CrowdAtlas: Self-Updating Mapsfor Cloud and Personal Use

Mike Lin

Authors

Yin Wang

I earned my B.S. and M.S. degrees at the Shanghai Jiao Tong University in 2000 and 2003, respectively, both in control theory. During 2003-2008, I worked with Stéphane Lafortune at the University of Michigan for my Ph.D degree in EECS. HP Labs was my first job after graduation. Since May 2013, I am affliated with Facebook.

Authors

I am a computer science researcher in the Data Mining and Machine Learning group at Hewlett-Packard Laboratories.   I work on techniques for automated classification, e.g. technology that learns to categorize documents into a topic hierarchy based on a small number of training examples given by humans, or to recognize computer systems that are likely to fail based on their past failures.  Repeatedly I find that applying such technologies to real-world business problems often leads to fixable robustness issues & opportunities for substantial performance improvement.  Hence, HP Labs is an excellent place for technology research as well as business impact.

George Forman

Introduction

i. Aggregates exceptional traces from usersii. Not conform to the open street mapiii. Automatically update the mapiv. Computer-generated roads

inaccurate maps:A British insurance survey found that car accidents caused by or related

to digital maps.

Introduction

Introduction

Introduction

Contributioni. An automatic map update systemii. Map inference with navigationiii. Contributing 61 km of roads for the beijing map on

OSM.

CrowdAtlas Service

8 days of data from 70 taxis in Beijing, with a sampling interval of 10 seconds.

CrowdAtlas Service

Extracting unmatched segments (red) after map matching seconds.

CrowdAtlas Service

With one week of data and a threshold of four sub-traces,there are three clusters in the area

CrowdAtlas Service

With one week of data and a threshold of four sub-traces,there are three clusters in the area with aerial image

MAP MATCHING

1. Within the error radius2. Candidate sets ex: {x00, x10} 3. Likely drive path with observing sequence

Extracting Unmatched Segments

Type I mismatch:Out of the error radiusWhen a sample’s error radius of 50m does not intersect any road.

Type II mismatch:

Accidental long trajectories will be eliminated The maximum travel speed to 180 km/h;therefore, any consecutive samples matched to locations beyond 50t meters from each other are considered a mismatch, where t is the sampling interval.

New Road Inference

i. Trace clustering by Hausdorff distance: The distance between two trajectories

ii. Centerline fitting: exceeds threshold

generates a polyline to minimize its mean square error to the samples.

iii. Connection: connect with intersections

iv. Iteration: Re-match and re-cluster

New Road Inference

New Road Inference

i. Road attributes: Give directions of roads

ii. Standalone mode: User-selected type of roads(drive,cycling,walking)

Challenges and Limitations

IMPLEMENTATION

PERFORMANCE

PERFORMANCE

PERFORMANCE

CONTRIBUTION

CENTERLINE OFFSET

COMMENT

DATA COLLECTION

RELIABLITY CHECK