Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control

21 September 2019

Papers citing "Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control"

29 / 29 papers shown

Title
SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation Achref Doula M. Mühlhäuser Alejandro Sánchez Guinea 26 0 0 14 May 2025
Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes Jie Liu Pan Zhou Zehao Xiao Jiayi Shen Wenzhe Yin J. Sonke E. Gavves 34 0 0 03 May 2025
Model uncertainty quantification using feature confidence sets for outcome excursions Junting Ren Armin Schwartzman 64 0 0 28 Apr 2025
Support is All You Need for Certified VAE Training Changming Xu Debangshu Banerjee Deepak Vasisht Gagandeep Singh AAML 44 0 0 16 Apr 2025
Pretraining with random noise for uncertainty calibration Jeonghwan Cheon Se-Bum Paik OnRL 51 0 0 23 Dec 2024
Streamlining Prediction in Bayesian Deep Learning Rui Li Marcus Klasson Arno Solin Martin Trapp UQCV BDL 102 2 0 27 Nov 2024
Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification Ruben Grewal Paolo Tonella Andrea Stocco 50 12 0 29 Apr 2024
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving Shahin Atakishiyev Mohammad Salameh Randy Goebel 72 6 0 18 Mar 2024
Context-aware LLM-based Safe Control Against Latent Risks Quang Khanh Luu Xiyu Deng Anh Van Ho Yorie Nakahira 54 4 0 18 Mar 2024
Inferring the Langevin Equation with Uncertainty via Bayesian Neural Networks Youngkyoung Bae Seungwoong Ha Hawoong Jeong 73 2 0 02 Feb 2024
Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learning Valentina Zangirolami Matteo Borrotti 31 6 0 12 Oct 2023
When to Trust AI: Advances and Challenges for Certification of Neural Networks Marta Kwiatkowska Xiyue Zhang AAML 39 9 0 20 Sep 2023
Distributionally Robust Statistical Verification with Imprecise Neural Networks Souradeep Dutta Michele Caprio Vivian Lin Matthew Cleaveland Kuk Jin Jang I. Ruchkin O. Sokolsky Insup Lee OOD AAML 49 7 0 28 Aug 2023
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents Amirhossein Zolfagharian Manel Abdellatif Lionel C. Briand S. Ramesh 32 5 0 03 Aug 2023
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review Chengmin Zhou Chao Wang Haseeb Hassan H. Shah Bingding Huang Pasi Fränti 3DV 43 3 0 16 Jul 2023
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference Arnaud Descours Tom Huix Arnaud Guillin Manon Michel Eric Moulines Boris Nectoux BDL 32 1 0 10 Jul 2023
Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation Wenbo Hu Xin Sun Qiang liu Wenbo Hu Shu Wu 47 0 0 23 Mar 2023
Safer Autonomous Driving in a Stochastic, Partially-Observable Environment by Hierarchical Contingency Planning Ugo Lecerf Christelle Yemdji Tchassi Pietro Michiardi 30 1 0 13 Apr 2022
Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems Andrea Stocco Brian Pulfer Paolo Tonella 27 69 0 21 Dec 2021
Statistical Perspectives on Reliability of Artificial Intelligence Systems Yili Hong J. Lian Li Xu Jie Min Yueyao Wang Laura J. Freeman Xinwei Deng 38 30 0 09 Nov 2021
Accurate and Reliable Forecasting using Stochastic Differential Equations Peng Cui Zhijie Deng Wenbo Hu Jun Zhu UQCV 38 1 0 28 Mar 2021
Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving Zhiyu Huang Jingda Wu Chen Lv 19 133 0 19 Mar 2021
Limits of Probabilistic Safety Guarantees when Considering Human Uncertainty Richard Cheng R. Murray J. W. Burdick 39 6 0 05 Mar 2021
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications Leonard Berrada Sumanth Dathathri Krishnamurthy Dvijotham Robert Stanforth Rudy Bunel J. Uesato Sven Gowal M. P. Kumar AAML OOD 30 17 0 18 Feb 2021
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing Andreas Bär Jonas Löhdefink Nikhil Kapoor Serin Varghese Fabian Hüger Peter Schlicht Tim Fingscheidt AAML 111 33 0 11 Jan 2021
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving Di Feng Ali Harakeh Steven Waslander Klaus C. J. Dietmayer AAML UQCV EDL 24 224 0 20 Nov 2020
Probabilistic Safety for Bayesian Neural Networks Matthew Wicker Luca Laurenti A. Patané Marta Z. Kwiatkowska AAML 14 52 0 21 Apr 2020
Robustness of Bayesian Neural Networks to Gradient-Based Attacks Ginevra Carbone Matthew Wicker Luca Laurenti A. Patané Luca Bortolussi G. Sanguinetti AAML 38 77 0 11 Feb 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,167 0 06 Jun 2015