Vehicular sensing | Ramesh Govindan

Cars have several onboard sensors, and we have, over the years, explored innovative uses of these to improve vehicular safety and comfort.

TVT

CATS: A Framework for Cooperative Autonomy Trust & Security

Namo Asavisanu, Tina Khezresmaeilzadeh, Rohan Sequeira, and 4 more authors

IEEE Transactions on Vehicular Technology, 2025

Bib

@article{asavisanu.n.khezresmaeilzadeh.t.ea:cats,
  author = {Asavisanu, Namo and Khezresmaeilzadeh, Tina and Sequeira, Rohan and Qiu, Hang and Ahmad, Fawad and Psounis, Konstantinos and Govindan, Ramesh},
  journal = {IEEE Transactions on Vehicular Technology},
  title = {CATS: A Framework for Cooperative Autonomy Trust &
  		  Security},
  year = {2025},
  volume = {},
  number = {},
  pages = {1-16},
  topics = {vehicular},
  keywords = {Vehicle-to-everything;Tides;Privacy;Phantoms;Electronic
  		  mail;Denial-of-service attack;Delays;Training;Time
  		  factors;Scalability},
  doi = {10.1109/TVT.2025.3546194}
}

IoTDI

Cooperative Infrastructure Perception

Fawad Ahmad, Christina Suyong Shin, Weiwu Pang, and 3 more authors

In 2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI), 2024

Bib

@inproceedings{ahmad.f.shin.cs.ea:cooperative,
  author = {Ahmad, Fawad and Shin, Christina Suyong and Pang, Weiwu and Leong, Branden and Ghosh, Pradipta and Govindan, Ramesh},
  title = {Cooperative Infrastructure Perception},
  booktitle = {2024 IEEE/ACM Ninth International Conference on
  		  Internet-of-Things Design and Implementation (IoTDI)},
  year = {2024},
  pages = {61-72},
  keywords = {Visualization;Accuracy;Three-dimensional displays;Mixed
  		  reality;Line-of-sight
  		  propagation;Throughput;Sensors;Cooperative
  		  Perception;Infrastructure-assisted Perception;Autonomous
  		  Vehicle Systems},
  topics = {vehicular},
  doi = {10.1109/IoTDI61053.2024.00010}
}

MobiCom
RECAP: 3D Traffic Reconstruction

Christina Shin, Weiwu Pang, Chuan Li, and 4 more authors

In Proceedings of the 30th Annual International Conference on Mobile Computing and Networking (MobiCom 24), 2024

Abs Bib

On-vehicle 3D sensing technologies, such as LiDARs and stereo cameras, enable a novel capability, 3D traffic reconstruction. This produces a volumetric video consisting of a sequence of 3D frames capturing the time evolution of road traffic. 3D traffic reconstruction can help trained investigators reconstruct the scene of an accident. In this paper, we describe the design and implementation of RECAP, a system that continuously and opportunistically produces 3D traffic reconstructions from multiple vehicles. RECAP builds upon prior work on point cloud registration, but adapts it to settings with minimal point cloud overlap (both in the spatial and temporal sense) and develops techniques to minimize error and computation time in multi-way registration. On-road experiments and trace-driven simulations show that RECAP can, within minutes, generate highly accurate reconstructions that have 2× or more lower errors than competing approaches.
@inproceedings{shin.c.pang.w.ea:recap, author = {Shin, Christina and Pang, Weiwu and Li, Chuan and Bai, Fan and Ahmad, Fawad and Paek, Jeongyeup and Govindan, Ramesh}, title = {RECAP: 3D Traffic Reconstruction}, booktitle = {Proceedings of the 30th Annual International Conference on Mobile Computing and Networking (MobiCom 24)}, year = {2024}, topics = {vehicular} }

TVT

NORA: Towards Large-Scale Vehicular Analytics for Driving Environment Monitoring/Assessment

Donald K. Grimm, Fan Bai, Jinzhu Chen, and 3 more authors

IEEE Open Journal of Vehicular Technology, 2023

Bib

@article{grimm.dk.bai.f.ea:nora,
  author = {Grimm, Donald K. and Bai, Fan and Chen, Jinzhu and Yu, Bo and Saraydar, Cem and Govindan, Ramesh},
  journal = {IEEE Open Journal of Vehicular Technology},
  title = {NORA: Towards Large-Scale Vehicular Analytics for Driving
  		  Environment Monitoring/Assessment},
  year = {2023},
  volume = {4},
  number = {},
  pages = {618-632},
  doi = {10.1109/OJVT.2023.3296021},
  topics = {vehicular}
}

MobiSys
AutoCast: Scalable Infrastructure-Less Cooperative Perception for Distributed Collaborative Driving

Hang Qiu, Po-Han Huang, Namo Asavisanu, and 3 more authors

In Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services, 2022

Abs Bib Link

Autonomous vehicles use 3D sensors for perception. Cooperative perception enables vehicles to share sensor readings with each other to improve safety. Prior work in cooperative perception scales poorly even with infrastructure support. AUTOCAST1 enables scalable infrastructure-less cooperative perception using direct vehicle-to-vehicle communication. It carefully determines which objects to share based on positional relationships between traffic participants, and the time evolution of their trajectories. It coordinates vehicles and optimally schedules transmissions in a distributed fashion. Extensive evaluation results under different scenarios show that, unlike competing approaches, AUTOCAST can avoid crashes and near-misses which occur frequently without cooperative perception, its performance scales gracefully in dense traffic scenarios providing 2-4x visibility into safety critical objects compared to existing cooperative perception schemes, its transmission schedules can be completed on the real radio testbed, and its scheduling algorithm is near-optimal with negligible computation overhead.
@inproceedings{qiu.h.huang.p.ea:autocast, author = {Qiu, Hang and Huang, Po-Han and Asavisanu, Namo and Liu, Xiaochen and Psounis, Konstantinos and Govindan, Ramesh}, title = {AutoCast: Scalable Infrastructure-Less Cooperative Perception for Distributed Collaborative Driving}, year = {2022}, isbn = {9781450391856}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3498361.3538925}, doi = {10.1145/3498361.3538925}, booktitle = {Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services}, pages = {128–141}, numpages = {14}, keywords = {cooperative perception, V2V communication, autonomous cars}, location = {Portland, Oregon}, topics = {vehicular}, series = {MobiSys '22} }

NSDI

CarMap-Fast 3D Feature Map Updates for Automobiles

Fawad Ahmad, Hang Qiu, Ray Eells, and 2 more authors

In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20), 2020

Bib Link Code Slides

@inproceedings{ahmad.f.qiu.h.ea:carmap-fast,
  title = {CarMap-Fast 3D Feature Map Updates for Automobiles},
  author = {Ahmad, Fawad and Qiu, Hang and Eells, Ray and Bai, Fan and Govindan, Ramesh},
  video = {https://www.youtube.com/watch?v=SlG4QGq5ypk},
  year = {2020},
  date = {2020-01-01},
  booktitle = {17th USENIX Symposium on Networked Systems Design and
  		  Implementation (NSDI 20)},
  publisher = {USENIX Association},
  address = {Santa Clara, CA},
  keywords = {},
  pubstate = {published},
  topics = {vehicular},
  tppubtype = {inproceedings}
}

Fusion

QuickSketch: Building 3D Representations in Unknown Environments Using Crowdsourcing

Fawad Ahmad, Hang Qiu, Xiaochen Liu, and 2 more authors

In 2018 21st International Conference on Information Fusion (FUSION), 2018

Bib Link

@inproceedings{ahmad.f.qiu.h.ea:quicksketch,
  title = {QuickSketch: Building 3D Representations in Unknown
  		  Environments Using Crowdsourcing},
  author = {Ahmad, Fawad and Qiu, Hang and Liu, Xiaochen and Bai, Fan and Govindan, Ramesh},
  doi = {10.23919/ICIF.2018.8455437},
  year = {2018},
  date = {2018-07-01},
  booktitle = {2018 21st International Conference on Information Fusion
  		  (FUSION)},
  pages = {2314-2321},
  keywords = {Three-dimensional
  		  displays;Visualization;Cameras;Simultaneous localization
  		  and mapping;Feature extraction},
  pubstate = {published},
  topics = {vehicular},
  tppubtype = {inproceedings}
}

TVT

Towards Robust Vehicular Context Sensing

H Qiu, J Chen, S Jain, and 7 more authors

IEEE Transactions on Vehicular Technology, 2018

Bib

@article{qiu.h.chen.j.ea:towards,
  title = {Towards Robust Vehicular Context Sensing},
  author = {Qiu, H and Chen, J and Jain, S and Jiang, Y and McCartney, M and Kar, G and Bai, F and Grimm, D K and Gruteser, M and Govindan, R},
  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 = {vehicular sensing},
  topics = {vehicular},
  pubstate = {published},
  tppubtype = {article}
}

IoTDI

Kestrel: Video Analytics for Augmented Multi-Camera Vehicle Tracking

H Qiu, X Liu, S Rallapalli, and 5 more authors

In 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI), 2018

Bib

@inproceedings{qiu.h.liu.x.ea:kestrel,
  title = {Kestrel: Video Analytics for Augmented Multi-Camera
  		  Vehicle Tracking},
  author = {Qiu, H and Liu, X and Rallapalli, S and Bency, A J and Chan, K and Urgaonkar, R and Manjunath, B S and Govindan, R},
  doi = {10.1109/IoTDI.2018.00015},
  year = {2018},
  date = {2018-04-01},
  booktitle = {2018 IEEE/ACM Third International Conference on
  		  Internet-of-Things Design and Implementation (IoTDI)},
  pages = {48-59},
  pubstate = {published},
  tppubtype = {inproceedings},
  topics = {vehicular,sensing},
}

MobiSys

AVR: Augmented Vehicular Reality

Hang Qiu, Fawad Ahmad, Fan Bai, and 2 more authors

In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services (Mobisys), 2018

Bib Link

@inproceedings{qiu.h.ahmad.f.ea:avr,
  title = {AVR: Augmented Vehicular Reality},
  author = {Qiu, Hang and Ahmad, Fawad and Bai, Fan and Gruteser, Marco and Govindan, Ramesh},
  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},
  topics = {vehicular},
  keywords = {Autonomous Cars, Collaborative Sensing, Extended Vision,
  		  vehicular sensing},
  pubstate = {published},
  tppubtype = {inproceedings}
}

SEC

Pre-DriveID: Pre-trip Driver Identification from In-vehicle Data

Gorkem Kar, Shubham Jain, Marco Gruteser, and 3 more authors

In Proceedings of the Second ACM/IEEE Symposium on Edge Computing, San Jose / Silicon Valley, SEC 2017, CA, USA, October 12-14, 2017, 2017

Bib

@inproceedings{kar.g.jain.s.ea:pre-driveid--pre-trip-driver-identification-from-in-vehicle-data,
  author = {Kar, Gorkem and Jain, Shubham and Gruteser, Marco and Chen, Jinzhu and Bai, Fan and Govindan, Ramesh},
  title = {{Pre-DriveID: Pre-trip Driver Identification from
  		  In-vehicle Data}},
  booktitle = {{Proceedings of the Second {ACM/IEEE} Symposium on Edge
  		  Computing, San Jose / Silicon Valley, {SEC} 2017, CA, USA,
  		  October 12-14, 2017}},
  pages = {2:1--2:12},
  year = {2017},
  topics = {vehicular},
  doi = {10.1145/3132211.3134462}
}

SEC

Real-time Traffic Estimation at Vehicular Edge Nodes

Gorkem Kar, Shubham Jain, Marco Gruteser, and 2 more authors

In Proceedings of the Second ACM/IEEE Symposium on Edge Computing, San Jose / Silicon Valley, SEC 2017, CA, USA, October 12-14, 2017, 2017

Bib

@inproceedings{kar.g.jain.s.ea:real-time-traffic-estimation-at-vehicular-edge-nodes,
  author = {Kar, Gorkem and Jain, Shubham and Gruteser, Marco and Bai, Fan and Govindan, Ramesh},
  title = {{Real-time Traffic Estimation at Vehicular Edge Nodes}},
  booktitle = {{Proceedings of the Second {ACM/IEEE} Symposium on Edge
  		  Computing, San Jose / Silicon Valley, {SEC} 2017, CA, USA,
  		  October 12-14, 2017}},
  pages = {3:1--3:13},
  topics = {vehicular},
  year = {2017}
}

Augmented Vehicular Reality: Enabling Extended Vision for Future Vehicles

Hang Qiu, Fawad Ahmad, Ramesh Govindan, and 3 more authors

In Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications, HotMobile 2017, Sonoma, CA, USA, February 21 - 22, 2017, 2017

Bib

@inproceedings{qiu.h.ahmad.f.ea:augmented,
  author = {Qiu, Hang and Ahmad, Fawad and Govindan, Ramesh and Gruteser, Marco and Bai, Fan and Kar, Gorkem},
  title = {Augmented Vehicular Reality: Enabling Extended Vision for
  		  Future Vehicles},
  booktitle = {Proceedings of the 18th International Workshop on Mobile
  		  Computing Systems and Applications, HotMobile 2017, Sonoma,
  		  CA, USA, February 21 - 22, 2017},
  pages = {67--72},
  topics = {vehicular},
  year = {2017}
}

TVT

Towards Robust Vehicular Context Sensing

Hang Qiu, Jinzhu Chen, Shubham Jain, and 7 more authors

IEEE Transactions on Vehicular Technology, 2017

Bib

@article{qiu.h.chen.j.ea:towards-robust-vehicular-context-sensing,
  author = {Qiu, Hang and Chen, Jinzhu and Jain, Shubham and Jiang, Yurong and McCartney, Matt and Kar, Gorkem and Bai, Fan and Grimm, Donald K. and Gruteser, Marco and Govindan, Ramesh},
  title = {{Towards Robust Vehicular Context Sensing}},
  journal = {IEEE Transactions on Vehicular Technology},
  topics = {vehicular},
  year = {2017}
}

SenSys

CARLOC: Precisely Tracking Automobile Position

Yurong Jiang, Hang Qiu, Matthew McCartney, and 5 more authors

In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (SenSys) , no 2015

Bib

@inproceedings{jiang.y.qiu.h.ea:carloc--precisely-tracking-automobile-position,
  author = {Jiang, Yurong and Qiu, Hang and McCartney, Matthew and Sukhatme, Gaurav and Gruteser, Marco and Bai, Fan and Grimm, Donald and Govindan, Ramesh},
  title = { {CARLOC: Precisely Tracking Automobile Position} },
  booktitle = { Proceedings of the 13th ACM Conference on Embedded
  		  Networked Sensor Systems (SenSys) },
  month =  no,
  topics = {vehicular},
  location = { Seoul, Korea },
  year = {2015}
}