2 citations · 3 across the 7 of their papers we have counts for
7 papers
Curriculum Proximal Policy Optimization with Stage-Decaying Clipping for Self-Driving at Unsignalized Intersections
Zengqi Peng, Xiao Zhou, Yubin Wang +3
Unsignalized intersections are typically considered as one of the most representative and challenging scenarios for self-driving vehicles. To tackle autonomous driving problems in…
Learning the References of Online Model Predictive Control for Urban Self-Driving
Yubin Wang, Zengqi Peng, Yusen Xie +3
In this work, we propose a novel learning-based model predictive control (MPC) framework for motion planning and control of urban self-driving. In this framework, instantaneous ref…
Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning
Yubin Wang, Yulin Li, Zengqi Peng +2
Lane change in dense traffic typically requires the recognition of an appropriate opportunity for maneuvers, which remains a challenging problem in self-driving. In this work, we p…
Learning to Socially Navigate in Pedestrian-rich Environments with Interaction Capacity
Quecheng Qiu, Shunyi Yao, Jing Wang +3
Existing navigation policies for autonomous robots tend to focus on collision avoidance while ignoring human-robot interactions in social life. For instance, robots can pass along…
Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learning
Shunyi Yao1, Guangda Chen, Quecheng Qiu +3
It is challenging for a mobile robot to navigate through human crowds. Existing approaches usually assume that pedestrians follow a predefined collision avoidance strategy, like so…
DRQN-based 3D Obstacle Avoidance with a Limited Field of View
Yu'an Chen, Guangda Chen, Lifan Pan +4
In this paper, we propose a map-based end-to-end DRL approach for three-dimensional (3D) obstacle avoidance in a partially observed environment, which is applied to achieve autonom…
Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Model
Yu'an Chen, Ruosong Ye, Ziyang Tao +7
Deep reinforcement learning (DRL) algorithms have proven effective in robot navigation, especially in unknown environments, by directly mapping perception inputs into robot control…