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Solving the Vehicle Cooperation Problem at Signal-free Intersection via an Asynchronous Deep Reinforcement Learning Approach
IEEE Internet of Things Journal
Year: 2025 | Early Access Article |
With the rapid development of modern intelligent transportation systems, connected and automated vehicles (CAVs) have garnered significant attention due to their advanced communication and decision-making capabilities. Intelligent collaborative decision-making in dynamic traffic scenarios poses significant challenges in the research of CAV technology. Strategies and methods have been developed to ...Show More
Snake Optimizer Improved Variational Mode Decomposition for Short-Term Prediction of Vehicle Charging Loads
Quanxue Guan;Qinhe Liu;Shaocong Tao;Yunjian Xu;Di Zhou;Haoyong Chen;Xiaojun Tan
The rapid proliferation of electric vehicles (EVs) significantly impacts the power grid, necessitating effective forecasting of charging loads. For ultra short-term load prediction, this paper proposes a Snake Optimization (SO)-Variational Mode Decomposition (VMD)-Long Short-Term Memory (LSTM) algorithm trained by only the historical charging data. Before the prediction starts, the VMD method is u...Show More
State of Health Estimation of Lithium-ion Batteries Based on Relaxation Voltage Reconstruction and AM-LSTM Method
Liangliang Wei;Hongzhang Xu;Yiwen Sun;Qi Diao;Xiaojun Tan;Yuqian Fan;Han Liu
IEEE Transactions on Transportation Electrification
Year: 2025 | Early Access Article |
It is essential to accurately estimate the state of health (SOH) for lithium-ion batteries from the perspectives of safety and reliability. However, most existing data-driven methods are based on charging or discharging data, which is relatively difficult to apply. This paper proposes a novel SOH estimation approach based on the relaxation voltage reconstruction and a long short-term memory networ...Show More
State of Health Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Reconstruction
Yiwen Sun;Qi Diao;Hongzhang Xu;Xiaojun Tan;Yuqian Fan;Liangliang Wei
To guarantee the safe and efficient operation of lithium-ion batteries, it is crucial to precisely estimate the state of health (SOH) of batteries. However, most of the existing studies have primarily focused on complete or large-range charging curves, which are highly challenging to acquire in practical applications. To this end, a novel SOH estimation method based on partial charging curve recon...Show More
MAEMOT: Pretrained MAE-Based Antiocclusion 3-D Multiobject Tracking for Autonomous Driving
Xiaofei Zhang;Zhengping Fan;Ying Shen;Yining Li;Yasong An;Xiaojun Tan
IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Early Access Article |
The existing 3-D multiobject tracking (MOT) methods suffer from object occlusion in real-world traffic scenes. However, previous works have faced challenges in providing a reasonable solution to the fundamental question: “How can the interference of the perception data loss caused by occlusion be overcome?” Therefore, this article attempts to provide a reasonable solution by developing a novel pre...Show More
A Novel SOH Estimation Method With Attentional Feature Fusion Considering Differential Temperature Features for Lithium-Ion Batteries
Yiwen Sun;Hengwei Xie;Qi Diao;Hongzhang Xu;Xiaojun Tan;Yuqian Fan;Liangliang Wei
It is vital to accurately estimate the state of health (SOH) of lithium-ion batteries of electrical vehicles. Despite the significant impact of temperature on the SOH, most conventional data-driven methods for SOH estimation neglect the influence of temperature-related characteristics. This article proposes a novel SOH estimation method with attentional feature fusion (AFF) considering differentia...Show More
State of Health Estimation of Lithium-Ion Batteries Based on Stacked-LSTM Transfer Learning With Bayesian Optimization and Multiple Features
Liangliang Wei;Yiwen Sun;Qi Diao;Hongzhang Xu;Xiaojun Tan;Yuqian Fan
It is critical to accurately estimate the state of health (SOH) to ensure the safe and efficient operation of lithium-ion batteries. To reduce the training amounts of existing data-driven methods, the transfer learning (TL) method has attracted more attention. However, most previous studies lack validation with different battery types and working conditions. Furthermore, the shared knowledge just ...Show More
Sensor Fusion and Motion Planning with Unified Bird’s-Eye View Representation for End-to-end Autonomous Driving
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
End-to-end autonomous driving has made significant advancements in these years. Efficiently fusing multi-modal sensor information to enhance the scene understanding capability and motion planning performance of end-to-end models is currently a prominent research topic. Existing methods fuse multimodal information from different views, which lack efficiency and restrict the sensor extension of the ...Show More
DualAT: Dual Attention Transformer for End-to-End Autonomous Driving
2024 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2024 | Conference Paper |
The effective reasoning of integrated multimodal perception information is crucial for achieving enhanced end-to-end autonomous driving performance. In this paper, we introduce a novel multitask imitation learning framework for end-to-end autonomous driving that leverages a dual attention transformer (DualAT) to enhance the multimodal fusion and waypoint prediction processes. A self-attention mech...Show More
BEVLOC: End-to-End 6-DoF Localization Via Cross-Modality Correlation Under Bird’s Eye View
Nanjie Chen;Jinping Wang;Hao Chen;Ying Shen;Shuai Wang;Xiaojun Tan
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Accurate ego-centric localization assumes a paramount significance in the domain of autonomous driving. However, traditional methods for camera-LiDAR map localization rely on perspective projection to create a unified representation, which often falls short due to challenges such as occlusion and the sparse nature of point cloud data. Despite the recent surge in popularity of the Bird’s-Eye-View (...Show More
Investigation of a Double-Stator Dual-PM Hybrid Excitation Machine With DC Bias Current
Dongqing Liu;Jun Jin;Yiwen Sun;Xiaojun Tan;Benfei Wang;Guodong Feng;Liangliang Wei
This article investigates a double-stator dual permanent magnet (dual-PM) hybrid excitation machine (DS-DHEM) with dc bias current. Integrated winding accommodated on the outer stator can produce the armature field and excitation field simultaneously with the dc bias current to improve the efficiency and flux regulation capability, and the dual consequent-pole PMs on the inner stator and rotor are...Show More
PDLC-LIO: A Precise and Direct SLAM System Toward Large-Scale Environments With Loop Closures
As a key technology of autonomous driving, the high-precision vehicle localization is the prerequisite for automatic steer. With the advantage of accuracy and robustness, the Light Detection and Ranging (LiDAR) is widely employed in Simultaneous Localization and Mapping (SLAM), which has been utilized to provide reliable positioning information. Compared with the experimental vehicles in the restr...Show More
Early Prediction of Knee Point and Knee Capacity for Fast-Charging Lithium-Ion Battery With Uncertainty Quantification and Calibration
Knee point has been observed in the capacity degradation of lithium-ion (Li-ion) batteries under fast charging, such as electric vehicle applications, which divides the degradation into slow aging and fast aging. Early prediction of knee point and knee capacity can help to guarantee the safe and optimal operation of batteries. Existing works fail to predict knee capacity and also lack uncertainty ...Show More
Short-Term EV Charging Load Predicting Based on Adaptive VMD and LSTM Methods
IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society
Year: 2023 | Conference Paper |
Cited by: Papers (5)
The uncoordinated charging of large-scale electric vehicles (EVs) generally deteriorates the peak-valley difference of daily electric demands. To facilitate the operation of charging stations and electric power distributers, this work proposes a charging load prediction algorithm by combining the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) methods. The VMD is adopted...Show More
Mutually Beneficial Transformer for Multimodal Data Fusion
Multimodal feature fusion representation, e.g., hyperspectral image and light detection and ranging (HSI-LiDAR) fusion, is an essential topic for fusion perception. However, existing networks tend to employ mandatory feature stacking or local context fusion strategies between multiple modalities, ignoring the power of globally mutual-guided feature transmission. Therefore, this paper develops a mu...Show More
LMBAO: A Landmark Map for Bundle Adjustment Odometry in LiDAR SLAM
Letian Zhang;Jinping Wang;Lu Jie;Nanjie Chen;Xiaojun Tan;Zhifei Duan
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Existing LiDAR odometry strategies match a new scan iteratively with previous fixed-pose scans, gradually accumulating errors. Furthermore, as an effective joint optimization mechanism, bundle adjustment (BA) cannot be directly introduced into odometry due to the intensive computation of global landmarks. Therefore, this paper designs a landmark map for bundle adjustment odometry (LMBAO) in LiDAR ...Show More
Asymptotically Consistent Estimation of Preferential Attachments in Growing Networks
Preferential attachment, also referred to as the “rich-get-richer” mechanism, characterizes the ability of already existing nodes in an evolutionary network to acquire new connections from newly-coming nodes throughout the growing process of the network. This mechanism is responsible for the emergence of some critical structures in many real networks, such as the Saccharomyces cerevisiae protein-p...Show More
Body weight, as one of the biometric traits, has been studied in both the forensic and medical domains. However, estimating weight directly from 2-D images is particularly challenging since visual inspection is rather sensitive to the distance between the subject and camera, even for frontal view images. In this case, the widely used body mass index (BMI), which is associated with body height and ...Show More
Point-Guided Contrastive Learning for Monocular 3-D Object Detection
IEEE Transactions on Cybernetics
Cited by: Papers (8)
3-D object detection is a fundamental task in the context of autonomous driving. In the literature, cheap monocular image-based methods show a significant performance drop compared to the expensive LiDAR and stereo-images-based algorithms. In this article, we aim to close this performance gap by bridging the representation capability between 2-D and 3-D domains. We propose a novel monocular 3-D ob...Show More
Local-guided Global Collaborative Learning Transformer for Vehicle Reidentification
2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Vehicle reidentification(ReID) has attracted much attention and is significant for traffic security surveillance. Due to the variety of views of the same vehicle captured by different camera and the great similarity in the visual appearance of different vehicles, it is necessary to explore how to effectively utilize local detail information to achieve collaborative perception to highlight discrimi...Show More
Deep Reinforcement Learning Based Radio Resource Selection Approach for C- V2X Mode 4 in Cooperative Perception Scenario
2022 18th International Conference on Mobility, Sensing and Networking (MSN)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
In recent years, vehicles have been equipped with multiple sensors to enable assisted driving and even autonomous driving. However, due to the physical characteristics of the sensors, there are numerous shortcomings in the perception of the surrounding environment by a single vehicle. The development of vehicle-to-everything technology enables vehicles to extend their sensing range or enhance the ...Show More
Battery Capacity Estimation based on Convolutional Neural Network
2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Estimating the battery capacity is an efficient way to monitor battery usage in real time. The data-driven capacity estimation methods proposed in the current literature usually require all the voltage, temperature, current data, etc. during the battery operation. However, obtaining such a large amount of data is unrealistic in practical application scenarios. Therefore, in this paper, a convoluti...Show More
Radio Resource Selection in C-V2X Mode 4: A Multiagent Deep Reinforcement Learning Approach
2022 International Conference on Computer Communications and Networks (ICCCN)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
The Third Generation Partnership Project has standardized cellular vehicle-to-everything (C-V2X) sidelink Mode 4 communication to support vehicle-to-vehicle safety applications. In Mode 4, the sensing-based semi-persistent scheduling (SPS) scheme allows vehicles to select radio resources autonomously. In particular, SPS has three steps to generate available resource lists (ARLs) for resource selec...Show More
Multiagent Reinforcement Learning-Based Semi-Persistent Scheduling Scheme in C-V2X Mode 4
Bo Gu;Weixiang Chen;Mamoun Alazab;Xiaojun Tan;Mohsen Guizani
The Third Generation Partnership Project has standardized cellular vehicle-to-everything (C-V2X) sidelink Mode 4 to support direct communication between vehicles. In Mode 4, the sensing-based semipersistent scheduling (SPS) scheme enables vehicles to autonomously reserve and select radio resources. In particular, SPS has three processes to realize the resource scheduling, including continuously se...Show More
Spectral-Spatial Symmetrical Aggregation Cross-Linking Multi-Modal Data Fusion Network
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
In this paper, a spectral-spatial symmetrical aggregation cross-linking network (SACLNet) is developed for multi-modal data classification, which contains three modules as follows. First, the Spectro-Spatial Feature Learning Module is proposed, using the involution operation sliding over the spectral channels of hyperspectral image (HSI) and fused-sharing weight obtained from HSI and light detecti...Show More
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