increasing repair costs due to vehicle damage
Determine “which” roads need to be fixed Static sensors will not do well – requires mobility! P2 is first of its kind Challenge : differentiate potholes from other road
anomalies (railroad crossings, expansion joints)
Challenge : coping with variations in detecting the same pothole. (speed, sensor orientation)
P2 successfully detects most potholes (>90% accuracy on test data)
P2 Architecture Vehicles have GPS and 3-axis accelerometer
<time,location,speed,heading,3-axis acceleration>
Opportunistic WiFi/Cellular connections with dPipe to cope network outages
Taxi Testbed 7 Toyota Priuses1
Soekris 48012 Embedded Linux Wifi Card Sprint EVDO Rev A3 Network card GPS
Some numerical facts 9730 total kms 2492 distinct kms 7 cabs 174 km with >10 repeated passes
1. http://www.carbuyersnotebook.com/archives/Toyota_Pruis_2006.jpg2. http://www.pkgbox.org/Soekris-4801.jpg3. http://gizmodo.com/gadgets/peripherals/two-new-sprint-evdo-rev-a-cards-pantech-px500-and-sierra-wireless-aircard-595-200423.php
1
2
3
P2 ArchitecturePotholeRecord
Clustering
Cab 1GPS
3 Axis Accelerometer
LocationInterpolator
PotholeDetector
Cab 2GPS
3 Axis Accelerometer
LocationInterpolator
PotholeDetector
Central Server
P2 Architecture
Distance Traveled vs. Total Hours
Across All Taxis
Lower line represents unique roads
Segments of roads that were repeatedly covered
258,021 unique road segments
DATA ACQUISITION
Accelerometer placement Dashboard Windshield Embedded Computer
GPS Accuracy Standard deviation 3.3m
DATA ACQUISITION
Hand Labeled Data Smooth Road Crosswalks/Expansion
Joints Railroad crossing Potholes Manholes Hard Stop Turn
DATA ACQUISITION
Loosely Labeled Training Data We know only types of
anomalies and their rough frequencies
Exact numbers and locations are unknown
Extends available training set
ALGORITHM Features of accelerometer data High energy events are potholes?
Not really! Rail road crossings, expansion joints, door
slamming are high energy events Accelerometer data is processed by
embedded computer 256-sample windows Pass through 5 different filters
ALGORITHM - Filtering
Input Raw accelerometer data 256-sample windows
INWindowsof all event classes
Speed High-pass z-peak
xz-ratiospeed vs. z ratio
OUTPothole Detections
ALGORITHM - Filtering
Speed Car is not moving or moving slowly Rejects door slam and curb ramp events
INWindowsof all event classes
Speed High-pass z-peak
xz-ratiospeed vs. z ratio
OUTPothole Detections
ALGORITHM - Filtering
High-Pass Removes low-freq components in x and z axes Filters out events like turning, veering, braking.
INWindowsof all event classes
Speed High-pass z-peak
xz-ratiospeed vs. z ratio
OUTPothole Detections
ALGORITHM - Filtering
z-peak Prime characteristic for significant anomalies Rejects all windows with absolute z-acceleration < tz
INWindowsof all event classes
Speed High-pass z-peak
xz-ratiospeed vs. z ratio
OUTPothole Detections
ALGORITHM - Filtering
xz- ratio Assumes potholes impact only side of the vehicle Identifies anomalies that span width of the road (rail crossings,
speed bumps) Rejects all windows with
xpeak within Δw (=32) samples from zpeak < tx X zpeak
Or, ( Xpeak/ zpeak )< tx
INWindowsof all event classes
Speed High-pass z-peak
xz-ratiospeed vs. z ratio
OUTPothole Detections
ALGORITHM - Filtering
speed vs. z ratio At high speeds, small anomalies cause high peak accelerations Rejects windows where Zpeak < ts X speed
or, (Zpeak /speed ) < ts
INWindowsof all event classes
Speed High-pass z-peak
xz-ratiospeed vs. z ratio
OUTPothole Detections
ALGORITHM – Sample Traces
ALGORITHM - Training
Tuning parameters t={tz,tx,ts} are computed using exhaustive search over a set of values
For each set t, we compute detector scores(t) = corr – incorr2
Corr is no. of pothole detections when sample was labeled as “pothole”
Maximize s(t) Include loosely labeled data
s(t) = corr – incorr2labeled – max(0,incorrloose – countr)
ALGORITHM - Clustering
Improve accuracy Cluster of at least k events must happen in the
same location with small margin of error(Δd) Clustering algorithm
Place each detection in Δd X Δd grid. Compute pairwise distances in same or neighboring grid
cells Iteratively merge pairs of distances in order of distance Max intra cluster distance < Δt Reported location is the centroid of the locations within it
ALGORITHM – Blacklisting &
False Negatives Well-known anomalies like bridges, railroad crossings, speed bumps etc can be located from GIS sources and blacklisted
GPS errors Pothole avoidance Biased detection will focus on critical
anomalies
PERFORMANCE EVALUATION Goals
Minimize false negative rate for smooth roads Never a flag a smooth road as anomaly
Missing a few potholes is acceptable Evaluation
1. Classification accuracy on hand-labeled data2. Performance improvement using loosely labeled
data3. Performance on loosely labeled roads4. Spot-checks
Performance on Labeled Data Randomly divided into training set and test set
False positive rate is 7.6% Not accurate
PERFORMANCE EVALUATION
Class Hand Labeled w/ Loosely Labeled
Pothole 88.9% 92.4%
Manhole 0.3% 0.0%
Expansion joints 2.7% 0.3%
Railroad Crossing 8.1% 7.3%
PERFORMANCE EVALUATION Estimating the false-positive rate
Ran the detector on loosely labeled roads
Helps set upper bound on false positive rate (at most 0.15%) on good roads.
Road # potholes # windows # detections rate
Storrow Dr. few 1865 3 0.16%
Memorial Dr. few 1781 2 0.12%
Hwy I-93 few 2877 5 0.17%
Binney St. some 6887 25 0.63%
Beacham St. many 1643 231 14%
PERFORMANCE EVALUATION Impact of features and thresholds
1. Only Z peak 2. w. xz-ratio filter 3. w. speed vs. z ratio
tx=1.5 tx=2.5ts=5
PERFORMANCE EVALUATION Performance under uncontrolled conditions
Slamming doors Fiddling with the sensor equipment Driving behaviors Deliberately avoiding potholes
Use clustering k=4
PERFORMANCE EVALUATION Spot Checks
Typical pothole Manhole Expansion joint
RELATED WORK
Surveys Falling weight deflectometer Machine vision – cameras, robots Accelerometer Microsoft Trafficsense – smartphones
DISCUSSION This is what I think
Innovative Ground truth establishment is tedious, expensive in dense
road networks Will it work in hilly areas ,slopes? Future work?
Driver feedback – Interactive embedded computers Smartphones – Cheaper solution, greater coverage
Comments/Questions ???
REFERENCES
The Pothole Patrol: Using a Mobile Sensor Network forRoad Surface Monitoring Jakob Eriksson, Lewis Girod, Bret Hull, Ryan Newton, Samuel Madden, Hari Balakrishnan MIT Computer Science and Artificial Intelligence Laboratory
U. Lee, E. Magistretti, B. Zhou, M. Gerla, P. Bellavista, and A. Corradi. MobEyes: Smart Mobs for Urban Monitoring with a Vehicular Sensor Network. IEEE Wireless Communications, 2006.
TrafficSense: Rich Monitoring of Road and Traffic Conditions using Mobile Smartphones Prashanth Mohan, Venkata N. Padmanabhan, and Ramachandran Ramjee {prmohan,padmanab,ramjee}@microsoft.com Microsoft Research India, Bangalore
http://research.microsoft.com/apps/pubs/default.aspx?id=70573