Occlusion Vehicle Target Recognition Method Based on Component Model by Haorui Han, Hanshan Li

“…Then, the component mask recognition unit is introduced to remove the occlusion component feature area and realize the accurate recognition of the occluded vehicle. By labeling the public data set and the collected data set, six types of vehicle component data sets are constructed for training, as well as design ablation experiments and comparison experiments to verify the trained network, which prove the superiority of the recognition algorithm. …”
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Attention-mechanism-based tracking method for intelligent Internet of vehicles by Xu Kang, Bin Song, Jie Guo, Xiaojiang Du, Mohsen Guizani

“…It mainly includes the following contents: (1) a fully convolutional neural network fused attention mechanism with the selection of the deep features for convolution; (2) a separation method for template and semantic background region to separate target vehicles from the background in the initial frame adaptively; (3) a two-stage method for model training using our traffic dataset. …”
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Optimized Energy Consumption of Electric Vehicles with Driving Pattern Recognition for Real Driving Scenarios by Bedatri Moulik, Sanmukh Kaur, Muhammad Ijaz

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Reinforcement Learning for Computational Guidance of Launch Vehicle Upper Stage by Shiyao Li, Yushen Yan, Hao Qiao, Xin Guan, Xinguo Li

“…This manuscript investigates the use of a reinforcement learning method for the guidance of launch vehicles and a computational guidance algorithm based on a deep neural network (DNN). …”
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Tri-band vehicle and vessel dataset for artificial intelligence research by Yingjian Liu, Gangnian Zhao, Shuzhen Fan, Cheng Fei, Junliang Liu, Zhishuo Zhang, Liqian Wang, Yongfu Li, Xian Zhao, Zhaojun Liu

“…About 60% of the dataset has been manually labeled with object instances to train and evaluate well-established object detection algorithms. …”
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Electric Vehicle Sentiment Analysis Using Large Language Models by Hemlata Sharma, Faiz Ud Din, Bayode Ogunleye

“…EV companies are becoming significant competitors in the automotive industry and are projected to cover up to 30% of the United States light vehicle market by 2030 In this study, we present a comparative study of large language models (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimised BERT (RoBERTa), and a generalised autoregressive pre-training method (XLNet) using Lucid Motors and Tesla Motors YouTube datasets. …”
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Research on Driver Driving Behavior Based on Decision Tree by LI Xi, FENG Ba, XIE Yongbo, WANG Wenming

“…Driver driving behavior is one of the important factors affecting energy consumption of vehicle. In order to accurately analyze driving behavior of a driver, correlation analysis of driving behavior parameters through grey correlation analysis was made, and then a driver driving behavior model was established combined with C4.5 decision tree algorithm. …”
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Comparative analysis of object tracking algorithms by B. A. Zalesky, V. A. Ivanyukovich, K. V. Reer, D. A. Starikovich

“…To train the neural network part of the trackers, versions of the algorithms were written in the Python programming language, and to calculate and analyze characteristics in conditions close to real ones, in C++, which required converting the trained network using the TensorRT framework. …”
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Cooperative Patrol Control of Multiple Unmanned Surface Vehicles for Global Coverage by Yuan Liu, Xirui Xu, Guoxing Li, Lingyun Lu, Yunfan Gu, Yuna Xiao, Wenfang Sun

“…The study proposes a cooperative patrol control algorithm for multiple unmanned surface vehicles (Multi-USVs) based on a hybrid embedded task state information model and reward reshaping techniques, addressing global coverage challenges in dynamic aquatic environments. …”
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FEXGBIDS: Federated XGBoost-Based Intrusion Detection System for In-Vehicle Network by Jie Li, Yuanyuan Song, Ming-Gang Zheng, Shuo Zhang, Han Liang

“…The system effectively resists spoofing attacks and data leakage, while supporting efficient collaborative training in large-scale Internet of Vehicles (IoV) environments.…”
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A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries by Alice Cervellieri

“…The accurate prediction and efficient management of the State of Charge (SoC) of electric vehicle (EV) batteries are critical challenges in the integration of vehicle-to-grid (V2G) systems within multi-energy microgrid (MMO) models. …”
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Dimensionality Reduction Optimization of High Subsonic Unmanned Aerial Vehicles Intake by Zeqi QI, Zheng GUO, Suqi CHEN, Ke YU

“…Specifically, for outlet total pressure distortion, the Kriging model trained with sensitivity analysis data had an average error reduced by 0.060 47 compared to the training results without dimensionality reduction, while the Kriging model trained with PCA data had an average error reduced by 0.051 05. …”
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Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation by Boyang Fan, Yuming Bo, Xiang Wu

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On-line Diagnosis Technology for the Shock Absorbers on Subway Vehicle Based on RLS by WANG Yu, LYU Yu, ZHAO Muhua

“…To ensure the safety of train operation and avoid excessive maintenance of shock absorbers, based on the vibration data, a process parameter evaluation model of the recursive least squares (RLS) and a input-output model were presented according to the research of the metro vehicle on-line monitoring technology. …”
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A real-time recursive dynamic model for vehicle driving simulators by M. Mirtaheri, M. Salkhordeh

“…Basic models for specific vehicle subsystems such as tire, steering, brake, power train, aerodynamics, etc., are interfaced with multibody dynamics to create a complete vehicle simulation model. …”
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Consumer Happiness in the Purchase of Electric Vehicles: a Fuzzy Logic Model by Fernando Lámbarry-Vilchis, Aboud Barsekh Onji, Leticia Refugio Chavarría López, Paola Judith Maldonado Colín

“…This research was conducted using a fuzzy Delphi method survey targeting a specific consumer group and two fuzzy inference systems: a multi-input single-output FIS model and an FIS Tree employing a hierarchical fuzzy inference structure, which leverages the survey's training data to optimize the models using different machine learning algorithms. …”
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Advancing Wildlife Protection: Mask R-CNN for Rail Track Identification and Unwanted Object Detection by Istiak Mahmud, Md. Mohsin Kabir, Jungpil Shin, Chayan Mistry, Yoichi Tomioka, M. F. Mridha

“…Similarly, sometimes train accident is prevalent at the rail gates, where motor vehicles are crossing the train line. …”
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Electric Vehicle Sales Forecast for the UK: Integrating Machine Learning, Time Series Models, and Global Trends by Shima Veysi, Mohammad Moshfeghi, Amir Sadrfaridpour, Peiman Emamy

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A Framework of Recommendation System for Unmanned Aerial Vehicle Autonomous Maneuver Decision by Qinzhi Hao, Tengyu Jing, Yao Sun, Zhuolin Yang, Jiali Zhang, Jiapeng Wang, Wei Wang

“…Through rigorous computer-based testing, we validated the effectiveness of established recommendation algorithms within our framework. Notably, the prioritized experience replay deep deterministic policy gradient (PER-DDPG) algorithm, employing dense rewards and continuous actions, demonstrated superior performance, achieving a 69% success rate in confrontational scenarios against a versatile expert algorithm after 1000 training iterations, marking an 80% reduction in training time compared to conventional reinforcement learning methods. …”
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Data Analysis for Emotion Classification Based on Bio-Information in Self-Driving Vehicles by Tae-Yeun Kim, Hoon Ko, Sung-Hwan Kim

“…When 80% of data were learned according to the ratio of training data by using the SVM algorithm to classify the EEG, blood pressure, and pulse rate databased on the biometric emotion information, the highest performance of 86.1% was shown. …”
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