Target Vehicle Research Articles

Research on Vehicle Detection in Infrared Aerial Images in Complex Urban and Road Backgrounds

The detection of vehicle targets in infrared aerial remote sensing images captured by drones presents challenges due to a significant imbalance in vehicle distribution, complex backgrounds, the large scale of vehicles, and the dense and arbitrarily oriented distribution of targets. The RYOLOv5_D model is proposed based on the YOLOv5-obb rotation model. Firstly, we reconstruct a new vehicle remote sensing dataset, BalancedVehicle, to achieve data balance. Secondly, given the challenges of complex backgrounds in infrared remote sensing images, the AAHE method is proposed to highlight infrared remote sensing vehicle targets while reducing background interference during the detection process. Moreover, in order to address the issue of detecting challenges under complex backgrounds, the CPSAB attention mechanism is proposed, which could be used together with DCNv2. GSConv is also used to reduce the model parameters while ensuring accuracy. This combination could improve the model’s generalization ability and, consequently, enhance the detection accuracy for various vehicle categories. The RYOLOv5s_D model, trained on the self-built dataset BalancedVehicle, demonstrates a notable improvement in its mean average precision (mAP), increasing from 73.6% to 78.5%. Specifically, the average precision (AP) for large aspect ratio vehicles such as trucks and freight cars increases by 11.4% and 8%, respectively. The RYOLOv5m_D and RYOLOv5l_D models achieve accuracies of 82.6% and 84.3%. The Param of RYOLOv5_D is similar to that of the YOLOv5-obb, while possessing a decrease in computational complexity of 0.6, 4.5, and 12.8GFLOPS. In conclusion, the RYOLOv5_D model’s superior accuracy and real-time capabilities in infrared remote sensing vehicle scenarios are validated by comparing various advanced models based on rotation boxes on the BalancedVehicle dataset.

Modeling the effect of daughter migration on dosimetry estimates for unlabeled actinium-225.

Actinium-225 (225Ac) is an alpha emitting radionuclide which has demonstrated promising results in Targeted Alpha Therapy (TAT). A concern with 225Ac is that the decay energy can break the bond to the targeting vehicle, resulting in the release of free alpha-emitting daughter radionuclides in the body. The aim of this work is to develop a compartment model to describe the movement of unlabeled 225Ac in a human where the daughter isotopes of 225Ac have unique biokinetics. The ICRP Occupational Intake of Radionuclides reports were used to construct a compartment model for the 225Ac decay chain where the daughter isotopes of 225Ac are assigned their own unique transfer coefficients (TCs) between compartments. Computer simulations were performed for unlabeled 225Ac uniformly placed in the plasma and only the dose from alpha particles was considered. Absorbed doses to normal organs were determined for the liver, kidneys, bone, soft tissue, active marrow, and blood. Simulations were performed for the case when: (1) the daughters have unique biokinetics and (2) the daughters decay at the site of 225Ac. When the daughters have unique biokinetics, the organs that receive the highest absorbed dose are the liver (male: 1466.6 mGy/MBq, female: 1885.7 mGy/MBq), bone (male: 293.6 mGy/MBq, female: 403.6 mGy/MBq) and kidneys (male: 260.8 mGy/MBq, female: 294.0 mGy/MBq). These doses were compared to the case when the daughters of 225Ac decay at the site of 225Ac. There was a 13.5% increase in kidney dose, a 0.8% decrease in liver dose, and <0.1% decrease in bone dose calculations when the daughters have unique biokinetics compared to assuming the daughters decay at the site of 225Ac. The kidneys received a large dose estimate (260-295 mGy/MBq) as well as a considerable change in dose of +13.5% when the daughters have unique biokinetics compared to assuming the daughters decay at the site of 225Ac. Therefore, to accurately determine the kidney dose from unlabeled 225Ac in a human, the biokinetics of the daughter isotopes should be considered.

ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network

The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. The data obtained from the simulation of target algorithms and vehicle models cannot be reasonably estimated. This problem affects subsequent vehicle improvement and parameter calibration. The authors relied on the simulation results of the AEB algorithm. We selected the BP Neural Network as the basis and improved it with a genetic algorithm optimized via a roulette algorithm. The regression evaluation indicators of the prediction results show that the GA-BP neural network has better prediction accuracy and generalization ability than the original BP neural network and other optimized BP neural networks. This GA-BP neural network also fills the Gap in Evaluation and Prediction Systems.

Speed Change Pattern Optimization for Improving the Electricity Consumption of an Electric Bus and Its Verification Using an Actual Vehicle

In this study, we focused on the eco-driving of electric vehicles (EVs). The target vehicle is an electric bus developed by our research team. Using the parameters of the bus and speed pattern optimization algorithm, we derived the EV’s eco-driving speed pattern. Compared to the eco-driving of internal combustion engine vehicles (ICVs), we found several different characteristics. We verified these characteristics with actual vehicle driving test data of the target bus, and the results confirmed its rationality. The EV’s eco-driving method can improve electricity consumption by about 10–20% under the same average speed.

Typical motion-based modelling and tracking for vehicle targets in linear road segment

In this paper, a typical motion-based modelling and tracking issue is investigated for vehicle targets in linear road segment by a variable structure multiple model (VSMM) method. Vehicle target motions in a road with multiple lanes are described by typical lane keeping and lane changing manoeuvres. To describe trajectories of typical manoeuvres, an Ornstein–Uhlenbeck process and a sine half-cycle are used to model lane keeping and lane changing motions in lateral direction, respectively. Note that starting point and length of lane changing motion are unknown in target tracking. Time-varying model sets are designed based on the current motion model with different motion parameters. A VSMM tracking frame is constructed to obtain target state estimates with time-varying model set. The effectiveness of the proposed typical motion-based tracking scheme is displayed by simulation results on road vehicle tracking.

Research on Over-the-Horizon Perception Distance Division of Optical Fiber Communication Based on Intelligent Roadways.

With the construction and application of more and more intelligent networking demonstration projects, a large number of advanced roadside digital infrastructures are deployed on both sides of the intelligent road. These devices sense the road situation in real time through algorithms and transmit it to edge computing units and cloud control platforms through high-speed optical fiber transmission networks. This article proposes a cloud edge terminal architecture system based on cloud edge cooperation, as well as a data exchange protocol for cloud control basic platforms. The over-the-horizon scene division and optical fiber network communication model are verified by deploying intelligent roadside devices on the intelligent highway. At the same time, this article uses the optical fiber network communication algorithm and ModelScope large model to model inference on real-time video data. The actual data results show that the StreamYOLO (Stream You Only Look Once) model can use the Streaming Perception method to detect and continuously track target vehicles in real-time videos. Finally, the method proposed in this article was experimentally validated in an actual smart highway digital infrastructure construction project. The experimental results demonstrate the high application value and promotion prospects of the fiber optic network in the division of over the horizon perception distance in intelligent roadways construction.

A Car-Following Model Considering Missing Data Based on TransGAN Networks

Car-following behavior is closely related to the longitudinal control of the vehicle, affecting the safety of the vehicle and traffic flow stability. In order to interact with the preceding vehicle, the target vehicle usually collects the driving data of the preceding vehicle. However, data acquisition devices often face malfunctions caused by various unpredictable disruptions, resulting in missing value problems. This may cause the target vehicle to make wrong control decisions. Given this situation, a new car-following(CF) model considering missing data based on Transformer-Generative Adversarial Networks (TransGAN) is proposed. Firstly, Transformer Network with multi-head attention is used to deeply extract the potential features from incomplete vehicle state data, which can filter important information from the input and focus on these, while capturing long distance dependencies. Secondly, a Generative Adversarial Network is constructed. The Generator generates the future multi-step control states of the target vehicle based on the features extracted by Transformer Network. The Discriminator with a fully connected network is applied to simultaneously ensure the generation accuracy. Finally, our proposed model was trained and tested on a publicly available NGSIM I-80 dataset. Compared with other existing advanced works, our model can fit the actual control states of the target vehicle with higher accuracy under different data missing rates of the preceding vehicle, which demonstrates that the proposed method effectively improves the robustness of vehicle longitudinal car-following control under missing data.

An Intelligent Control Method for Industrial Equipment Based on Distributed Electrical and Electronic Architecture

Abstract The article establishes the objective function of distributed electronic and electrical architecture from three dimensions of economy, quantization and loadability, and constructs a mathematical model for multi-objective optimization based on the satisfaction of constraints. The model was applied and tested in the intelligent control system of automotive industrial equipment. In practical applications, the intelligent control system can automatically reduce the speed to 16km/h to maintain a safe distance and prevent collision when the target vehicle is monitored to enter the lane it is in and the distance between the two cars is less than 30 meters. Under 1500 pcu/h traffic flow conditions, the average delays of vehicles at the intersection through intelligent control were only 8.06 seconds and 8.02 seconds, compared to 20.58 seconds and 23.29 seconds for vehicles under conventional control. Vehicle intelligent control systems based on distributed electronic and electrical architectures have great potential for development and significant safety performance improvement in automotive industrial equipment manufacturing. The research in this paper provides a new perspective and adequate technical support for the intelligent control of future industrial equipment.

Research on Intelligent Vehicle Operation Risk Assessment and Early Warning Based on Predictive Risk Field

In order to enhance the driving safety of intelligent vehicles in complex road scenarios, a method for vehicle operation risk assessment and early warning based on the predictive risk field is proposed. The temporal feature vector composed of the spatiotemporal state characteristics of the ego vehicle and surrounding traffic participants is taken as input data for the Attention‐Bidirectional Long‐Short Term Memory (Attention‐BiLSTM) model, which is trained to establish the desired mapping relationship. By predicting the motion state of the target vehicle and utilizing an improved risk field model based on the target vehicle of heading angle, the predictive risk field is obtained. This allows for the assessment of the ego vehicle operational risks. The risk warning model is integrated to provide risk early warning, and the safety path for the ego vehicle is planned based on the interaction between the predictive risk field equipotential lines and the cubic spline curves. Experimental results demonstrate that the proposed vehicle operation risk assessment and early warning model is effective in providing early warnings and safe path references for the ego vehicle in complex urban road test scenarios.

Nanoscale engineering of solid-state materials for boosting hydrogen storage.

The development of novel materials capable of securely storing hydrogen at high volumetric and gravimetric densities is a requirement for the wide-scale usage of hydrogen as an energy carrier. In recent years, great efforts via nanoscale tuning and designing strategies on both physisorbents and chemisorbents have been devoted to improvements in their thermodynamic and kinetic aspects. Increasing the hydrogen storage capacity/density for physisorbents and chemisorbents and improving the dehydrogenation kinetics of hydrides are still considered a challenge. The extensive and fast development of advanced nanotechnologies has fueled a surge in research that presents huge potential in designing solid-state materials to meet the ultimate U.S. Department of Energy capacity targets for onboard light-duty vehicles, material-handling equipments, and portable power applications. Different from the existing literature, in this review, particular attention is paid to the recent advances in nanoscale engineering of solid-state materials for boosting hydrogen storage, especially the nanoscale tuning and designing strategies. We first present a short overview of hydrogen storage mechanisms of nanoscale engineering for boosted hydrogen storage performance on solid-state materials, for example, hydrogen spillover, nanopump effect, nanosize effect, nanocatalysis, and other non-classical hydrogen storage mechanisms. Then, the focus is on recent advancements in nanoscale engineering strategies aimed at enhancing the gravimetric hydrogen storage capacity of porous materials, reducing dehydrogenation temperature and improving reaction kinetics and reversibility of hydrogen desorption/absorption for metal hydrides. Effective nanoscale tuning strategies for enhancing the hydrogen storage performance of porous materials include optimizing surface area and pore volume, fine-tuning nanopore sizes, introducing nanostructure doping, and crafting nanoarchitecture and nanohybrid materials. For metal hydrides, successful strategies involve nanoconfinement, nanosizing, and the incorporation of nanocatalysts. This review further addresses the points to future research directions in the hope of ushering in the practical applications of hydrogen storage materials.

Vehicle Lane Change Multistep Trajectory Prediction Based on Data and CNN_BiLSTM Model

In order to accurately predict the lane‐changing trajectory of the vehicle and improve the driving safety of the vehicle, a lane‐changing trajectory prediction model based on the combination of convolutional neural network (CNN) and bidirectional long short‐term memory (BiLSTM) neural network is proposed by comprehensively considering the historical driving behavior, the spatial characteristics of surrounding vehicles and the bidirectional time sequence information of the vehicle trajectory. Firstly, the vehicle trajectory data are filtered and smoothed, and it is divided into three categories: left lane change, right lane change, and straight driving, and a lane change trajectory sample set is constructed. Secondly, CNN‐BiLSTM model is constructed to identify the sample set of lane‐changing trajectory. Considering the interaction between vehicles in the driving process, the information of predicted vehicle, and surrounding vehicles is taken as the input of the model. The extracted feature vector is input to the BiLSTM layer for prediction after the CNN layer feature extraction, and the horizontal and vertical coordinates of the target vehicle at the next time are output. Thirdly, the trajectory data of the US‐101 dataset in NGSIM is selected to verify the performance of the CNN‐BiLSTM model, and at the same time, it is compared with models such as CNN‐LSTM, long short‐term memory (LSTM), BiLSTM, and CNN‐GRU‐Att. Finally, the verification result shows that the overall fitting degree of the vehicle lane change trajectory prediction of the proposed model reaches 99.50%, and the mean square error and mean absolute error are 0.0003076 and 0.01417, which are improved compared with other models. In the meanwhile, the research on multistep trajectory prediction in different prediction time domains is carried out. It was found that the longer the prediction time domain is, the lower the prediction performance of the model decreases, but the prediction accuracy still reached more than 96%, and it was able to accurately predict the lane change trajectory.

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

Automatic Identification and Tracking Method of Case-Related Vehicles Based on Computer Vision Algorithm

Abstract Amidst the rapid advancement of artificial intelligence, computer vision algorithms have found extensive applications across various societal sectors. This paper presents the development of an automatic vehicle identification algorithm for crime-related scenarios, leveraging enhancements in computer vision technology. Initially, we refine the multi-scale feature fusion within the YOLOv4 architecture, subsequently substituting the standard convolution in the feature extraction network with depth-separable convolution to minimize parameter computation. Furthermore, we replace the conventional CIOU target localization loss function with EIOU to expedite model convergence. To address the issue of target vehicle detection failures, we incorporate the Kalman filter algorithm, ensuring precise tracking. Our experimental analysis, which utilizes both target detection and multi-target tracking evaluation indices, demonstrates that the modified YOLOv4 algorithm excels in recall, precision, and average IOU metrics compared to other algorithms, with a mean Average Precision (mAP) of 95.68% and an average detection speed of 0.039 seconds per image, satisfying real-time operational criteria. Vehicle tracking efficacy, compared using the YOLOv4-based detector before and after the modifications, shows significant improvements, indicated by a reduction in Identification Switches (IDS) across all video samples. This study introduces a robust method for the accurate identification and localization of vehicles involved in criminal activities, significantly enhancing case resolution efficiency.

High-Precision Imaging and Dense Vehicle Target Detection Method for Airborne Single-Pass Circular Synthetic Aperture Radar

High-Precision Imaging and Dense Vehicle Target Detection Method for Airborne Single-Pass Circular Synthetic Aperture Radar

Special Vehicle Target Detection and Tracking Based on Virtual Simulation Environment and YOLOv5-Block+DeepSort Algorithm

Special Vehicle Target Detection and Tracking Based on Virtual Simulation Environment and YOLOv5-Block+DeepSort Algorithm

Vehicle Target Detection Algorithm Based on Improved Faster R-CNN for Remote Sensing Images

Vehicle Target Detection Algorithm Based on Improved Faster R-CNN for Remote Sensing Images

SatDetX-YOLO: A More Accurate Method for Vehicle Target Detection in Satellite Remote Sensing Imagery

SatDetX-YOLO: A More Accurate Method for Vehicle Target Detection in Satellite Remote Sensing Imagery

Data Distribution Loss for Imbalanced SAR Vehicle Target Recognition

Data Distribution Loss for Imbalanced SAR Vehicle Target Recognition

GIVA: Interaction-aware trajectory prediction based on GRU-Improved VGG-Attention Mechanism model for autonomous vehicles

Predicting future trajectories is crucial for autonomous vehicles, as accurate predictions enhance safety and inform subsequent decision-making and planning modules. This is however a challenging task due to the complex interactions between surrounding vehicles. Existing methods struggled to extract deep representations and often overlook spatial dependence. To address this problem, this paper introduces GIVA, an interaction-aware trajectory prediction method based on the Gated Recurrent Unit (GRU)-Improved Visual Geometry Group (VGG)-Attention Mechanism model. GIVA first encodes the historical trajectories of the target vehicle and its surrounding vehicles using a GRU Encoder. Next, an Interaction Module, which combines the Improved VGG Pooling Module and the Attention Mechanism Pooling Module, effectively captures spatial interaction features between vehicles. The Improved VGG Pooling Module extracts more detailed and effective interaction information, while the Attention Mechanism Pooling Module emphasizes the importance of surrounding vehicles for the target vehicle’s future trajectory. Lastly, the dynamic encoding feature of the target vehicle and the fused interaction feature are concatenated and input into a GRU Decoder to generate the future trajectory. Experiments on the public Next Generation Simulation (NGSIM) dataset showcase the effectiveness of GIVA compared to existing prediction approaches, demonstrating its potential for improving autonomous vehicle performance.

Performance Analysis of Non-Humidified High-Temperature PEFC by Fuel Cell System Simulator

Polymer electrolyte fuel cells (PEFCs) are widely used for automobiles and other applications due to their excellent start-ability and load followability. For further popularization in the future, it is expected to be applied to commercial vehicles such as large trucks and buses. However, insufficient cooling of the PEFC becomes a major issue, for example when heavy-duty vehicle needs to operate on uphill roads at high-loads and high- temperatures. This is because conventional electrolyte membranes use water as a proton-conducting carrier, so PEFC’s operation temperature must be controlled below 100°C.In this study, we focus on high-temperature PEFC (HT-PEFC), which is expected to improve cooling performance by high-temperature operation (over 100°C). HT-PEFC can operate under non-humidified high-temperature environment by doping phosphoric acid as a proton-conducting carrier in the electrolyte membrane. Then, we evaluate the usefulness of the HT-PEFC by performance analysis using a FC system simulator.We use FC-DynaMo(1),(2), which is researched and developed by NEDO's industry-academia-government collaboration project, as the FC system simulator. FC-DynaMo is based on the FC stack system of Toyota motor corporation's 2nd generation MIRAI. As shown in Figure 1, in addition to the FC stack, hydrogen system, air system, and cooling system are also modeled, making it possible to simulate unsteady operation.The target vehicle for the simulation is a heavy-duty truck (Class8), and the results of a survey conducted by Zhang et al.(3) on the various uses of in the United States were used as a reference. From the results including output power, vehicle speed, and road gradient, we extracted and used the driving data of long-haul with continuous uphill and downhill roads, which is considered to be the high-load and high-temperature operation.For the membrane electrode assembly (MEA) of HT-PEFC, we used APM STD (PBI-MEA) manufactured by Advent technologies, which uses a polybenzimidazole membrane. We give the characteristic that the exchange current density increases exponentially as the temperature rises, referring to the study of Korsgaard et al.(4). In addition, proton conductivity is given as 10S/m published by Advent technologies. Figure 2(a) shows the calculated IV characteristic of MIRAI-MEA. And Figure 2(b) shows the that of PBI-MEA. As the temperature rises, the effect of reducing the activation overpotential by increasing the exchange current density can be confirmed. These results are in good agreement with the study which investigated the operation characteristics of PBI-MEA by Chen et al.(5) and Waller et al.(6). However, compared to the IV characteristic of MIRAI-MEA shown in Figure 2(a), the overpotential is too large, the maximum output is less than half, and the heat generation is nearly double. This is because it is nearly twice as large as the MIRAI-MEA’s activation overpotential, which causes a decrease in output power and an increase in heat generation. With this current PBI-MEA performance, it is difficult to evaluate the system compared with MIRAI-MEA. Therefore, in this study, we hypothetically set up a high-temperature MEA assuming future development (Virtual HT-MEA). Virtual HT-MEA is given MIRAI-MEA’s low activation overpotential and the characteristics of PBI-MEA that don’t dry-out at high-temperature. Figure 2(c) shows the Virtual HT-MEA’s IV characteristics. Here, at high current density, the performance at 120°C and 180°C is lower than that at 70°C because the concentration overpotential increases due to the decrease in the oxygen molar concentration at high-temperature.Figure 3 shows the results of Class8 driving simulation with each MEA adapted to the FC stack. In the MIRAI-MEA shown in Figure 3(a), a combination of 4-FC stack systems and 5-cooling systems is optimal. With this combination, the FC cell temperature can be controlled below 95°C. Figure 3(b) shows the analysis results of the FC stack using PBI-MEA. As shown in Figure 2(b), the PBI-MEA has a large overpotential, so a combination of 10-FC stack systems and 5-cooling systems is required to meet the request power. The current PBI-MEA’s performance cannot show the merits of non-humidified high-temperature operation. Figure 3(c) shows the analysis results of the FC stack using the Virtual HT-MEA. Stable operation is possible with 4-FC stack systems and 2-cooling systems. This means that 3-cooling systems can be reduced compared to the MIRAI-MEA’s system. This is because the cooling performance is improved due to the larger temperature difference between the outside air and the FC stacks.Reference(1)S.Hasegawa, et al., ECS Transactions,104, (2021)(2)S.Hasegawa, et al., ECS Transactions,109, (2022)(3)C.Zhang, et al., Transportation Research,Part D95, (2021)(4)A.R.Korsgaad, et al., Journal of Power Sources,162, (2006)(5)C.-Y.Chen, et al., Journal of Power Sources,195, (2010)(6)M.G.Waller, et al., International Journal of Hydrogen Energy,41, (2016) Figure 1