Fetching the paper…
Reading the bibliography…
As shown by recent studies, machine intelligence-enabled systems are vulnerable to test cases resulting from either adversarial manipulation or natural distribution shifts.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Pre-crash scenario typology for crash avoidance research
Wassim G Najm, John D Smith, Mikio Yanagisawa, et al · 2007
Earlier work this paper cites.
Particle swarm optimization
Riccardo Poli, James Kennedy, and Tim Blackwell · 2007
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: no regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger · 2010
Earlier work this paper cites.
Adversarial machine learning
Ling Huang, Anthony D Joseph, Blaine Nelson, Benjamin IP Rubinstein, and JD Tygar · 2011
Earlier work this paper cites.
Introduction to rare event simulation
James Bucklew · 2013
Earlier work this paper cites.
Docker: lightweight linux containers for consistent development and deployment
Dirk Merkel · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Earlier work this paper cites.
Accelerated Evaluation of Automated Vehicles
Ding Zhao · 2016
Earlier work this paper cites.
Accelerated evaluation of automated vehicles safety in lane-change scenarios based on importance sampling techniques
Ding Zhao, Henry Lam, Huei Peng, Shan Bao, David J LeBlanc, Kazutoshi Nobukawa, and Christopher S Pan · 2016
Earlier work this paper cites.
Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
Earlier work this paper cites.
Automated driving systems 2.0: A vision for safety
National Highway Traffic Safety Administration · 2017
Earlier work this paper cites.
Sequential experimentation to efficiently test automated vehicles
Zhiyuan Huang, Henry Lam, and Ding Zhao · 2017
Earlier work this paper cites.
Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
Earlier work this paper cites.
Simmobility short-term: An integrated microscopic mobility simulator
Carlos Lima Azevedo, Neeraj Milind Deshmukh, Balakumar Marimuthu, Simon Oh, Katarzyna Marczuk, Harold Soh, Kakali Basak, Tomer Toledo, Li-Shiuan Peh, and Moshe E Ben-Akiva · 2017
Earlier work this paper cites.
The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?
Philip Polack, Florent Altché, Brigitte d’Andréa Novel, and Arnaud de La Fortelle · 2017
Earlier work this paper cites.
Deep reinforcement learning framework for autonomous driving
Ahmad EL Sallab, Mohammed Abdou, Etienne Perot, and Senthil Yogamani · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles R Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Earlier work this paper cites.
Openpilot
Comma.ai · 2017
Earlier work this paper cites.
A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis · 2018
Earlier work this paper cites.
Scalable end-to-end autonomous vehicle testing via rare-event simulation
Matthew O’Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, and John C Duchi · 2018
Earlier work this paper cites.
Accelerated evaluation of automated vehicles in car-following maneuvers
Ding Zhao, Xianan Huang, Huei Peng, Henry Lam, and David J LeBlanc · 2018
Earlier work this paper cites.
A versatile approach to evaluating and testing automated vehicles based on kernel methods
Zhiyuan Huang, Yaohui Guo, Mansur Arief, Henry Lam, and Ding Zhao · 2018
Earlier work this paper cites.
Rare-event simulation without structural information: a learning-based approach
Zhiyuan Huang, Henry Lam, and Ding Zhao · 2018
Earlier work this paper cites.
Synthesis of different autonomous vehicles test approaches
Zhiyuan Huang, Mansur Arief, Henry Lam, and Ding Zhao · 2018
Earlier work this paper cites.
An environment for autonomous driving decision-making
Edouard Leurent · 2018
Earlier work this paper cites.
https://sumo.dlr.de/docs/Netedit/index.html , 2018
SUMO NETEDIT · 2018
Earlier work this paper cites.
https://deepdrive.io/ , 2018
Deepdrive Simulation · 2018
Cited alongside, same era.
https://github.com/esmini/esmini , 2018
Environment Simulator Minimalistic (esmini) · 2018
Cited alongside, same era.
Autonovi-sim: Autonomous vehicle simulation platform with weather, sensing, and traffic control
Andrew Best, Sahil Narang, Lucas Pasqualin, Daniel Barber, and Dinesh Manocha · 2018
Cited alongside, same era.
A new multi-vehicle trajectory generator to simulate vehicle-to-vehicle encounters
Wenhao Ding, Wenshuo Wang, and Ding Zhao · 2018
Cited alongside, same era.
Ontology based scene creation for the development of automated vehicles
Gerrit Bagschik, Till Menzel, and Markus Maurer · 2018
Cited alongside, same era.
Testing autonomous cars for feature interaction failures using many-objective search
Smarts: Scalable multi-agent reinforcement learning training school for autonomous driving
Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, et al · 2020
Later among the works it cites.
Data-driven test scenario generation for cooperative maneuver planning on highways
Christian Knies and Frank Diermeyer · 2020
Later among the works it cites.
Cmts: A conditional multiple trajectory synthesizer for generating safety-critical driving scenarios
Wenhao Ding, Mengdi Xu, and Ding Zhao · 2020
Later among the works it cites.
Learning to collide: An adaptive safety-critical scenarios generating method
Wenhao Ding, Baiming Chen, Minjun Xu, and Ding Zhao · 2020
Later among the works it cites.
Driving etiquette
Huei Peng · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Raja Ben Abdessalem, Annibale Panichella, Shiva Nejati, Lionel C Briand, and Thomas Stifter · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
Cited alongside, same era.
Second: Sparsely embedded convolutional detection
Yan Yan, Yuxing Mao, and Bo Li · 2018
Cited alongside, same era.
A framework for automated driving system testable cases and scenarios
Eric Thorn, Shawn C Kimmel, Michelle Chaka, Booz Allen Hamilton, et al · 2018
Cited alongside, same era.
http://apollo.auto/platform/simulation.html , 2018
Apollo Simulation · 2018
Cited alongside, same era.
Solving the rubik’s cube with deep reinforcement learning and search
Forest Agostinelli, Stephen McAleer, Alexander Shmakov, and Pierre Baldi · 2019
Cited alongside, same era.
Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, and Ding Zhao · 2020
Later among the works it cites.
Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation
Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka · 2020
Later among the works it cites.
Polarnet: An improved grid representation for online lidar point clouds semantic segmentation
Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh · 2020
Later among the works it cites.
Cylinder3d: An effective 3d framework for driving-scene lidar semantic segmentation
Hui Zhou, Xinge Zhu, Xiao Song, Yuexin Ma, Zhe Wang, Hongsheng Li, and Dahua Lin · 2020
Later among the works it cites.
Clocs: Camera-lidar object candidates fusion for 3d object detection
Su Pang, Daniel Morris, and Hayder Radha · 2020
Later among the works it cites.
Advsim: Generating safety-critical scenarios for self-driving vehicles
Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, and Raquel Urtasun · 2021
Later among the works it cites.
DI-drive: OpenDILab decision intelligence platform for autonomous driving simulation
DI drive Contributors · 2021
Later among the works it cites.
Commonroad-rl: a configurable reinforcement learning environment for motion planning of autonomous vehicles
Xiao Wang, Hanna Krasowski, and Matthias Althoff · 2021
Later among the works it cites.
Causalcity: Complex simulations with agency for causal discovery and reasoning
Daniel McDuff, Yale Song, Jiyoung Lee, Vibhav Vineet, Sai Vemprala, Nicholas Gyde, Hadi Salman, Shuang Ma, Kwanghoon Sohn, and Ashish Kapoor · 2021
Later among the works it cites.
Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning
Quanyi Li, Zhenghao Peng, Zhenghai Xue, Qihang Zhang, and Bolei Zhou · 2021
Later among the works it cites.
Learn-to-race: A multimodal control environment for autonomous racing
James Herman, Jonathan Francis, Siddha Ganju, Bingqing Chen, Anirudh Koul, Abhinav Gupta, Alexey Skabelkin, Ivan Zhukov, Max Kumskoy, and Eric Nyberg · 2021
Later among the works it cites.
Autodrive simulator: A simulator for scaled autonomous vehicle research and education
Tanmay Vilas Samak, Chinmay Vilas Samak, and Ming Xie · 2021
Later among the works it cites.
Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain, 2021
John M Scanlon, Kristofer D Kusano, Tom Daniel, Christopher Alderson, Alexander Ogle, and Trent Victor · 2021
Later among the works it cites.
Multimodal safety-critical scenarios generation for decision-making algorithms evaluation
Wenhao Ding, Baiming Chen, Bo Li, Kim Ji Eun, and Ding Zhao · 2021
Later among the works it cites.
Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment
Shuo Feng, Xintao Yan, Haowei Sun, Yiheng Feng, and Henry X Liu · 2021
Later among the works it cites.
Semantically controllable scene generation with guidance of explicit knowledge
Wenhao Ding, Bo Li, Kim Ji Eun, and Ding Zhao · 2021
Later among the works it cites.
Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning
Jianyu Chen, Shengbo Eben Li, and Masayoshi Tomizuka · 2021
Later among the works it cites.
Deep reinforcement learning for autonomous driving: A survey
B Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A Al Sallab, Senthil Yogamani, and Patrick Pérez · 2021
Later among the works it cites.
Tss: Transformation-specific smoothing for robustness certification
Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, and Bo Li · 2021
Later among the works it cites.
https://www.dmv.ca.gov/portal/vehicle-industry-services/autonomous-vehicles/disengagement-reports/ , 2022
California Department of Motor Vehicle Disengagement Report · 2022
Closest in time.
On adversarial robustness of trajectory prediction for autonomous vehicles
Qingzhao Zhang, Shengtuo Hu, Jiachen Sun, Qi Alfred Chen, and Z Morley Mao · 2022
Closest in time.
Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning
Quanyi Li, Zhenghao Peng, Lan Feng, Qihang Zhang, Zhenghai Xue, and Bolei Zhou · 2022
Closest in time.
On the robustness of safe reinforcement learning under observational perturbations
Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Jie Tan, Bo Li, and Ding Zhao · 2022
Closest in time.
Vista 2.0: An open, data-driven simulator for multimodal sensing and policy learning for autonomous vehicles
Alexander Amini, Tsun-Hsuan Wang, Igor Gilitschenski, Wilko Schwarting, Zhijian Liu, Song Han, Sertac Karaman, and Daniela Rus · 2022
Closest in time.