147 citations · 341 across the 18 of their papers we have counts for
19 papers
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
Fei Ye, Shen Zhang, Pin Wang +1
In this survey, we systematically summarize the current literature on studies that apply reinforcement learning (RL) to the motion planning and control of autonomous vehicles. Many…
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks
Pin Wang, Hanhan Li, Ching-Yao Chan
Learning from demonstrations has made great progress over the past few years. However, it is generally data hungry and task specific. In other words, it requires a large amount of…
Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles
Fei Ye, Pin Wang, Ching-Yao Chan +1
Recent advances in supervised learning and reinforcement learning have provided new opportunities to apply related methodologies to automated driving. However, there are still chal…
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning
Fei Ye, Xuxin Cheng, Pin Wang +2
Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan, overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane chang…
TPPO: A Novel Trajectory Predictor with Pseudo Oracle
Biao Yang, Caizhen He, Pin Wang +3
Forecasting pedestrian trajectories in dynamic scenes remains a critical problem in various applications, such as autonomous driving and socially aware robots. Such forecasting is…
A Novel Graph based Trajectory Predictor with Pseudo Oracle
Biao Yang, Guocheng Yan, Pin Wang +3
Pedestrian trajectory prediction in dynamic scenes remains a challenging and critical problem in numerous applications, such as self-driving cars and socially aware robots. Challen…
Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles
Pin Wang, Hanhan Li, Ching-Yao Chan
Reinforcement Learning algorithms have recently been proposed to learn time-sequential control policies in the field of autonomous driving. Direct applications of Reinforcement Lea…
Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning
Pin Wang, Dapeng Liu, Jiayu Chen +2
Making decisions in complex driving environments is a challenging task for autonomous agents. Imitation learning methods have great potentials for achieving such a goal. Adversaria…
Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks
Long Xin, Pin Wang, Ching-Yao Chan +3
As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possib…
A Data Driven Method of Feedforward Compensator Optimization for Autonomous Vehicle Control
Pin Wang, Tianyu Shi, Chonghao Zou +2
A reliable controller is critical for execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface,…
Continuous Control for Automated Lane Change Behavior Based on Deep Deterministic Policy Gradient Algorithm
Pin Wang, Hanhan Li, Ching-Yao Chan
Lane change is a challenging task which requires delicate actions to ensure safety and comfort. Some recent studies have attempted to solve the lane-change control problem with Rei…
A Reinforcement Learning Approach for Intelligent Traffic Signal Control at Urban Intersections
Mengyu Guo, Pin Wang, Ching-Yao Chan +1
Ineffective and inflexible traffic signal control at urban intersections can often lead to bottlenecks in traffic flows and cause congestion, delay, and environmental problems. How…
Behavior Planning of Autonomous Cars with Social Perception
Liting Sun, Wei Zhan, Ching-Yao Chan +1
Autonomous cars have to navigate in dynamic environment which can be full of uncertainties. The uncertainties can come either from sensor limitations such as occlusions and limited…
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…
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…
A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers
Pin Wang, Ching-Yao Chan, Arnaud de La Fortelle
Lane change is a crucial vehicle maneuver which needs coordination with surrounding vehicles. Automated lane changing functions built on rule-based models may perform well under pr…
Autonomous Ramp Merge Maneuver Based on Reinforcement Learning with Continuous Action Space
Pin Wang, Ching-Yao Chan
Ramp merging is a critical maneuver for road safety and traffic efficiency. Most of the current automated driving systems developed by multiple automobile manufacturers and supplie…
Automated Driving Maneuvers under Interactive Environment based on Deep Reinforcement Learning
Pin Wang, Ching-Yao Chan, Hanhan Li
Safe and efficient autonomous driving maneuvers in an interactive and complex environment can be considerably challenging due to the unpredictable actions of other surrounding agen…
Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge
Pin Wang, Ching-Yao Chan
Multiple automakers have in development or in production automated driving systems (ADS) that offer freeway-pilot functions. This type of ADS is typically limited to restricted-acc…