7 citations · 14 across the 5 of their papers we have counts for
6 papers
Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning
Kaleb Ben Naveed, Zhiqian Qiao, John M. Dolan
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous driving problem significantly complex. Current sampling-based methods such as Rapidly Explori…
Safe Trajectory Planning Using Reinforcement Learning for Self Driving
Josiah Coad, Zhiqian Qiao, John M. Dolan
Self-driving vehicles must be able to act intelligently in diverse and difficult environments, marked by high-dimensional state spaces, a myriad of optimization objectives and comp…
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning
Zhiqian Qiao, Jeff Schneider, John M. Dolan
For autonomous vehicles, effective behavior planning is crucial to ensure safety of the ego car. In many urban scenarios, it is hard to create sufficiently general heuristic rules,…
Human Driver Behavior Prediction based on UrbanFlow
Zhiqian Qiao, Jing Zhao, Zachariah Tyree +3
How autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off. Unde…
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning
Zhiqian Qiao, Zachariah Tyree, Priyantha Mudalige +2
In this work, we propose a hierarchical reinforcement learning (HRL) structure which is capable of performing autonomous vehicle planning tasks in simulated environments with multi…
Vehicle Powertrain Connected Route Optimization for Conventional, Hybrid and Plug-in Electric Vehicles
Zhiqian Qiao, Orkun Karabasoglu
Most navigation systems use data from satellites to provide drivers with the shortest-distance, shortest-time or highway-preferred paths. However, when the routing decisions are ma…