174 citations · 356 across the 27 of their papers we have counts for
27 papers
Quantifying the Individual Differences of Driver' Risk Perception with Just Four Interpretable Parameters
Chen Chen, Zhiqian Lan, Guojian Zhan +3
There will be a long time when automated vehicles are mixed with human-driven vehicles. Understanding how drivers assess driving risks and modelling their individual differences ar…
Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification
Yang Guan, Liye Tang, Chuanxiao Li +5
Self-evolution is indispensable to realize full autonomous driving. This paper presents a self-evolving decision-making system based on the Integrated Decision and Control (IDC), a…
Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning
Yangang Ren, Guojian Zhan, Liye Tang +3
Intersections are quite challenging among various driving scenes wherein the interaction of signal lights and distinct traffic actors poses great difficulty to learn a wise and rob…
Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees
Wenhan Cao, Jingliang Duan, Shengbo Eben Li +3
This paper proposes a primal-dual framework to learn a stable estimator for linear constrained estimation problems leveraging the moving horizon approach. To avoid the online compu…
Encoding Distributional Soft Actor-Critic for Autonomous Driving in Multi-lane Scenarios
Jingliang Duan, Yangang Ren, Fawang Zhang +5
In this paper, we propose a new reinforcement learning (RL) algorithm, called encoding distributional soft actor-critic (E-DSAC), for decision-making in autonomous driving. Unlike…
Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian
Baiyu Peng, Jingliang Duan, Jianyu Chen +6
Safety is essential for reinforcement learning (RL) applied in the real world. Adding chance constraints (or probabilistic constraints) is a suitable way to enhance RL safety under…
Approximate Optimal Filter for Linear Gaussian Time-invariant Systems
Kaiming Tang, Shengbo Eben Li, Yuming Yin +4
State estimation is critical to control systems, especially when the states cannot be directly measured. This paper presents an approximate optimal filter, which enables to use pol…
Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm
Yiting Kong, Yang Guan, Jingliang Duan +3
Merging into the highway from the on-ramp is an essential scenario for automated driving. The decision-making under the scenario needs to balance the safety and efficiency performa…
Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function
Haitong Ma, Jianyu Chen, Shengbo Eben Li +4
Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous region when implementing reinforcement learning (RL) on real-world tasks,…
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence
Yang Guan, Yangang Ren, Qi Sun +5
Decision and control are core functionalities of high-level automated vehicles. Current mainstream methods, such as functionality decomposition and end-to-end reinforcement learnin…
Feasibility Enhancement of Constrained Receding Horizon Control Using Generalized Control Barrier Function
Haitong Ma, Xiangteng Zhang, Shengbo Eben Li +3
Receding horizon control (RHC) is a popular procedure to deal with optimal control problems. Due to the existence of state constraints, optimization-based RHC often suffers the not…
Recurrent Model Predictive Control
Zhengyu Liu, Jingliang Duan, Wenxuan Wang +5
This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems. Unlike traditional…
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning
Baiyu Peng, Yao Mu, Jingliang Duan +3
Safety is essential for reinforcement learning (RL) applied in real-world tasks like autonomous driving. Chance constraints which guarantee the satisfaction of state constraints at…
Steadily Learn to Drive with Virtual Memory
Yuhang Zhang, Yao Mu, Yujie Yang +4
Reinforcement learning has shown great potential in developing high-level autonomous driving. However, for high-dimensional tasks, current RL methods suffer from low data efficienc…
Model-Based Actor-Critic with Chance Constraint for Stochastic System
Baiyu Peng, Yao Mu, Yang Guan +3
Safety is essential for reinforcement learning (RL) applied in real-world situations. Chance constraints are suitable to represent the safety requirements in stochastic systems. Pr…
Numerically Stable Dynamic Bicycle Model for Discrete-time Control
Qiang Ge, Shengbo Eben Li, Qi Sun +1
Dynamic/kinematic model is of great significance in decision and control of intelligent vehicles. However, due to the singularity of dynamic models at low speed, kinematic models h…
Centralized Coordination of Connected Vehicles at Intersections using Graphical Mixed Integer Optimization
Qiang Ge, Qi Sun, Zhen Wang +3
This paper proposes a centralized multi-vehicle coordination scheme serving unsignalized intersections. The whole process consists of three stages: a) target velocity optimization:…
Reinforcement Solver for H-infinity Filter with Bounded Noise
Jie Li, Shengbo Eben Li, Kaiming Tang +2
H-infinity filter has been widely applied in engineering field, but copping with bounded noise is still an open problem and difficult to solve. This paper considers the H-infinity…
Ternary Policy Iteration Algorithm for Nonlinear Robust Control
Jie Li, Shengbo Eben Li, Yang Guan +3
The uncertainties in plant dynamics remain a challenge for nonlinear control problems. This paper develops a ternary policy iteration (TPI) algorithm for solving nonlinear robust c…
Continuous-time finite-horizon ADP for automated vehicle controller design with high efficiency
Ziyu Lin, Jingliang Duan, Shengbo Eben Li +2
The design of an automated vehicle controller can be generally formulated into an optimal control problem. This paper proposes a continuous-time finite-horizon approximate dynamicp…
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization
Lu Wen, Jingliang Duan, Shengbo Eben Li +2
Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to its potential to solve complex classification and control problems. However, existing RL…
Mixed Reinforcement Learning with Additive Stochastic Uncertainty
Yao Mu, Shengbo Eben Li, Chang Liu +4
Reinforcement learning (RL) methods often rely on massive exploration data to search optimal policies, and suffer from poor sampling efficiency. This paper presents a mixed reinfor…
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic
Yangang Ren, Jingliang Duan, Shengbo Eben Li +2
Reinforcement learning (RL) has achieved remarkable performance in numerous sequential decision making and control tasks. However, a common problem is that learned nearly optimal p…
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
Jianyu Chen, Shengbo Eben Li, Masayoshi Tomizuka
Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the perception, decision and control problems in an integrated way, which can be more adapting to…