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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