Search Results for author: Ruochen Jiao

Found 5 papers, 0 papers with code

Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments

no code implementations29 Sep 2022 YiXuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin, Zhuoran Yang, Zhaoran Wang, Chao Huang, Qi Zhu

It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an unknown and stochastic environment under hard constraints that require the system state not to reach certain specified unsafe regions.

reinforcement-learning Safe Reinforcement Learning

A Tool for Neural Network Global Robustness Certification and Training

no code implementations15 Aug 2022 Zhilu Wang, YiXuan Wang, Feisi Fu, Ruochen Jiao, Chao Huang, Wenchao Li, Qi Zhu

Moreover, GROCET provides differentiable global robustness, which is leveraged in the training of globally robust neural networks.

Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction

no code implementations27 May 2022 Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu

Predicting the trajectories of surrounding objects is a critical task in self-driving and many other autonomous systems.

Adversarial Robustness Decision Making +1

TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor

no code implementations2 Mar 2022 Ruochen Jiao, Xiangguo Liu, Bowen Zheng, Dave Liang, Qi Zhu

Our model addresses trajectory generation and prediction in a unified architecture and benefits both tasks: the model can generate diverse, controllable and realistic trajectories to enhance planner optimization in safety-critical and long-tailed scenarios, and it can provide prediction of critical behavior in addition to the final trajectories for decision making.

Decision Making

End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering

no code implementations27 Feb 2021 Ruochen Jiao, Hengyi Liang, Takami Sato, Junjie Shen, Qi Alfred Chen, Qi Zhu

The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attacks and can achieve 55% to 90% improvement over the original OpenPilot.

Autonomous Driving

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