density_estimate.md

Density Estimation for SCSG

Here're some resources about Density Estimation for SCSG

Intros:

• This directory contains the density estimation methods for SCSG, including the classic Bayesian-Networks, Gaussian Process, and Deep Generative Models.

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Bayesian Networks

Critical factor graph situation clusters for accelerated automotive safety validation

paper link: here

citation:

@inproceedings{wheeler2019critical,
  title={Critical factor graph situation clusters for accelerated automotive safety validation},
  author={Wheeler, Tim Allan and Kochenderfer, Mykel J},
  booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)},
  pages={2133--2139},
  year={2019},
  organization={IEEE}
}

Modeling multi-vehicle interaction scenarios using gaussian random field

paper link: here

citation:

@inproceedings{guo2019modeling,
  title={Modeling multi-vehicle interaction scenarios using gaussian random field},
  author={Guo, Yaohui and Kalidindi, Vinay Varma and Arief, Mansur and Wang, Wenshuo and Zhu, Jiacheng and Peng, Huei and Zhao, Ding},
  booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)},
  pages={3974--3980},
  year={2019},
  organization={IEEE}
}

Real-time prediction of intermediate-horizon automotive collision risk

paper link: here

citation:

@article{wulfe2018real,
  title={Real-time prediction of intermediate-horizon automotive collision risk},
  author={Wulfe, Blake and Chintakindi, Sunil and Choi, Sou-Cheng T and Hartong-Redden, Rory and Kodali, Anuradha and Kochenderfer, Mykel J},
  journal={arXiv preprint arXiv:1802.01532},
  year={2018}
}

Factor graph scene distributions for automotive safety analysis

paper link: here

citation:

@inproceedings{wheeler2016factor,
  title={Factor graph scene distributions for automotive safety analysis},
  author={Wheeler, Tim A and Kochenderfer, Mykel J},
  booktitle={2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)},
  pages={1035--1040},
  year={2016},
  organization={IEEE}
}

Initial scene configurations for highway traffic propagation

paper link: here

citation:

@inproceedings{wheeler2015initial,
  title={Initial scene configurations for highway traffic propagation},
  author={Wheeler, Tim A and Kochenderfer, Mykel J and Robbel, Philipp},
  booktitle={2015 IEEE 18th International Conference on Intelligent Transportation Systems},
  pages={279--284},
  year={2015},
  organization={IEEE}
}

Deep Generative Models

Self-supervised real-to-sim scene generation

paper link: here

citation:

@inproceedings{prakash2021self,
  title={Self-supervised real-to-sim scene generation},
  author={Prakash, Aayush and Debnath, Shoubhik and Lafleche, Jean-Francois and Cameracci, Eric and Birchfield, Stan and Law, Marc T and others},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={16044--16054},
  year={2021}
}

Surfelgan: Synthesizing realistic sensor data for autonomous driving

paper link: here

citation:

@inproceedings{yang2020surfelgan,
  title={Surfelgan: Synthesizing realistic sensor data for autonomous driving},
  author={Yang, Zhenpei and Chai, Yuning and Anguelov, Dragomir and Zhou, Yin and Sun, Pei and Erhan, Dumitru and Rafferty, Sean and Kretzschmar, Henrik},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11118--11127},
  year={2020}
}

Cmts: A conditional multiple trajectory synthesizer for generating safety-critical driving scenarios

paper link: here

citation:

@inproceedings{ding2020cmts,
  title={Cmts: A conditional multiple trajectory synthesizer for generating safety-critical driving scenarios},
  author={Ding, Wenhao and Xu, Mengdi and Zhao, Ding},
  booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={4314--4321},
  year={2020},
  organization={IEEE}
}

A scenario generation pipeline for autonomous vehicle simulators

paper link: here

citation:

@article{wen2020scenario,
  title={A scenario generation pipeline for autonomous vehicle simulators},
  author={Wen, Mingyun and Park, Jisun and Cho, Kyungeun},
  journal={Human-centric Computing and Information Sciences},
  volume={10},
  number={1},
  pages={1--15},
  year={2020},
  publisher={Springer}
}

RELATE: Physically plausible multi-object scene synthesis using structured latent spaces

paper link: here

citation:

@article{ehrhardt2020relate,
  title={RELATE: Physically plausible multi-object scene synthesis using structured latent spaces},
  author={Ehrhardt, S{\'e}bastien and Groth, Oliver and Monszpart, Aron and Engelcke, Martin and Posner, Ingmar and Mitra, Niloy and Vedaldi, Andrea},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={11202--11213},
  year={2020}
}

Meta-sim2: Unsupervised learning of scene structure for synthetic data generation

paper link: here

citation:

@inproceedings{devaranjan2020meta,
  title={Meta-sim2: Unsupervised learning of scene structure for synthetic data generation},
  author={Devaranjan, Jeevan and Kar, Amlan and Fidler, Sanja},
  booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XVII 16},
  pages={715--733},
  year={2020},
  organization={Springer}
}

Meta-sim: Learning to generate synthetic datasets

paper link: here

citation:

@inproceedings{kar2019meta,
  title={Meta-sim: Learning to generate synthetic datasets},
  author={Kar, Amlan and Prakash, Aayush and Liu, Ming-Yu and Cameracci, Eric and Yuan, Justin and Rusiniak, Matt and Acuna, David and Torralba, Antonio and Fidler, Sanja},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4551--4560},
  year={2019}
}

A new multi-vehicle trajectory generator to simulate vehicle-to-vehicle encounters

paper link: here

citation:

@article{ding2018new,
  title={A new multi-vehicle trajectory generator to simulate vehicle-to-vehicle encounters},
  author={Ding, Wenhao and Wang, Wenshuo and Zhao, Ding},
  journal={arXiv preprint arXiv:1809.05680},
  year={2018}
}