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