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- W2896025246 abstract "Connected and autonomous vehicles (CAVs) are envisioned as a promising solution integrating the powerful AI and communication technologies to realize fully self-driving. However, there is few vehicular dataset open to study AI assisted self-driving. To make effectively use of AI technologies to optimize self-driving maneuver, we develop an open VeData platform to share the collected datasets. We also develop a Vehicular network Data harvester (VeData), which can collect various vehicular data at an arbitrary frequency. Based on this, we have incrementally collected diversified first-hand data in many different vehicular scenarios, including driving-in-campus, driving-around-campus, driving-in-downtown, and driving-on-highway. More datasets will be collected and shared to promote the research of AI assisted CAVs." @default.
- W2896025246 created "2018-10-26" @default.
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- W2896025246 date "2018-10-15" @default.
- W2896025246 modified "2023-09-27" @default.
- W2896025246 title "VeData" @default.
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- W2896025246 doi "https://doi.org/10.1145/3241539.3267756" @default.
- W2896025246 hasPublicationYear "2018" @default.
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