18 citations · 50 across the 11 of their papers we have counts for
6 papers · 1 filter
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model
Tianyu Shi, Yifei Ai, Omar ElSamadisy +1
In the coming years and decades, autonomous vehicles (AVs) will become increasingly prevalent, offering new opportunities for safer and more convenient travel and potentially smart…
Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement
Tianyu Shi, Dong Chen, Kaian Chen +1
Reinforcement learning (RL) is a powerful data-driven control method that has been largely explored in autonomous driving tasks. However, conventional RL approaches learn control p…
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning
Tianyu Shi, Pin Wang, Xuxin Cheng +2
We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver. Furthermore, we design two similar Deep Q learning fram…
Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization
Chenyang Xi, Tianyu Shi, Yuankai Wu +1
Intelligent motion planning is one of the core components in automated vehicles, which has received extensive interests. Traditional motion planning methods suffer from several dra…
A Data Driven Method of Optimizing Feedforward Compensator for Autonomous Vehicle
Tianyu Shi, Pin Wang, Ching-Yao Chan +1
A reliable controller is critical and essential for the execution of safe and smooth maneuvers of an autonomous vehicle.The controller must be robust to external disturbances, such…
Research on Control Method and Evaluation System of Ground Unmanned Vehicle Formation Transform
Tianyu Shi, Zhichao Wang, Chong hao Zou +5
In this paper,we design a formation control systrm for multi-unmanned ground vehicles(UGV) from the prospective of path planning and path tracking.The master-slave control is adopt…