Shengbo Eben Li

verfied profile

Shengbo Eben Li received his M.S. and Ph.D. degrees from Tsinghua University in 2006 and 2009. Before joining Tsinghua University, he has worked at Stanford University, University of Michigan, and UC Berkeley. He is now leading Intelligent Driving Lab (iDLab) at Tsinghua University. His active research interests include intelligent vehicles and driver assistance, reinforcement learning and optimal control, distributed control and estimation, etc. He is the author of over 120 peer-reviewed journal/conference papers, and the co-inventor of over 30 patents. Dr. Li was the recipient of Best Paper Award in IEEE ITSC 2020, ICCAS 2020, IEEE ICUS 2020, CCCC 2018/2019, ITSAPF 2015, IEEE ITSC 2014 etc. He also received a few important awards like National Award for Technological Invention of China (2013), Excellent Young Scholar of NSF China (2016), Young Professorship of ChangJiang Scholar Program (2016), National Award for Progress in Science and Technology of China (2018), Distinguished Young Scholar of Beijing NSF (2018), Youth Sci & Tech Innovation Leader from Ministry of Science and Technology (2020), etc. He also serves as Board of Governor of IEEE ITS Society, AEs of IEEE ITSM, IEEE Trans ITS, etc.

Fixed-Dimensional and Permutation Invariant State Representation of Autonomous Driving

In this paper, we propose a new state representation method, called enco...
05/24/2021 ∙ by Jingliang Duan, et al. ∙ 0 ∙ share

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Integrated Decision and Control: Towards Interpretable and Efficient Driving Intelligence

Decision and control are two of the core functionalities of high-level a...
03/18/2021 ∙ by Yang Guan, et al. ∙ 3 ∙ share

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Approximate Optimal Filter for Linear Gaussian Time-invariant Systems

State estimation is critical to control systems, especially when the sta...
03/09/2021 ∙ by Kaiming Tang, et al. ∙ 0 ∙ share

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Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm

Merging into the highway from the on-ramp is an essential scenario for a...
03/08/2021 ∙ by Yiting Kong, et al. ∙ 0 ∙ share

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Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function

Model information can be used to predict future trajectories, so it has ...
03/02/2021 ∙ by Haitong Ma, et al. ∙ 0 ∙ share

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Recurrent Model Predictive Control

This paper proposes an off-line algorithm, called Recurrent Model Predic...
02/23/2021 ∙ by Zhengyu Liu, et al. ∙ 0 ∙ share

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Mixed Policy Gradient

Reinforcement learning (RL) has great potential in sequential decision-m...
02/23/2021 ∙ by Yang Guan, et al. ∙ 13 ∙ share

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Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning

Safety is essential for reinforcement learning (RL) applied in real-worl...
02/17/2021 ∙ by Baiyu Peng, et al. ∙ 0 ∙ share

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Steadily Learn to Drive with Virtual Memory

Reinforcement learning has shown great potential in developing high-leve...
02/16/2021 ∙ by Yuhang Zhang, et al. ∙ 0 ∙ share

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Model-Based Actor-Critic with Chance Constraint for Stochastic System

Safety constraints are essential for reinforcement learning (RL) applied...
12/19/2020 ∙ by Baiyu Peng, et al. ∙ 0 ∙ share

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Ternary Policy Iteration Algorithm for Nonlinear Robust Control

The uncertainties in plant dynamics remain a challenge for nonlinear con...
07/14/2020 ∙ by Jie Li, et al. ∙ 0 ∙ share

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Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization

Reinforcement learning (RL) is attracting increasing interests in autono...
03/03/2020 ∙ by Lu Wen, et al. ∙ 0 ∙ share

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Mixed Reinforcement Learning with Additive Stochastic Uncertainty

Reinforcement learning (RL) methods often rely on massive exploration da...
02/28/2020 ∙ by Yao Mu, et al. ∙ 30 ∙ share

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Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic

Reinforcement learning (RL) has achieved remarkable performance in a var...
02/13/2020 ∙ by Yangang Ren, et al. ∙ 0 ∙ share

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Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

Unlike popular modularized framework, end-to-end autonomous driving seek...
01/23/2020 ∙ by Jianyu Chen, et al. ∙ 38 ∙ share

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Addressing Value Estimation Errors in Reinforcement Learning with a State-Action Return Distribution Function

In current reinforcement learning (RL) methods, function approximation e...
01/09/2020 ∙ by Jingliang Duan, et al. ∙ 0 ∙ share

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Direct and indirect reinforcement learning

Reinforcement learning (RL) algorithms have been successfully applied to...
12/23/2019 ∙ by Yang Guan, et al. ∙ 0 ∙ share

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Centralized Conflict-free Cooperation for Connected and Automated Vehicles at Intersections by Proximal Policy Optimization

Connected vehicles will change the modes of future transportation manage...
12/18/2019 ∙ by Yang Guan, et al. ∙ 0 ∙ share

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Deep adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints

This paper presents a constrained deep adaptive dynamic programming (CDA...
11/26/2019 ∙ by Jingliang Duan, et al. ∙ 0 ∙ share

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Generalized Policy Iteration for Optimal Control in Continuous Time

This paper proposes the Deep Generalized Policy Iteration (DGPI) algorit...
09/11/2019 ∙ by Jingliang Duan, et al. ∙ 0 ∙ share

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Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks

As autonomous vehicles (AVs) need to interact with other road users, it ...
06/06/2019 ∙ by Long Xin, et al. ∙ 0 ∙ share

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Shengbo Eben Li

Featured Co-authors

Chang Liu

Jie Li

Masayoshi Tomizuka

Pin Wang

Qi Sun

Huei Peng

Ching-Yao Chan

Yang Guan

Jingliang Duan

Bo Cheng

Jianyu Chen

Fixed-Dimensional and Permutation Invariant State Representation of Autonomous Driving

Integrated Decision and Control: Towards Interpretable and Efficient Driving Intelligence

Approximate Optimal Filter for Linear Gaussian Time-invariant Systems

Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm

Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function

Recurrent Model Predictive Control

Mixed Policy Gradient

Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning

Steadily Learn to Drive with Virtual Memory

Model-Based Actor-Critic with Chance Constraint for Stochastic System

Ternary Policy Iteration Algorithm for Nonlinear Robust Control

Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization

Mixed Reinforcement Learning with Additive Stochastic Uncertainty

Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic

Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

Addressing Value Estimation Errors in Reinforcement Learning with a State-Action Return Distribution Function

Direct and indirect reinforcement learning

Centralized Conflict-free Cooperation for Connected and Automated Vehicles at Intersections by Proximal Policy Optimization

Deep adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints

Generalized Policy Iteration for Optimal Control in Continuous Time

Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks