受强制性开放获取政策约束的文章 - Ding Zhao了解详情
无法在其他位置公开访问的文章：2 篇
Semantic traffic law adaptive decision-making for self-driving vehicles
J Liu, H Wang, Z Cao, W Yu, C Zhao, D Zhao, D Yang, J Li
IEEE Transactions on Intelligent Transportation Systems, 2023
强制性开放获取政策： 国家自然科学基金委员会
Coalitional fairness of autonomous vehicles at a t-intersection
D Gomez, H Lin, P Huang, C Harper, D Zhao
2022 IEEE 25th International Conference on Intelligent Transportation …, 2022
强制性开放获取政策： US Department of Transportation
可在其他位置公开访问的文章：41 篇
Accelerated evaluation of automated vehicles safety in lane-change scenarios based on importance sampling techniques
D Zhao, H Lam, H Peng, S Bao, DJ LeBlanc, K Nobukawa, CS Pan
IEEE transactions on intelligent transportation systems 18 (3), 595-607, 2016
强制性开放获取政策： US National Institutes of Health
Accelerated evaluation of automated vehicles in car-following maneuvers
D Zhao, X Huang, H Peng, H Lam, DJ LeBlanc
IEEE Transactions on Intelligent Transportation Systems 19 (3), 733-744, 2017
强制性开放获取政策： US National Science Foundation, US Department of Defense
A survey on safety-critical driving scenario generation—A methodological perspective
W Ding, C Xu, M Arief, H Lin, B Li, D Zhao
IEEE Transactions on Intelligent Transportation Systems 24 (7), 6971-6988, 2023
强制性开放获取政策： US National Science Foundation
Prompting decision transformer for few-shot policy generalization
M Xu, Y Shen, S Zhang, Y Lu, D Zhao, J Tenenbaum, C Gan
international conference on machine learning, 24631-24645, 2022
强制性开放获取政策： US National Science Foundation, US Department of Energy, US Department of …
Learning and inferring a driver's braking action in car-following scenarios
W Wang, J Xi, D Zhao
IEEE Transactions on Vehicular Technology 67 (5), 3887-3899, 2018
强制性开放获取政策： 国家自然科学基金委员会
Constrained variational policy optimization for safe reinforcement learning
Z Liu, Z Cen, V Isenbaev, W Liu, S Wu, B Li, D Zhao
International Conference on Machine Learning, 13644-13668, 2022
强制性开放获取政策： US National Science Foundation
Empirical study of DSRC performance based on safety pilot model deployment data
X Huang, D Zhao, H Peng
IEEE Transactions on Intelligent Transportation Systems 18 (10), 2619-2628, 2017
强制性开放获取政策： US Department of Energy
Constrained decision transformer for offline safe reinforcement learning
Z Liu, Z Guo, Y Yao, Z Cen, W Yu, T Zhang, D Zhao
International Conference on Machine Learning, 21611-21630, 2023
强制性开放获取政策： US National Science Foundation
Eco-trajectory planning with consideration of queue along congested corridor for hybrid electric vehicles
Z Yang, Y Feng, X Gong, D Zhao, J Sun
Transportation research record 2673 (9), 277-286, 2019
强制性开放获取政策： US Department of Energy
Safebench: A benchmarking platform for safety evaluation of autonomous vehicles
C Xu, W Ding, W Lyu, Z Liu, S Wang, Y He, H Hu, D Zhao, B Li
Advances in Neural Information Processing Systems 35, 25667-25682, 2022
强制性开放获取政策： US National Science Foundation
Generalizing goal-conditioned reinforcement learning with variational causal reasoning
W Ding, H Lin, B Li, D Zhao
Advances in Neural Information Processing Systems 35, 26532-26548, 2022
强制性开放获取政策： US National Science Foundation
Causalaf: Causal autoregressive flow for goal-directed safety-critical scenes generation
W Ding, H Lin, B Li, D Zhao
arXiv preprint arXiv:2110.13939 4 (4.4), 2021
强制性开放获取政策： US National Science Foundation
Safe model-based reinforcement learning with robust cross-entropy method
Z Liu, H Zhou, B Chen, S Zhong, M Hebert, D Zhao
arXiv preprint arXiv:2010.07968 3, 2020
强制性开放获取政策： US Department of Transportation
Deep probabilistic accelerated evaluation: A robust certifiable rare-event simulation methodology for black-box safety-critical systems
M Arief, Z Huang, GKS Kumar, Y Bai, S He, W Ding, H Lam, D Zhao
International Conference on Artificial Intelligence and Statistics, 595-603, 2021
强制性开放获取政策： US National Science Foundation
Curriculum reinforcement learning using optimal transport via gradual domain adaptation
P Huang, M Xu, J Zhu, L Shi, F Fang, D Zhao
Advances in Neural Information Processing Systems 35, 10656-10670, 2022
强制性开放获取政策： US National Science Foundation
Rare-event simulation for neural network and random forest predictors
Y Bai, Z Huang, H Lam, D Zhao
ACM Transactions on Modeling and Computer Simulation (TOMACS) 32 (3), 1-33, 2022
强制性开放获取政策： US National Science Foundation
Evaluation uncertainty in data-driven self-driving testing
Z Huang, M Arief, H Lam, D Zhao
2019 IEEE Intelligent Transportation Systems Conference (ITSC), 1902-1907, 2019
强制性开放获取政策： US National Science Foundation
Synthesis of different autonomous vehicles test approaches
Z Huang, M Arief, H Lam, D Zhao
2018 21st International Conference on Intelligent Transportation Systems …, 2018
强制性开放获取政策： US National Science Foundation
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