8 citations · 12 across the 6 of their papers we have counts for
7 papers
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…
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…
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…
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:…
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…