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VeData: Promoting AI Assisted Autonomous Vehicles

Authors:

Xuemin (Sherman) ShenAuthors Info & Claims

MobiCom '18: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking

Pages 771 - 773

https://doi.org/10.1145/3241539.3267756

Published: 15 October 2018 Publication History

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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.

References

[1]

Nan Cheng, Feng Lyu, Jiayin Chen, Wenchao Xu, Haibo Zhou, Shan Zhang, and Xuemin Shen. 2018. Big Data Driven Vehicular Networks. IEEE Netw. (2018). http://arxiv.org/abs/1805.04687

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[2]

Susan Hassler. 2017. Self-driving cars and trucks are on the move. IEEE Spectrum, Vol. 54, 1 (January 2017), 6--6.

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[3]

Xinyu Huang, Xinjing Cheng, Qichuan Geng, Binbin Cao, Dingfu Zhou, Peng Wang, Yuanqing Lin, and Ruigang Yang. 2018. The ApolloScape Dataset for Autonomous Driving. CoRR (2018). http://arxiv.org/abs/1803.06184

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Wei Quan, Yana Liu, Hongke Zhang, and Shui Yu. 2017. Enhancing Crowd Collaborations for Software Defined Vehicular Networks. IEEE Commun. Magazine, Vol. 55, 8 (2017), 80--86.

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Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell. 2018. BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling. CoRR (2018). http://arxiv.org/abs/1805.04687

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Hongke Zhang, Wei Quan, H. Chao, and Chunming Qiao. 2016. Smart identifier network: A collaborative architecture for the future internet. IEEE Network, Vol. 30, 3 (2016), 46--51.

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[7]

Shan Zhang, Wei Quan, Junling Li, Weisen Shi, Peng Yang, and Xuemin (Sherman) Shen. 2018. Air-Ground Integrated Vehicular Network Slicing with Content Pushing and Caching. IEEE JSAC (2018). https://arxiv.org/abs/1806.03860

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Cited By

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Zhao LLiu YAl-Dubai AZomaya AMin GHawbani A(2021)A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular NetworksIEEE Internet of Things Journal10.1109/JIOT.2020.30211418:3(2066-2077)Online publication date: 1-Feb-2021
https://doi.org/10.1109/JIOT.2020.3021141
Liu XYu JLi FLv WWang YCheng X(2020)Data Aggregation in Wireless Sensor Networks: From the Perspective of SecurityIEEE Internet of Things Journal10.1109/JIOT.2019.29573967:7(6495-6513)Online publication date: Jul-2020
https://doi.org/10.1109/JIOT.2019.2957396
Taherifard NSimsek MKantarci B(2019)Bridging Connected Vehicles with Artificial Intelligence for Smart First Responder Services2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)10.1109/GlobalSIP45357.2019.8969516(1-5)Online publication date: Nov-2019
https://doi.org/10.1109/GlobalSIP45357.2019.8969516

Index Terms

VeData: Promoting AI Assisted Autonomous Vehicles
1. Computing methodologies
  1. Artificial intelligence
2. Networks
  1. Network performance evaluation
    1. Network experimentation

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Published In

MobiCom '18: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking

October 2018

884 pages

ISBN:9781450359030

DOI:10.1145/3241539

General Chairs:
Rajeev Shorey
TCS Innovation Labs, India
,
Rohan Murty
Soroco, USA/India
,
Program Chairs:
Yingying (Jennifer) Chen
WINLAB, Rutgers University, USA
,
Kyle Jamieson
Princeton University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 15 October 2018

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National Natural Science Foundation of China (NSFC)
National Key R&D Program

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MobiCom '18

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SIGMOBILE

MobiCom '18: The 24th Annual International Conference on Mobile Computing and Networking

October 29 - November 2, 2018

New Delhi, India

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MobiCom '18 Paper Acceptance Rate 42 of 187 submissions, 22%;

Overall Acceptance Rate 440 of 2,972 submissions, 15%

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Zhao LLiu YAl-Dubai AZomaya AMin GHawbani A(2021)A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular NetworksIEEE Internet of Things Journal10.1109/JIOT.2020.30211418:3(2066-2077)Online publication date: 1-Feb-2021
https://doi.org/10.1109/JIOT.2020.3021141
Liu XYu JLi FLv WWang YCheng X(2020)Data Aggregation in Wireless Sensor Networks: From the Perspective of SecurityIEEE Internet of Things Journal10.1109/JIOT.2019.29573967:7(6495-6513)Online publication date: Jul-2020
https://doi.org/10.1109/JIOT.2019.2957396
Taherifard NSimsek MKantarci B(2019)Bridging Connected Vehicles with Artificial Intelligence for Smart First Responder Services2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)10.1109/GlobalSIP45357.2019.8969516(1-5)Online publication date: Nov-2019
https://doi.org/10.1109/GlobalSIP45357.2019.8969516

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Passenger Condition Based Route-Planning for Cognitive Vehicle System

An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems

BigRoad: Scaling Road Data Acquisition for Dependable Self-Driving

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