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Vehicles, Volume 6, Issue 2 (June 2024) – 14 articles

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18 pages, 9058 KiB  
Article
New Design of an Electrical Excavator and Its Path Generation for Energy Saving and Obstacle Avoidance
by Omid Ahmadi Khiyavi, Jaho Seo and Xianke Lin
Vehicles 2024, 6(2), 832-849; https://doi.org/10.3390/vehicles6020040 - 9 May 2024
Viewed by 302
Abstract
This study’s goals are divided into two categories. The first is to design and build an excavator equipped with parallel electrical linear actuators. The second is to generate and test a PSO-based and a PFM-based path for this excavator in order to save [...] Read more.
This study’s goals are divided into two categories. The first is to design and build an excavator equipped with parallel electrical linear actuators. The second is to generate and test a PSO-based and a PFM-based path for this excavator in order to save energy by reducing energy consumption, improve the digging accuracy by minimizing the deviation between the desired and dug surfaces of the ground, and prevent colliding with subsurface objects. For this purpose, computer vision was employed to improve monitoring and verification. Five types of experiments were carried out in this investigation. The first two and the other three examined the impact of energy conservation in PSO- and PFM-based path generation, respectively. Finally, the results from these experiments were compared to identify and show the effect of optimal path generation. Full article
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18 pages, 6693 KiB  
Article
The Individual Drive of a Wheelset and the Problematics of Its Electromechanical Phenomena
by Vojtěch Dybala, Tomáš Fridrichovský and Jan Kalivoda
Vehicles 2024, 6(2), 814-831; https://doi.org/10.3390/vehicles6020039 - 4 May 2024
Viewed by 490
Abstract
This paper deals with the phenomenon of torsion oscillations in railway vehicle drive systems. The main goal is to reduce the risk of presence of torsional oscillations in wheelset drive, eventually to propose systems to effectively identify and eliminate torsional oscillations of wheelsets. [...] Read more.
This paper deals with the phenomenon of torsion oscillations in railway vehicle drive systems. The main goal is to reduce the risk of presence of torsional oscillations in wheelset drive, eventually to propose systems to effectively identify and eliminate torsional oscillations of wheelsets. Therefore, a simulation wheelset drive model including a detailed model of the asynchronous traction motor control was built. The results of computer simulations show that the torsional oscillations can be effectively eliminated by avoiding the resonance states between the excitation frequencies given by pulse width modulation of the inverter and the eigenfrequencies of the mechanical part of the drive. Furthermore, it was found that the presence of torsional oscillations of the wheels can be detected based on the traction motor current ripple. The wheelset drive model was subsequently implemented in a simulation model of a four-axle locomotive. A new algorithm of an anti-slip protection system that utilizes motor currents was implemented in the model. Simulations show that such an anti-slip protection system can prevent the occurrence of undesired large amplitude of wheelset torsional oscillations. The models and simulation results are presented in detail in the paper. Full article
(This article belongs to the Special Issue Railway Vehicles and Infrastructure)
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15 pages, 3411 KiB  
Article
Random Forest-Based Grouping for Accurate SOH Estimation in Second-Life Batteries
by Joelton Deonei Gotz, José Rodolfo Galvão, Fernanda Cristina Corrêa, Alceu André Badin, Hugo Valadares Siqueira, Emilson Ribeiro Viana, Attilio Converti and Milton Borsato
Vehicles 2024, 6(2), 799-813; https://doi.org/10.3390/vehicles6020038 - 30 Apr 2024
Viewed by 300
Abstract
Retired batteries pose a significant current and future challenge for electric mobility due to their high cost and the need for a state of health (SOH) above 80% to supply energy efficiently. Recycling and alternative applications are the primary options for these batteries, [...] Read more.
Retired batteries pose a significant current and future challenge for electric mobility due to their high cost and the need for a state of health (SOH) above 80% to supply energy efficiently. Recycling and alternative applications are the primary options for these batteries, with recycling still undergoing research as regards more efficient and cost-effective techniques. While advancements have been made, researchers are actively seeking improved methods. Repurposing retired batteries for lower-performance applications like stationary systems or low-speed vehicles is recommended. Second-life batteries (SLB) can be directly reused or reconstructed, with the latter involving the disassembly, measurement, and separation of cells based on their characteristics. The traditional measurement process, involving full charge and discharge cycles, is time-consuming. To address this, a Machine Learning (ML)-based SOH estimator is introduced in this work, offering the instant measurement and estimation of battery health without complete discharge. The results indicate that the model can accurately identify SOH within a nominal capacity range of 1400–2300 mAh, with a resolution near 45.70 mAh, in under five minutes of discharging. This innovative technique could be instrumental in selecting and assembling SLB packs. Full article
(This article belongs to the Special Issue Battery Management of Hybrid Electric Vehicles)
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18 pages, 2271 KiB  
Article
Sim-to-Real Application of Reinforcement Learning Agents for Autonomous, Real Vehicle Drifting
by Szilárd Hunor Tóth, Zsolt János Viharos, Ádám Bárdos and Zsolt Szalay
Vehicles 2024, 6(2), 781-798; https://doi.org/10.3390/vehicles6020037 - 30 Apr 2024
Viewed by 427
Abstract
Enhancing the safety of passengers by venturing beyond the limits of a human driver is one of the main ideas behind autonomous vehicles. While drifting is mostly witnessed in motorsports as an advanced driving technique, it could provide many possibilities for improving traffic [...] Read more.
Enhancing the safety of passengers by venturing beyond the limits of a human driver is one of the main ideas behind autonomous vehicles. While drifting is mostly witnessed in motorsports as an advanced driving technique, it could provide many possibilities for improving traffic safety by avoiding accidents in extreme traffic situations. The purpose of the research presented in this article is to provide a machine learning-based solution to autonomous drifting as a proof of concept for vehicle control at the limits of handling. To achieve this, reinforcement learning (RL) agents were trained for the task in a MATLAB/Simulink-based simulation environment, using the state-of-the-art Soft Actor–Critic (SAC) algorithm. The trained agents were tested in reality at the ZalaZONE proving ground on a series production sports car with zero-shot transfer. Based on the test results, the simulation environment was improved through domain randomization, until the agent could perform the task both in simulation and in reality on a real test car. Full article
(This article belongs to the Topic Vehicle Dynamics and Control)
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16 pages, 1040 KiB  
Article
Mixed Learning- and Model-Based Mass Estimation of Heavy Vehicles
by Abdurrahman İşbitirici, Laura Giarré and Paolo Falcone
Vehicles 2024, 6(2), 765-780; https://doi.org/10.3390/vehicles6020036 - 30 Apr 2024
Viewed by 327
Abstract
This research utilized long short-term memory (LSTM) to oversee an RLS-based mass estimator based on longitudinal vehicle dynamics for heavy-duty vehicles (HDVs) instead of using the predefined rules. A multilayer LSTM network that analyzed parameters such as vehicle speed, longitudinal acceleration, engine torque, [...] Read more.
This research utilized long short-term memory (LSTM) to oversee an RLS-based mass estimator based on longitudinal vehicle dynamics for heavy-duty vehicles (HDVs) instead of using the predefined rules. A multilayer LSTM network that analyzed parameters such as vehicle speed, longitudinal acceleration, engine torque, engine speed, and estimated mass from the RLS mass estimator was employed as the supervision method. The supervisory LSTM network was trained offline to recognize when the vehicle was operated so that the RLS estimator gave an estimate with the desired accuracy and the network was used as a reliability flag. High-fidelity simulation software was employed to collect data used to train and test the network. A threshold on the error percentage of the RLS mass estimator was used by the network to check the reliability of the algorithm. The preliminary findings indicate that the reliability of the RLS mass estimator could be predicted by using the LSTM network. Full article
(This article belongs to the Special Issue Feature Papers on Advanced Vehicle Technologies)
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18 pages, 4441 KiB  
Article
Using Probe Counts to Provide High-Resolution Detector Data for a Microscopic Traffic Simulation
by Tobias Veihelmann, Victor Shatov, Maximilian Lübke and Norman Franchi
Vehicles 2024, 6(2), 747-764; https://doi.org/10.3390/vehicles6020035 - 28 Apr 2024
Viewed by 377
Abstract
Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with [...] Read more.
Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with real-world measurements since these data are often not available. To overcome this issue, this work integrates probe counts from floating car data as reference counts to model a large-scale microscopic traffic scenario with high-resolution detector data. To integrate the frequent probe counts, a road network matching is required. Thus, a novel road network matching method based on a decision tree classifier is proposed. The classifier automatically adjusts its cosine similarity and Hausdorff distance-based similarity metrics to match the network’s requirements. The approach performs well with an F1-score of 95.6%. However, post-processing steps are required to produce a sufficiently consistent detector dataset for the subsequent traffic simulation. The finally modeled traffic shows a good agreement of 95.1%. with upscaled probe counts and no unrealistic traffic jams, teleports, or collisions in the simulation. We conclude that probe counts can lead to consistent traffic simulations and, especially with increasing and consistent penetration rates in the future, help to accurately model large-scale microscopic traffic simulations. Full article
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19 pages, 4690 KiB  
Article
Meta-Feature-Based Traffic Accident Risk Prediction: A Novel Approach to Forecasting Severity and Incidence
by Wei Sun, Lili Nurliynana Abdullah, Puteri Suhaiza Sulaiman and Fatimah Khalid
Vehicles 2024, 6(2), 728-746; https://doi.org/10.3390/vehicles6020034 - 25 Apr 2024
Viewed by 336
Abstract
This study aims to improve the accuracy of predicting the severity of traffic accidents by developing an innovative traffic accident risk prediction model—StackTrafficRiskPrediction. The model combines multidimensional data analysis including environmental factors, human factors, roadway characteristics, and accident-related meta-features. In the model comparison, [...] Read more.
This study aims to improve the accuracy of predicting the severity of traffic accidents by developing an innovative traffic accident risk prediction model—StackTrafficRiskPrediction. The model combines multidimensional data analysis including environmental factors, human factors, roadway characteristics, and accident-related meta-features. In the model comparison, the StackTrafficRiskPrediction model achieves an accuracy of 0.9613, 0.9069, and 0.7508 in predicting fatal, serious, and minor accidents, respectively, which significantly outperforms the traditional logistic regression model. In the experimental part, we analyzed the severity of traffic accidents under different age groups of drivers, driving experience, road conditions, light and weather conditions. The results showed that drivers between 31 and 50 years of age with 2 to 5 years of driving experience were more likely to be involved in serious crashes. In addition, it was found that drivers tend to adopt a more cautious driving style in poor road and weather conditions, which increases the margin of safety. In terms of model evaluation, the StackTrafficRiskPrediction model performs best in terms of accuracy, recall, and ROC–AUC values, but performs poorly in predicting small-sample categories. Our study also revealed limitations of the current methodology, such as the sample imbalance problem and the limitations of environmental and human factors in the study. Future research can overcome these limitations by collecting more diverse data, exploring a wider range of influencing factors, and applying more advanced data analysis techniques. Full article
(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives)
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17 pages, 9837 KiB  
Article
Robust Calibration Technique for Precise Transformation of Low-Resolution 2D LiDAR Points to Camera Image Pixels in Intelligent Autonomous Driving Systems
by Ravichandran Rajesh and Pudureddiyur Venkataraman Manivannan
Vehicles 2024, 6(2), 711-727; https://doi.org/10.3390/vehicles6020033 - 19 Apr 2024
Viewed by 602
Abstract
In the context of autonomous driving, the fusion of LiDAR and camera sensors is essential for robust obstacle detection and distance estimation. However, accurately estimating the transformation matrix between cost-effective low-resolution LiDAR and cameras presents challenges due to the generation of uncertain points [...] Read more.
In the context of autonomous driving, the fusion of LiDAR and camera sensors is essential for robust obstacle detection and distance estimation. However, accurately estimating the transformation matrix between cost-effective low-resolution LiDAR and cameras presents challenges due to the generation of uncertain points by low-resolution LiDAR. In the present work, a new calibration technique is developed to accurately transform low-resolution 2D LiDAR points into camera pixels by utilizing both static and dynamic calibration patterns. Initially, the key corresponding points are identified at the intersection of 2D LiDAR points and calibration patterns. Subsequently, interpolation is applied to generate additional corresponding points for estimating the homography matrix. The homography matrix is then optimized using the Levenberg–Marquardt algorithm to minimize the rotation error, followed by a Procrustes analysis to minimize the translation error. The accuracy of the developed calibration technique is validated through various experiments (varying distances and orientations). The experimental findings demonstrate that the developed calibration technique significantly reduces the mean reprojection error by 0.45 pixels, rotation error by 65.08%, and distance error by 71.93% compared to the standard homography technique. Thus, the developed calibration technique promises the accurate transformation of low-resolution LiDAR points into camera pixels, thereby contributing to improved obstacle perception in intelligent autonomous driving systems. Full article
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18 pages, 6428 KiB  
Article
Connected Automated and Human-Driven Vehicle Mixed Traffic in Urban Freeway Interchanges: Safety Analysis and Design Assumptions
by Anna Granà, Salvatore Curto, Andrea Petralia and Tullio Giuffrè
Vehicles 2024, 6(2), 693-710; https://doi.org/10.3390/vehicles6020032 - 11 Apr 2024
Viewed by 684
Abstract
The introduction of connected automated vehicles (CAVs) on freeways raises significant challenges, particularly in interactions with human-driven vehicles, impacting traffic flow and safety. This study employs traffic microsimulation and surrogate safety assessment measures software to delve into CAV–human driver interactions, estimating potential conflicts. [...] Read more.
The introduction of connected automated vehicles (CAVs) on freeways raises significant challenges, particularly in interactions with human-driven vehicles, impacting traffic flow and safety. This study employs traffic microsimulation and surrogate safety assessment measures software to delve into CAV–human driver interactions, estimating potential conflicts. While previous research acknowledges that human drivers adjust their behavior when sharing the road with CAVs, the underlying reasons and the extent of associated risks are not fully understood yet. The study focuses on how CAV presence can diminish conflicts, employing surrogate safety measures and real-world mixed traffic data, and assesses the safety and performance of freeway interchange configurations in Italy and the US across diverse urban contexts. This research proposes tools for optimizing urban layouts to minimize conflicts in mixed traffic environments. Results reveal that adding auxiliary lanes enhances safety, particularly for CAVs and rear-end collisions. Along interchange ramps, an exclusive CAV stream performs similarly to human-driven ones in terms of longitudinal conflicts, but mixed traffic flows, consisting of both CAVs and human-driven vehicles, may result in more conflicts. Notably, when CAVs follow human-driven vehicles in near-identical conditions, more conflicts arise, emphasizing the complexity of CAV integration and the need for careful safety measures and roadway design considerations. Full article
(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives)
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27 pages, 6591 KiB  
Article
Enhancing Urban Intersection Efficiency: Utilizing Visible Light Communication and Learning-Driven Control for Improved Traffic Signal Performance
by Manuela Vieira, Manuel Augusto Vieira, Gonçalo Galvão, Paula Louro, Mário Véstias and Pedro Vieira
Vehicles 2024, 6(2), 666-692; https://doi.org/10.3390/vehicles6020031 - 4 Apr 2024
Viewed by 647
Abstract
This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, [...] Read more.
This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, and the accommodation of diverse traffic movements during multiple signal phases. The proposed system utilizes VLC to facilitate communication among interconnected vehicles and infrastructure. This is achieved by utilizing streetlights, headlamps, and traffic signals for transmitting information. By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established. A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively. Agents placed at each intersection control traffic lights by incorporating information from VLC-ready cars, including their positions, destinations, and intended routes. The agents devise optimal strategies to improve traffic flow and engage in communication to optimize the collective traffic performance. An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times. The reinforcement learning approach effectively schedules traffic signals, and the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios. The discussion emphasizes the possibility of applying reinforcement learning in everyday traffic scenarios, showcasing the potential for the dynamic identification of control actions and improved traffic management. Full article
(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives)
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15 pages, 5310 KiB  
Article
A New Strategy for Railway Bogie Frame Designing Combining Structural–Topological Optimization and Sensitivity Analysis
by Alessio Cascino, Enrico Meli and Andrea Rindi
Vehicles 2024, 6(2), 651-665; https://doi.org/10.3390/vehicles6020030 - 31 Mar 2024
Viewed by 863
Abstract
Rolling stock manufacturers are finding innovative structural solutions to improve the quality and reliability of railway vehicles components. Structural optimization processes represent an effective strategy for reducing manufacturing costs, resulting in geometries easier to design and produce. In this framework, the present paper [...] Read more.
Rolling stock manufacturers are finding innovative structural solutions to improve the quality and reliability of railway vehicles components. Structural optimization processes represent an effective strategy for reducing manufacturing costs, resulting in geometries easier to design and produce. In this framework, the present paper proposes a new methodology to design a railway metro bogie frame, combining structural–topological optimization methods and sensitivity analysis. In addition, manufacturing constraints were included to make the component design suitable for production through sand-casting. A robust sensitivity analysis has highlighted the most critical load conditions acting on the bogie frame. Its effectiveness was verified by carrying out two different structural optimizations based on different loadings. Two equivalent designs were obtained. Computational times were positively reduced by about 57%. The maximum value of stress was reduced about 23%. This new methodology has shown encouraging results to streamline the design process of this complex mechanical system, allowing researchers to also include manufacturing requirements. Full article
(This article belongs to the Special Issue Railway Vehicles and Infrastructure)
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19 pages, 8078 KiB  
Article
Evaluation of the Energy Equivalent Speed of Car Damage Using a Finite Element Model
by Paweł Droździel, Tomas Pasaulis, Robertas Pečeliūnas and Saugirdas Pukalskas
Vehicles 2024, 6(2), 632-650; https://doi.org/10.3390/vehicles6020029 - 30 Mar 2024
Viewed by 596
Abstract
To determine the speed of a vehicle in a collision with body deformation, the kinetic energy input of the vehicle to cause body damage must be estimated. This paper analyzes the methods for estimating the energy equivalent of vehicle damage. A finite element [...] Read more.
To determine the speed of a vehicle in a collision with body deformation, the kinetic energy input of the vehicle to cause body damage must be estimated. This paper analyzes the methods for estimating the energy equivalent of vehicle damage. A finite element model of a Toyota Yaris developed by the National Crash Analysis Center (NCAC) for use in the LS DYNA R.11.0.0 software environment is used for the simulation. The simulations include tests of the vehicle hitting a non-deformable wall, an object simulating a pole or a tree. The residual deformations obtained are used to determine the energy equivalent speed (EES) values using the “Crash 3—EBS Calculation 12.0” software and a visual comparison with the EES catalog database, where the EES parameter value is recalculated to take into account the difference in the mass of the vehicles. Full article
(This article belongs to the Special Issue Feature Papers on Advanced Vehicle Technologies)
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21 pages, 7005 KiB  
Article
An Estimation of the Energy Savings of a Mainline Diesel Locomotive Equipped with an Energy Storage Device
by Ievgen Riabov, Sergey Goolak and Larysa Neduzha
Vehicles 2024, 6(2), 611-631; https://doi.org/10.3390/vehicles6020028 - 29 Mar 2024
Viewed by 567
Abstract
The method of improving a two-section mainline diesel locomotive by using energy storage in the traction system is considered. A mathematical model was developed to study the movement of a diesel locomotive based on the recommendations and provisions of the theory of locomotive [...] Read more.
The method of improving a two-section mainline diesel locomotive by using energy storage in the traction system is considered. A mathematical model was developed to study the movement of a diesel locomotive based on the recommendations and provisions of the theory of locomotive traction. For this purpose, the movement of a diesel locomotive as part of a train along a given section of a track was studied. It was determined that the use of an energy storage device on a diesel locomotive will allow up to 64% of the energy spent on train traction to accumulate. The use of energy storage in the accumulator during electrodynamic braking ensured a reduction in fuel consumption by about 50%, regardless of the options for equipping the traction system of the diesel locomotive with an energy accumulator. It is established that regardless of the options for equipping the traction system of the diesel locomotive with an energy storage device, the indicators characterizing the degree of use of the diesel engine do not change. These research results can be used in works devoted to the improvement of the control system of energy exchange between the accumulator and traction engines of diesel locomotives. Full article
(This article belongs to the Special Issue Railway Vehicles and Infrastructure)
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21 pages, 522 KiB  
Article
Computing Safe Stop Trajectories for Autonomous Driving Utilizing Clustering and Parametric Optimization
by Johannes Langhorst, Kai Wah Chan, Christian Meerpohl and Christof Büskens
Vehicles 2024, 6(2), 590-610; https://doi.org/10.3390/vehicles6020027 - 24 Mar 2024
Viewed by 980
Abstract
In the realm of autonomous driving, ensuring a secure halt is imperative across diverse scenarios, ranging from routine stops at traffic lights to critical situations involving detected system boundaries of crucial modules. This article presents a novel methodology for swiftly calculating safe stop [...] Read more.
In the realm of autonomous driving, ensuring a secure halt is imperative across diverse scenarios, ranging from routine stops at traffic lights to critical situations involving detected system boundaries of crucial modules. This article presents a novel methodology for swiftly calculating safe stop trajectories. We utilize a clustering method to categorize lane shapes to assign encountered traffic situations at runtime to a set of precomputed resources. Among these resources, there are precalculated halt trajectories along representative lane centers that serve as parametrizations of the optimal control problem. At runtime, the current road settings are identified, and the respective precomputed trajectory is selected and then adjusted to fit the present situation. Here, the perceived lane center is considered a change in the parameters of the optimal control problem. Thus, techniques based on parametric sensitivity analysis can be employed, such as the low-cost feasibility correction. This approach covers a substantial number of lane shapes and exhibits a similar solution quality as a re-optimization to generate a trajectory while demanding only a fraction of the computation time. Full article
(This article belongs to the Special Issue Design and Control of Autonomous Driving Systems)
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