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Scenic: A Language for Scenario Specification and Data Generation

Scenic: A Language for Scenario Specification and Data Generation

13 October 2020
Daniel J. Fremont
Edward J. Kim
T. Dreossi
Shromona Ghosh
Xiangyu Yue
Alberto L. Sangiovanni-Vincentelli
Sanjit A. Seshia
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Papers citing "Scenic: A Language for Scenario Specification and Data Generation"

13 / 13 papers shown
Title
Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model
Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model
Bo-Kai Ruan
Hao-Tang Tsui
Yung-Hui Li
Hong-Han Shuai
LM&Ro
112
8
0
20 Feb 2025
LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation
LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation
Bowen Li
Zhaoyu Li
Qiwei Du
Jinqi Luo
Wenshan Wang
...
Katia Sycara
Pradeep Kumar Ravikumar
Alexander G. Gray
X. Si
Sebastian A. Scherer
AI4CE
LRM
102
3
0
01 Nov 2024
Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving
Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving
Axel Brunnbauer
Luigi Berducci
P. Priller
D. Ničković
Radu Grosu
72
1
0
26 Mar 2024
LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving
LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving
Guodong Rong
B. Shin
Hadi Tabatabaee
Q. Lu
Steve Lemke
...
Eric Sterner
Keunhae Ushiroda
Michael Reyes
Dmitry Zelenkovsky
Seonman Kim
39
394
0
07 May 2020
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to
  the Real World
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World
Daniel J. Fremont
Edward Kim
Yash Vardhan Pant
Sanjit A. Seshia
Atul Acharya
Xantha Bruso
Paul Wells
Steve Lemke
Q. Lu
Shalin Mehta
83
127
0
17 Mar 2020
Counterexample-Guided Data Augmentation
Counterexample-Guided Data Augmentation
T. Dreossi
Shromona Ghosh
Xiangyu Yue
Kurt Keutzer
Alberto L. Sangiovanni-Vincentelli
Sanjit A. Seshia
AAML
29
61
0
17 May 2018
Domain Randomization for Transferring Deep Neural Networks from
  Simulation to the Real World
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
Joshua Tobin
Rachel Fong
Alex Ray
Jonas Schneider
Wojciech Zaremba
Pieter Abbeel
171
2,948
0
20 Mar 2017
Compositional Falsification of Cyber-Physical Systems with Machine
  Learning Components
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
T. Dreossi
Alexandre Donzé
Sanjit A. Seshia
AAML
72
230
0
02 Mar 2017
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural
  Networks for Real-Time Object Detection for Autonomous Driving
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
Bichen Wu
Alvin Wan
F. Iandola
Peter H. Jin
Kurt Keutzer
74
513
0
04 Dec 2016
Driving in the Matrix: Can Virtual Worlds Replace Human-Generated
  Annotations for Real World Tasks?
Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?
Matthew Johnson-Roberson
Charlie Barto
Rounak Mehta
S. N. Sridhar
Karl Rosaen
Ram Vasudevan
74
616
0
06 Oct 2016
A New Approach to Probabilistic Programming Inference
A New Approach to Probabilistic Programming Inference
Frank Wood
Jan-Willem van de Meent
Vikash K. Mansinghka
47
345
0
03 Jul 2015
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
185
14,831
1
21 Dec 2013
A survey of cross-validation procedures for model selection
A survey of cross-validation procedures for model selection
Sylvain Arlot
Alain Celisse
145
3,583
0
27 Jul 2009
1