publications | Sepehr Samavi

For an up-to-date list of publications, please visit my Google Scholar page.

2023

SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimization

Sepehr Samavi, Florian Shkurti , and Angela P. Schoellig

arXiv preprint arXiv:2310.10982, 2023

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Robots need to predict and react to human motions to navigate through a crowd without collisions. Many existing methods decouple prediction from planning, which does not account for the interaction between robot and human motions and can lead to the robot getting stuck. We propose SICNav, a Model Predictive Control (MPC) method that jointly solves for robot motion and predicted crowd motion in closed-loop. We model each human in the crowd to be following an Optimal Reciprocal Collision Avoidance (ORCA) scheme and embed that model as a constraint in the robot’s local planner, resulting in a bilevel nonlinear MPC optimization problem. We use a KKT-reformulation to cast the bilevel problem as a single level and use a nonlinear solver to optimize. Our MPC method can influence pedestrian motion while explicitly satisfying safety constraints in a single-robot multi-human environment. We analyze the performance of SICNav in a simulation environment to demonstrate safe robot motion that can influence the surrounding humans. We also validate the trajectory forecasting performance of ORCA on a human trajectory dataset.
@article{samavi2023sicnav, author = {Samavi, Sepehr and Shkurti, Florian and Schoellig, Angela P.}, journal = {arXiv preprint arXiv:2310.10982}, title = {SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimization}, year = {2023}, url = {https://arxiv.org/abs/2310.10982}, }
Does Unpredictability Influence Driving Behavior?

Sepehr Samavi, Florian Shkurti , and Angela P. Schoellig

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2023

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In this paper we investigate the effect of the unpredictability of surrounding cars on an ego-car performing a driving maneuver. We use Maximum Entropy Inverse Reinforcement Learning to model reward functions for an ego-car conducting a lane change in a highway setting. We define a new feature based on the unpredictability of surrounding cars and use it in the reward function. We learn two reward functions from human data: a baseline and one that incorporates our defined unpredictability feature, then compare their performance with a quantitative and qualitative evaluation. Our evaluation demonstrates that incorporating the unpredictability feature leads to a better fit of human-generated test data. These results encourage further investigation of the effect of unpredictability on driving behavior.
@inproceedings{Samavi2023, author = {Samavi, Sepehr and Shkurti, Florian and Schoellig, Angela P.}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, keywords = {autonomous vehicle,autonomy system,computer vision,perception,self driving car}, title = {{Does Unpredictability Influence Driving Behavior?}}, year = {2023}, url = {https://arxiv.org/abs/2307.15287}, }

2020

Zeus: A system description of the two-time winner of the collegiate SAE AutoDrive competition

Keenan Burnett , Jingxing Qian , Xintong Du , and 14 more authors

Journal of Field Robotics, 2020

@article{Burnett2020,
  archiveprefix = {arXiv},
  arxivid = {2004.08752},
  author = {Burnett, Keenan and Qian, Jingxing and Du, Xintong and Liu, Linqiao and Yoon, David J. and Shen, Tianchang and Sun, Susan and Samavi, Sepehr and Sorocky, Michael J. and Bianchi, Mollie and Zhang, Kaicheng and Arkhangorodsky, Arkady and Sykora, Quinlan and Lu, Shichen and Huang, Yizhou and Schoellig, Angela P. and Barfoot, Timothy D.},
  url = {https://doi.org/10.1002/rob.21958},
  doi = {10.1002/rob.21958},
  eprint = {2004.08752},
  issn = {15564967},
  journal = {Journal of Field Robotics},
  keywords = {autonomous vehicle,autonomy system,computer vision,perception,self driving car},
  volume = {1},
  number = {28},
  title = {{Zeus: A system description of the two-time winner of the collegiate SAE AutoDrive competition}},
  year = {2020}
}

2019

Building a Winning Self-Driving Car in Six Months

Keenan Burnett , Andreas Schimpe , Sepehr Samavi, and 5 more authors

In Proc. of the IEEE International Conference on Robotics and Automation (ICRA) , May 2019

Bib PDF

@inproceedings{Burnett2018,
  archiveprefix = {arXiv},
  arxivid = {1811.01273},
  author = {Burnett, Keenan and Schimpe, Andreas and Samavi, Sepehr and Gridseth, Mona and Liu, Chengzhi Winston and Li, Qiyang and Kroeze, Zachary and Schoellig, Angela P.},
  booktitle = {Proc. of the IEEE International Conference on Robotics and Automation (ICRA)},
  doi = {10.1109/ICRA.2019.8794029},
  eprint = {1811.01273},
  isbn = {978-1-5386-6027-0},
  month = may,
  pages = {9583--9589},
  title = {{Building a Winning Self-Driving Car in Six Months}},
  url = {https://doi.org/10.1109/ICRA.2019.8794029},
  access = {true},
  year = {2019}
}

aUToTrack : A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge

Keenan Burnett , Sepehr Samavi, Steven L. Waslander , and 2 more authors

In Proc. of the International Conference on Computer and Robot Vision (CRV) , May 2019

Best poster presentation award

Bib PDF

@inproceedings{Burnett2019,
  author = {Burnett, Keenan and Samavi, Sepehr and Waslander, Steven L. and Barfoot, Timothy D. and Schoellig, Angela P.},
  booktitle = {Proc. of the International Conference on Computer and Robot Vision (CRV)},
  keywords = {-vision for autonomous vehicles,ception,object recognition and detection,real-time per-},
  title = {{aUToTrack : A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge}},
  month = may,
  url = {https://doi.org/10.1109/CRV.2019.00036},
  dataset = {https://www.autodrive.utoronto.ca/uoftped50},
  year = {2019},
  note = {\emph{Best poster presentation award}}
}