Publications (33)
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…
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…
Envelope Imbalance Learning Algorithm based on Multilayer Fuzzy C-means Clustering and Minimum Interlayer discrepancy
Fan Li, Xiaoheng Zhang, Pin Wang +1
Imbalanced learning is important and challenging since the problem of the classification of imbalanced datasets is prevalent in machine learning and data mining fields. Sampling ap…
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…
Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super Resolution
Hongbo Wang, Huaibo Huang, Pin Wang +3
Generative priors in Image Super-Resolution (SR) often compromise faithful restoration, we attribute this limitation to a fundamental spectral misalignment between isotropic object…
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution
Mingfeng Jiang, Minghao Zhi, Liying Wei +6
High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time. In this paper, a framework called…
Classification Algorithm of Speech Data of Parkinsons Disease Based on Convolution Sparse Kernel Transfer Learning with Optimal Kernel and Parallel Sample Feature Selection
Xiaoheng Zhang, Yongming Li, Pin Wang +2
Labeled speech data from patients with Parkinsons disease (PD) are scarce, and the statistical distributions of training and test data differ significantly in the existing datasets…
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…
Subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Parkinson's Speech Data
Yiwen Wang, Fan Li, Xiaoheng Zhang +2
Parkinson disease (PD)'s speech recognition is an effective way for its diagnosis, which has become a hot and difficult research area in recent years. As we know, there are large c…
Overlapping oriented imbalanced ensemble learning method based on projective clustering and stagewise hybrid sampling
Fan Li, Bo Wang, Pin Wang +1
The challenge of imbalanced learning lies not only in class imbalance problem, but also in the class overlapping problem which is complex. However, most of the existing algorithms…
Integrated Age Estimation Mechanism
Fan Li, Yongming Li, Pin Wang +3
Machine-learning-based age estimation has received lots of attention. Traditional age estimation mechanism focuses estimation age error, but ignores that there is a deviation betwe…
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…
Subject Envelope based Multitype Reconstruction Algorithm of Speech Samples of Parkinson's Disease
Yongming Li, Chengyu Liu, Pin Wang +2
The risk of Parkinson's disease (PD) is extremely serious, and PD speech recognition is an effective method of diagnosis nowadays. However, due to the influence of the disease stag…
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…
Envelope imbalanced ensemble model with deep sample learning and local-global structure consistency
Fan Li, Xiaoheng Zhang, Yongming Li +1
The class imbalance problem is important and challenging. Ensemble approaches are widely used to tackle this problem because of their effectiveness. However, existing ensemble meth…
A new Stack Autoencoder: Neighbouring Sample Envelope Embedded Stack Autoencoder Ensemble Model
Chuanyan Zhou, Jie Ma, Fan Li +3
Stack autoencoder (SAE), as a representative deep network, has unique and excellent performance in feature learning, and has received extensive attention from researchers. However,…
Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition
Yongming Li, Lang Zhou, Lingyun Qin +5
Diagnosis and therapeutic effect assessment of Parkinson disease based on voice data are very important,but its few-shot learning problem is challenging.Although deep learning is g…
Adversarially Robust Frame Sampling with Bounded Irregularities
Hanhan Li, Pin Wang
In recent years, video analysis tools for automatically extracting meaningful information from videos are widely studied and deployed. Because most of them use deep neural networks…
Tuning Real-World Image Restoration at Inference: A Test-Time Scaling Paradigm for Flow Matching Models
Purui Bai, Junxian Duan, Pin Wang +4
Although diffusion-based real-world image restoration (Real-IR) has achieved remarkable progress, efficiently leveraging ultra-large-scale pre-trained text-to-image (T2I) models an…
YAYI 2: Multilingual Open-Source Large Language Models
Yin Luo, Qingchao Kong, Nan Xu +50
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world…
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 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…
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…
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…
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…
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…
Hybrid Embedded Deep Stacked Sparse Autoencoder with w_LPPD SVM Ensemble
Yongming Li, Yan Lei, Pin Wang +1
Deep learning is a kind of feature learning method with strong nonliear feature transformation and becomes more and more important in many fields of artificial intelligence. Deep a…
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…
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…
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…
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…
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…