Search Results for author: Ruochen Jiao

Found 10 papers, 2 papers with code

Empowering Autonomous Driving with Large Language Models: A Safety Perspective

no code implementations28 Nov 2023 YiXuan Wang, Ruochen Jiao, Chengtian Lang, Sinong Simon Zhan, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu

Autonomous Driving (AD) faces crucial hurdles for commercial launch, notably in the form of diminished public trust and safety concerns from long-tail unforeseen driving scenarios.

Autonomous Driving Common Sense Reasoning

State-Wise Safe Reinforcement Learning With Pixel Observations

1 code implementation3 Nov 2023 Simon Sinong Zhan, YiXuan Wang, Qingyuan Wu, Ruochen Jiao, Chao Huang, Qi Zhu

In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the challenges of balancing the tradeoff between maximizing rewards and minimizing safety violations, particularly in complex environments with contact-rich or non-smooth dynamics, and when dealing with high-dimensional pixel observations.

reinforcement-learning Reinforcement Learning (RL) +2

Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling

no code implementations17 Sep 2023 Ruochen Jiao, YiXuan Wang, Xiangguo Liu, Chao Huang, Qi Zhu

Trajectory generation and trajectory prediction are two critical tasks for autonomous vehicles, which generate various trajectories during development and predict the trajectories of surrounding vehicles during operation, respectively.

Autonomous Vehicles Trajectory Prediction

Learning Representation for Anomaly Detection of Vehicle Trajectories

no code implementations9 Mar 2023 Ruochen Jiao, Juyang Bai, Xiangguo Liu, Takami Sato, Xiaowei Yuan, Qi Alfred Chen, Qi Zhu

We conduct extensive experiments to demonstrate that our supervised method based on contrastive learning and unsupervised method based on reconstruction with semantic latent space can significantly improve the performance of anomalous trajectory detection in their corresponding settings over various baseline methods.

Anomaly Detection Autonomous Driving +3

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 (RL) 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

In addition, experiments show that our method can significantly improve the system's robust generalization to unseen patterns of attacks.

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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