CrowdAtlas: Self-Updating Maps for Cloud and Personal Use Mike Lin.
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Transcript of CrowdAtlas: Self-Updating Maps for Cloud and Personal Use Mike Lin.
![Page 1: CrowdAtlas: Self-Updating Maps for Cloud and Personal Use Mike Lin.](https://reader036.fdocuments.us/reader036/viewer/2022081520/56649e155503460f94aff509/html5/thumbnails/1.jpg)
CrowdAtlas: Self-Updating Mapsfor Cloud and Personal Use
Mike Lin
![Page 2: CrowdAtlas: Self-Updating Maps for Cloud and Personal Use Mike Lin.](https://reader036.fdocuments.us/reader036/viewer/2022081520/56649e155503460f94aff509/html5/thumbnails/2.jpg)
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.
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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
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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.
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Introduction
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Introduction
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Introduction
Contributioni. An automatic map update systemii. Map inference with navigationiii. Contributing 61 km of roads for the beijing map on
OSM.
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CrowdAtlas Service
8 days of data from 70 taxis in Beijing, with a sampling interval of 10 seconds.
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CrowdAtlas Service
Extracting unmatched segments (red) after map matching seconds.
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CrowdAtlas Service
With one week of data and a threshold of four sub-traces,there are three clusters in the area
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CrowdAtlas Service
With one week of data and a threshold of four sub-traces,there are three clusters in the area with aerial image
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MAP MATCHING
1. Within the error radius2. Candidate sets ex: {x00, x10} 3. Likely drive path with observing sequence
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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.
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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
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New Road Inference
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New Road Inference
i. Road attributes: Give directions of roads
ii. Standalone mode: User-selected type of roads(drive,cycling,walking)
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Challenges and Limitations
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IMPLEMENTATION
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PERFORMANCE
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PERFORMANCE
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PERFORMANCE
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CONTRIBUTION
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CENTERLINE OFFSET
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COMMENT
DATA COLLECTION
RELIABLITY CHECK
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