CARLOC: Precisely Tracking Automobile Position Yurong Jiang, Hang Qiu, Matthew McCartney, Gaurav S....
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Transcript of CARLOC: Precisely Tracking Automobile Position Yurong Jiang, Hang Qiu, Matthew McCartney, Gaurav S....
CARLOC: Precisely Tracking Automobile Position
Yurong Jiang, Hang Qiu, Matthew McCartney, Gaurav S. Sukhatme, Marco Gruteser, Fan Bai, Donald Grimm, Ramesh Govindan
1
2Importance of GPS for navigation
Motivation Problem Design Evaluation Conclusion
253 million passenger vehicles
on U.S. roads [rita.dot.gov]
77% of US vehicles traveling use GPS for navigation [LandAirSea.com]
3Impact of GPS Errors
Motivation Problem Design Evaluation Conclusion
GPS errors can sometimes have serious consequences
4GPS Reading in Downtown
Motivation Problem Design Evaluation Conclusion
Smartphone (Google
API)
Urban Area Shaded Area
Opensky Area
Avg Error (m)
24.3 15.3 4.7
Error Std (m)
5.5 3.2 1.6
High-precision GPS - ublox
NEO-7P
5Goal
Motivation Problem Design Evaluation Conclusion
Can we achieve lane-level accuracy?
3 ~ 4 m
6To achieve lane-width accuracy
Onboard Sensors
RoadwayLandmark
sMotivation Problem Design Evaluation Conclusion
Map
Crowd-Sourcing
GPS Errors
Deadreckoning
Map Matching
How to incorporate different techniques?
How to detect and use landmarks?
Process of calculating current position from previous position based on speed and course (heading)
7CARLOC Contributions
A common probabilistic position representation to incorporate different error reduction techniques
Improved accuracy of dead reckoning and map matching using car sensors
Enhanced position estimates by crowdsourcing positions of stop signs, speed bumps and right turns
4m max error in highly obstructed environments, improving GPS-only strategies by 10x
Motivation Problem Design Evaluation Conclusion
8
Key Insight: Use Car Sensors to Improve Position Accuracy
Motivation Problem Design Evaluation Conclusion
Automobiles come with hundreds of sensors
9
l
l
l
Key Insight: Use Car Sensors to Improve Position Accuracy
Speed
Motivation Problem Design Evaluation Conclusion
Steering Wheel Angle
Brake
Yaw Rate
Throttle Position
Lateral Acceleration
Engine Speed
Rough Road Magnitude
Vertical Acceleration
Gear Shift Driver Behavior
Car dynamic
s
Road Surface
10
l
CARLOC Overview
Crowd-SourcedLandmarks
Map-MatchingGPS UpdateDead-Reckoning
Motivation Problem Design Evaluation Conclusion
11CARLOC Challenges
Representing position uncertainty due to sensor errors
Detecting roadway landmarks using car sensors and refining position estimates using crowdsourcing
Modeling uncertainty for GPS reading
Accurately modeling vehicle motion using car sensors
Improving the accuracy of map matching algorithms using car sensors
Motivation Problem Design Evaluation Conclusion
12Representing Position Uncertainty
use a Probabilistic Representation
) , , )
Motivation Problem Design Evaluation Conclusion
)
Particle Filter
0.1
0.20.30.2
0.10.1
13CARLOC Challenges
Representing position uncertainty due to sensor errors
Detecting roadway landmarks using car sensors and refining position estimates using crowdsourcing
Modeling uncertainty for GPS reading
Accurately modeling vehicle motion using car sensors
Improving the accuracy of map matching algorithms using car sensors
Motivation Problem Design Evaluation Conclusion
14Roadway Landmarks
Motivation Problem Design Evaluation Conclusion
Stop Sign Landmark
Street Corner Landmark
Speed Bump Landmark
15Role of Crowdsourcing
Motivation Problem Design Evaluation Conclusion
How Landmark Crowdsourcing works?
Particle Cloud
Resampling
Car’s possible position
16Speed Bump Detection
Motivation Problem Design Evaluation Conclusion
Speed Bump
Speedometer
17Speed Bump Detection
Motivation Problem Design Evaluation Conclusion
Acceleration Sensor
Rough Road Sensor
Front Wheel
Speedometer
~ Car Length
Rear Wheel Front WheelRear Wheel
18CARLOC Challenges
Representing position uncertainty due to sensor errors
Detecting roadway landmarks using car sensors and refining position estimates using crowdsourcing
Modeling uncertainty for GPS reading
Accurately modeling vehicle motion using car sensors
Improving the accuracy of map matching algorithms using car sensors
Motivation Problem Design Evaluation Conclusion
19How to describe the motion?
Motivation Problem Design Evaluation Conclusion
Motion Model
Motion model captures how the pose of car evolves with time
• Estimating displacement• Estimating change in heading
Key observation: Can estimate these parameters accurately using car sensors
Motion Model Parameter Estimation 20
Motivation Problem Design Evaluation Conclusion
𝑥𝑡=𝑥𝑡− 1+12(𝑣𝑡+𝑣𝑡− 1)δ 𝑡
Simplified displacement estimation
Heading change estimation
Odometer
Inertial Bearing?
Error in inertial sensors causes significant error in heading
Model Heading Change with Vehicle Kinematic Model
Heading Change Modeling21
Motivation Problem Design Evaluation Conclusion
Steering Wheel Angle
Ackermann Motion Model
Heading () = Bearing () + Slip ()
Yaw Rate
) )
22CARLOC Challenges
Representing position uncertainty due to sensor errors
Detecting roadway landmarks using car sensors and refining position estimates using crowdsourcing
Modeling uncertainty for GPS reading
Accurately modeling vehicle motion using car sensors
Improving the accuracy of map matching algorithms using car sensors
Motivation Problem Design Evaluation Conclusion
23Map Matching
Roa
d A
Roa
d C
Road B
Motivation Problem Design Evaluation Conclusion
P
Map matching is a technique that integrates positioning data with road network to identify correct
link on a digital map
24Hidden Markov Model (HMM) for Map Matching
Motivation Problem Design Evaluation Conclusion
1
0
Observation
Probability
A
BC
Travel Distance
How to distinguish?
How to obtain accurate Observation and Transition Probability?
Transition Probability
Steering Wheel Angle
Yaw Rate
Speed Odometer
Car sensors give more accurate transition probability estimates
25Map Matching Usage
Motivation Problem Design Evaluation Conclusion
)
A
B
𝑑𝑖
𝒘 ′ 𝒊=𝒘 𝒊∗𝟏
√𝟐 𝝅 𝞂𝟐𝒆−
𝒅 𝒊𝟐
𝟐𝞂𝟐
26CARLOC Evaluation
Methodology• Trace-driven comparison• Under 3 different circumstances – Obstructed,
Partially-Obstructed, Un-Obstructed -- from GPS view
Ground Truth
• Closed-loop routes for partially obstructed area• High-precision GPS Receiver for open sky area• Fiducials for obstructed area
Metrics
• Position error measured by distance between CARLOC position and ground truth
Motivation Problem Design Evaluation
Conclusion
27CARLOC Evaluation Experiments
CARLOC Performance on 3 different situations
• Obstructed Downtown Area• Partially Obstructed Area• Un-obstructed Open-sky Area
CARLOC Optimization Benefits
• Crowd-Sourcing• Map-Matching• Motion Model
Landmark Roles
• Landmark accuracy and detection accuracy• Crowd-sourcing degree impact on accuracy• Landmark number impact on accuracy
Motivation Problem Design Evaluation
Conclusion
28CARLOC and GPS Comparison in Downtown
CARLOCSmartphoneHigh-PrecisionRTK-GPSDifferential-GPS
Motivation Problem Design Evaluation
Conclusion
Map Pin Points Comparison 29
Motivation Problem Design Evaluati
onConclusi
on
Our Approach Brings 10x Improvement
We also achieve better accuracy than GPS strategies for partially-obstructed and un-obstructed routes, details can be found in paper ...
10
100
2.7 m
30Benefits of Optimization
Motivation Problem Design Evaluation
Conclusion
3.4 km 4.5 km 5.3 km 7.6 km 9.2 km1
10
W/O Motion Model
W/O Map-Matching
CARLOC
Baseline GPS Error
Route Length
Sta
rt-E
nd
Err
or
(m)
Each optimization has significant benefits
31Benefits of Crowd-Sourcing
Motivation Problem Design Evaluation
Conclusion
What degree of Crowd-Sourcing is necessary ?
Degree of Crowd-Sourcing
CARLOC accuracy can be improved by adding a higher degree of crowd-sourcing
32Role of Crowd-Sourcing
Motivation Problem Design Evaluation
Conclusion
How many landmarks are enough?
33CARLOC Contributions and Summary
A common probabilistic position representation to incorporate different error reduction techniques using car sensors
Enhanced position estimates by crowdsourcing positions of roadway landmarks
Extensive evaluations on roads with varying degree of satellite obstructions, improving GPS-only strategies by 10x
Motivation Problem Design Evaluation Conclusion
Thank you