2018
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Qiu, H; Chen, J; Jain, S; Jiang, Y; McCartney, M; Kar, G; Bai, F; Grimm, D K; Gruteser, M; Govindan, R Towards Robust Vehicular Context Sensing Journal Article IEEE Transactions on Vehicular Technology, 67 (3), pp. 1909-1922, 2018, ISSN: 0018-9545. Links | BibTeX @article{Qiu18b,
title = {Towards Robust Vehicular Context Sensing},
author = {H Qiu and J Chen and S Jain and Y Jiang and M McCartney and G Kar and F Bai and D K Grimm and M Gruteser and R Govindan},
doi = {10.1109/TVT.2017.2771623},
issn = {0018-9545},
year = {2018},
date = {2018-03-01},
journal = {IEEE Transactions on Vehicular Technology},
volume = {67},
number = {3},
pages = {1909-1922},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Qiu, Hang; Ahmad, Fawad; Bai, Fan; Gruteser, Marco; Govindan, Ramesh AVR: Augmented Vehicular Reality Inproceedings Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services (Mobisys), pp. 81–95, ACM, Munich, Germany, 2018, ISBN: 978-1-4503-5720-3, (Best Paper Runner-up). Abstract | Links | BibTeX @inproceedings{Qiu18d,
title = {AVR: Augmented Vehicular Reality},
author = {Hang Qiu and Fawad Ahmad and Fan Bai and Marco Gruteser and Ramesh Govindan},
url = {http://doi.acm.org/10.1145/3210240.3210319},
doi = {10.1145/3210240.3210319},
isbn = {978-1-4503-5720-3},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services (Mobisys)},
pages = {81--95},
publisher = {ACM},
address = {Munich, Germany},
series = {MobiSys '18},
abstract = {Autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These 3D sensors are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to wirelessly share visual information with other nearby vehicles. We show that AVR is feasible using off-the-shelf wireless technologies, and it can qualitatively change the decisions made by autonomous vehicle path planning algorithms. Our AVR prototype achieves positioning accuracies that are within a few percentages of car lengths and lane widths, and it is optimized to process frames at 30fps.},
note = {Best Paper Runner-up},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These 3D sensors are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to wirelessly share visual information with other nearby vehicles. We show that AVR is feasible using off-the-shelf wireless technologies, and it can qualitatively change the decisions made by autonomous vehicle path planning algorithms. Our AVR prototype achieves positioning accuracies that are within a few percentages of car lengths and lane widths, and it is optimized to process frames at 30fps. |
2017
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Qiu, Hang; Ahmad, Fawad; Govindan, Ramesh; Gruteser, Marco; Bai, Gorkem Kar Fan Augmented Vehicular Reality: Enabling Extended Vision for Future Vehicles Inproceedings the 18th Workshop on Mobile Computing Systems and Applications (HotMobile 2017), Sonoma, CA, 2017. Abstract | Links | BibTeX @inproceedings{Qiu17a,
title = {Augmented Vehicular Reality: Enabling Extended Vision for Future Vehicles},
author = {Hang Qiu and Fawad Ahmad and Ramesh Govindan and Marco Gruteser and Gorkem Kar Fan Bai},
url = {http://doi.acm.org/10.1145/3032970.3032976},
year = {2017},
date = {2017-02-01},
booktitle = {the 18th Workshop on Mobile Computing Systems and Applications (HotMobile 2017)},
address = {Sonoma, CA},
abstract = {Like today's autonomous vehicle prototypes, vehicles in the future will have rich sensors to map and identify objects in the environment. For example, many autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These cameras are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, not just for autonomous vehicles but for other Advanced Driving Assistance Systems (ADAS), we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to share visual information with other nearby vehicles, but requires careful techniques to align coordinate frames of reference, and to detect dynamic objects. Preliminary evaluations hint at the feasibility of AVR and also highlight research challenges in achieving AVR's potential to improve autonomous vehicles and ADAS.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Like today's autonomous vehicle prototypes, vehicles in the future will have rich sensors to map and identify objects in the environment. For example, many autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These cameras are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, not just for autonomous vehicles but for other Advanced Driving Assistance Systems (ADAS), we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to share visual information with other nearby vehicles, but requires careful techniques to align coordinate frames of reference, and to detect dynamic objects. Preliminary evaluations hint at the feasibility of AVR and also highlight research challenges in achieving AVR's potential to improve autonomous vehicles and ADAS. |
Kar, Gorkem; Jain, Shubham; Gruteser, Marco; Bai, Fan; Govindan, Ramesh Real-time Traffic Estimation at Vehicular Edge Nodes Inproceedings Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 3:1–3:13, ACM, San Jose, California, 2017, ISBN: 978-1-4503-5087-7. Links | BibTeX @inproceedings{Kar17a,
title = {Real-time Traffic Estimation at Vehicular Edge Nodes},
author = {Gorkem Kar and Shubham Jain and Marco Gruteser and Fan Bai and Ramesh Govindan},
url = {http://doi.acm.org/10.1145/3132211.3134461},
doi = {10.1145/3132211.3134461},
isbn = {978-1-4503-5087-7},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the Second ACM/IEEE Symposium on Edge Computing},
pages = {3:1--3:13},
publisher = {ACM},
address = {San Jose, California},
series = {SEC '17},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Kar, Gorkem; Jain, Shubham; Gruteser, Marco; Chen, Jinzhu; Bai, Fan; Govindan, Ramesh PredriveID: Pre-trip Driver Identification from In-vehicle Data Inproceedings Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 2:1–2:12, ACM, San Jose, California, 2017, ISBN: 978-1-4503-5087-7. Links | BibTeX @inproceedings{Kar17b,
title = {PredriveID: Pre-trip Driver Identification from In-vehicle Data},
author = {Gorkem Kar and Shubham Jain and Marco Gruteser and Jinzhu Chen and Fan Bai and Ramesh Govindan},
url = {http://doi.acm.org/10.1145/3132211.3134462},
doi = {10.1145/3132211.3134462},
isbn = {978-1-4503-5087-7},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the Second ACM/IEEE Symposium on Edge Computing},
pages = {2:1--2:12},
publisher = {ACM},
address = {San Jose, California},
series = {SEC '17},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2015
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Jiang, Yurong; Qiu, Hang; McCartney, Matthew; Sukhatme, Gaurav; Gruteser, Marco; Bai, Fan; Grimm, Donald; Govindan, Ramesh CARLOC: Precisely Tracking Automobile Position Inproceedings Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (SenSys), Seoul, Korea, 2015. BibTeX @inproceedings{Jiang15a,
title = {CARLOC: Precisely Tracking Automobile Position},
author = {Yurong Jiang and Hang Qiu and Matthew McCartney and Gaurav Sukhatme and Marco Gruteser and Fan Bai and Donald Grimm and Ramesh Govindan},
year = {2015},
date = {2015-11-01},
booktitle = {Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (SenSys)},
address = {Seoul, Korea},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2014
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Jiang, Yurong; Qiu, Hang; McCartney, Matthew; Halfond, William G J; Bai, Fan; Grimm, Donald; Govindan, Ramesh CarLog: A Platform for Flexible and Efficient Automotive Sensing Inproceedings Proceedings of the 12th ACM Conference on Embedded Networked Sensor Systems (SenSys'14), Memphis, TN, USA, 2014. BibTeX @inproceedings{Jiang14a,
title = {CarLog: A Platform for Flexible and Efficient Automotive Sensing},
author = {Yurong Jiang and Hang Qiu and Matthew McCartney and William G J Halfond and Fan Bai and Donald Grimm and Ramesh Govindan},
year = {2014},
date = {2014-11-01},
booktitle = {Proceedings of the 12th ACM Conference on Embedded Networked Sensor Systems (SenSys'14)},
address = {Memphis, TN, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
