Different Classes Of Vehicles Research Articles

Coupled occupant-suspension-seat system analysis and vehicle-specific design optimization

A suspension seat is generally designed to achieve attenuation of low frequency whole-body vibration for a broad range of vehicles. Owing to the strong dependence of its vibration isolation performance on the nature of vibration excitation, an optimal performance could be realized via optimal tuning of suspension for specific classes of vehicles. In this study, a coupled occupant suspension seat model is developed and analyzed to identify optimal design parameters for different classes of vehicles. The multibody dynamic model of the air suspension seat is formulated considering kineto-dynamics of the suspension system and the elastic motion-limiters. The component models, namely, air spring with auxiliary air volume, hydraulic damper, and cushion, are identified from the laboratory-measured static and dynamic characteristics. A two-degree-of-freedom occupant model, is integrated to the suspension seat model to account for contribution due to occupant biodynamics. Model validity is examined using the laboratory-measured responses with a rigid load and a human occupant of similar body weight, and analyzed for different body weights and excitation spectra. The results suggest strong dependency of the suspension resonance frequency on nonlinear behavior of the air spring and the seated body mass. The model is subsequently employed to identify optimal coordinates of the air spring to achieve nearly constant natural frequency for a broad range of body masses. Furthermore, the model is used to identify optimal linkage and suspension design parameters with an objective to minimize the SEAT (Seat Effective Amplitude Transmissibility) for different spectral classes of earth moving machines defined in the ISO-7096 standard, while limiting the suspension travel. It is shown that the optimal coordinates of air spring could yield nearly constant natural frequency for different body weights, while damper could help limit the suspension travel, in addition to notable reductions in SEAT, ranging from 9% to 31% for different spectral classes of vehicles.

Scaling Effects on Morphing Structures: Preliminary Guidelines for Managing the Effects on a Case Study

The technique of morphing in aerospace engineering is a relatively new discipline targeting the improvement of aircraft performance, even through dramatic changes to some critical geometrical and mechanical features, to adapt aircrafts’ configurations to evolving operation conditions. The development path of morphing systems is complex and shall pass through articulated gates to prove its readiness level due to the concurrence of different disciplines and approaches. The characterization and demonstration of the concepts in a representative environment, such as wind tunnel test facilities, are some of the most relevant steps needed for the maturation of the engineering technique. The practical size limitations of test facilities usually impose the use of scaled models. In the case of morphing systems, whose architecture is strictly dependent on the available room, and whose performance is tightly correlated with the general structural stiffness, changes in dimensions may affect the overall behaviour significantly. Therefore, the adaptive design may change a lot until it arrives to the formation of completely different products. Transportability issues of certain architectural forms, as well as the different classes of vehicles, are also related to that aspect. The scope of this paper is to investigate the impact of some effects of scaling processes on certain features of a morphing system, particularly focusing on the stiffness parameters, for their impact on several features such as the load bearing capability and structural stability in both steady and dynamic conditions. As a case study, a rotorcraft blade segment integrated with torsional shape memory alloy (SMA) actuators was considered. Relevant numerical models were exploited to highlight the different evolution laws of the characteristic structural parameters vs. the referred scale factors. In this investigation, the axial, flap, lag bending, and torsion stiffnesses, as well as normal modes and stress levels, are considered. The achieved results confirm the complexity of attaining an effective reproduction of the targeted morphing architecture, as scaled configurations are considered. In spite of the unavoidable specificity of the analysis herein reported, it is believed that such attainments can have a general validity at least to some extent, and the outcomes may be exported to other morphing systems, at least as guidelines. This study took place within the European project SABRE (Shape Adaptive Blades for Rotorcraft Efficiency, H2020).

The Contact Phase in Vehicle–Pedestrian Accident Reconstruction

The need for continuous research to refine the models used in forensic accident reconstruction appears with the development of new car models that satisfy consumer complaints. This paper analyzed a sub-sequence of car and pedestrian accidents from the perspective of the distance traveled by them in the contact phase with the aim of improving the information regarding the reconstruction of road accidents. This research included the analysis of some real tests with pedestrian dummies, as well as simulations of the impact between different classes of vehicles and pedestrians in two different walking positions. Specialized software was used with complex multibody models of pedestrians, modifying the speed and deceleration parameters of the car at the time of the collision. For pedestrian characteristics, the friction coefficients of the ground, car and its mass were modified. The research results highlight the differences between the bilinear models used in accident reconstruction and the proposed study. They can also be used to determine the distance traveled by the vehicle in the first phase of a collision with pedestrians.

Fine-Grained Image Recognition by Means of Integrating Transformer Encoder Blocks in a Robust Single-Stage Object Detector

Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is both minimal and substantial intra-class variance. Current methods address the issue through first locating selective regions with region proposal networks (RPNs), object localization, or part localization, followed by implementing a CNN network or SVM classifier to those selective regions. This approach, however, makes the process simple via implementing a single-stage end-to-end feature encoded with a localization method, which leads to improved feature representations of individual tokens/regions through integrating the transformer encoder blocks into the Yolov5 backbone structure. These transformer encoder blocks, with their self-attention mechanism, effectively capture global dependencies and enable the model to learn relationships between distant regions. This improves the model’s ability to understand context and capture long-range spatial relationships in an image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative feature maps from transformer encoder blocks. We established the potential of the single-stage detector for the fine-grained image recognition task, achieving state-of-the-art 93.4% accuracy, as well as outperforming existing one-stage recognition models. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances.

Towards clean transportation systems: Infrastructure planning for EVs charging while driving

Charging while driving, or wireless charging lane (WCL), has been considered a promising solution to promote the widespread adoption of electric vehicles (EVs) for clean transportation systems. However, the WCL location problem is a strategic decision that costs a significant amount of money and greatly impacts network performance due to their interactions with traffic flow patterns. The inappropriate deployment of charging infrastructure may potentially cause more congestion, leading to more pollutants emitted from internal combustion vehicles. This paper proposes a decision-making framework to assist the system planner in finding the preferred optimal WCL location based on incorporating different stakeholders’ preferences, i.e. capital cost, congestion and environmental considerations. To capture the traffic evolution of different classes of vehicles (i.e. EVs and internal combustion vehicles), the travellers’ optimal time-dependent route choice is modelled using the multi-class dynamic traffic assignment. The proposed framework is transformed into a single-objective problem, i.e. minimizing total system cost, and solved efficiently by a meta-heuristic based on the cross-entropy method. The framework has been intensively tested to demonstrate the capability of tracking congestion propagation and energy consumption. Finally, the trade-offs between social welfare and the capital cost of WCL infrastructure are also investigated.

КОМПЛЕКСНАЯ МЕТОДИКА ВЫБОРА ПОДВИЖНОГО СОСТАВА ГОРОДСКОГО ПАССАЖИРСКОГО ТРАНСПОРТА

The paper discusses issues related to the development of a new methodology for determining the rational fleet of vehicles on the route to serve the city's population. The methodology is based on a questionnaire of passengers aimed at identifying preferences in choosing different classes of vehicles for carrying out transport movements in the city. Based on the identified patterns, a comprehensive methodology is proposed that allows the simultaneous use of different classes of rolling stock to meet the needs of different groups of passengers.

An Enhanced Cell Transmission Model for Multi-Class Signal Control

Existing multi-class cell transmission model (CTM) based methodologies for signal timing or traffic assignment may transfer prioritized transit vehicles from one cell to the next one before processing their preceding passenger cars. In addition, existing CTM-based methodologies process a proportion of a slow-moving transit vehicle in each time step. As such a portion of each transit vehicle remains in each cell and it never clears them. This paper presents constraints to project the position of transit vehicles based on the speed and cell occupancy variations between different classes of vehicles and incorporates them into the CTM. The resulting optimization program is a mixed-integer nonlinear problem. We used a distributed receding horizon control framework to solve it in real-time. The proposed formulation is executed in a simulated arterial street with four signalized intersections in Springfield, IL with different traffic volume levels and transit vehicle frequencies. The results showed that the proposed algorithm addressed the mentioned issues of the existing multi-class CTM, and yielded more efficient network performance than the conventional transit signal priority-based (CTSP) systems. The proposed formulation reduced average bus delay by 1% to 70% and car delay by 52% to 76% compared to CTSP.

Willans Line Bidirectional Power Flow Model for Energy Consumption of Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">A new and unique electric vehicle powertrain model based on bidirectional power flow for propel and regenerative brake power capture is developed and applied to production battery electric vehicles. The model is based on a Willans line model to relate power input from the battery and power output to tractive effort, with one set of parameters (marginal efficiency and an offset loss) for the bidirectional power flow through the powertrain. An electric accessory load is included for the propel, brake and idle phases of vehicle operation. In addition, regenerative brake energy capture is limited with a regen fraction (where the balance goes to friction braking), a power limit, and a low-speed cutoff limit. The purpose of the model is to predict energy consumption and range using only tractive effort based on EPA published road load and test mass (test car list data) and vehicle powertrain parameters derived from EPA reported unadjusted UDDS and HWFET energy consumption. The model can then be applied for other drive cycles, road grades, and parasitic accessory loads due to cabin or battery heating, air conditioning/cooling, or additional ADAS and autonomy loads (cameras and radar sensors, and compute and actuation hardware). The modeled battery net energy consumption is within 1% error for the UDDS and HWFET data used for fitting the powertrain parameters, as expected. The model is then applied to a vehicle that also has US06 data available, and predicts the US06 energy consumption to within 4%, validating the model parameterization method. The proposed modeling method is applied to a range of current and past EVs with very accurate results. A sensitivity study provides insight into the model parameters and similarities of electric powertrain efficiency among different classes of vehicles.</div></div>

CaT: CAVS Traversability Dataset for Off-Road Autonomous Driving

In the context of autonomous driving, the existing semantic segmentation concept strongly supports on-road driving where hard inter-class boundaries are enforced and objects can be categorized based on their visible structures with high confidence. Due to the well-structured nature of typical on-road scenes, current road extraction processes are largely successful and most types of vehicles are able to traverse through the area that is detected as road. However, the off-road driving domain has many additional uncertainties such as uneven terrain structure, positive and negative obstacles, ditches, quagmires, hidden objects, etc. making it very unstructured. Traversing through such unstructured area is constrained by a vehicle’s type and its capability. Therefore, an alternative approach to segmentation of the off-road driving trail is required that supports consideration of the vehicle type in a way that is not considered in state-of-the-art on-road segmentation approaches. To overcome this limitation and facilitate the path extraction in the off-road driving domain, we propose traversability concept and corresponding dataset which is based on the notion that the driving trails should be finely resolved into different sub-trails and areas corresponding to the capability of different vehicle classes in order to achieve safe traversal. Based on this, we consider three different classes of vehicles (sedan, pickup, and off-road) and label the images corresponding to the traversing capability of those vehicles. So the proposed dataset facilitates the segmentation of off-road driving trail into three regions based on the nature of the driving area and vehicle capability. We call this dataset as CaT (CAVS Traversability, where CAVS stands for Center for Advanced Vehicular Systems) dataset and is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.cavs.msstate.edu/resources/downloads/CaT/CaT.tar.gz</uri> .

Novel Approach for Design of Merging and Diverging Length at Toll Plazas: A Case Study

Traffic operations at toll plazas are influenced by the merging and diverging behavior of vehicles near the toll plaza in the flare area. Proper guidelines for the design of merging and diverging sections at toll plazas under mixed traffic conditions are not specified by the Indian Highway Capacity Manual, the Indian Road Congress, or the American Association of State Highway and Transportation Officials. Thus, the present study attempts to develop design criteria for diverging and merging lengths. The field data were collected using the performance-box instrument at Ghoti toll plaza located at National Highway No. 60 in India, operating under mixed traffic conditions. The concept of discretization of speed profile is proposed and applied to segregate sections of the zone of influence into ten equal parts each of 50 m length. The statistical test shows that speed has a significant difference between the sections and also in between the different classes of vehicle. Results of the speed-distance plot show that the second-degree polynomial function is the best fit for zone of deceleration and power function for the zone of acceleration. The study revealed that, for 50 km/h approach speed, the minimum diverging and merging distances should be 125.67–308.98 m and 47.93–294.23 m, respectively. The proposed guidelines for merging and diverging length are useful for the design of the flare area and to improve operational efficiency and level of service at toll plazas operating under mixed traffic conditions.

Prediction and modeling of traffic flow of human-driven vehicles at a signalized road intersection using artificial neural network model: A South African road transportation system scenario

In recent years, an increasing number of models and algorithms based on time series prediction and machine learning have been applied to traffic flow prediction, with good results achieved. However, few transportation researchers have been able to draw on any systematic research into the modeling of traffic flow at signalized road intersections. In this study, an artificial neural network (ANN) predictive model was developed using traffic flow variables from the South African Road transportation system as a case study. Four hundred and thirty-four (434) traffic datasets were obtained from seven road intersections connected to the N1 Allandale interchange (the busiest road in South Africa in terms of traffic volume) using sophisticated traffic data equipment such as inductive loop detectors, video cameras, and geographical positioning equipment. These traffic datasets were categorized into thirteen inputs and one output for developing the ANN model. These traffic data sets comprise of several different classes of vehicles, the speed of each category of vehicles on the road, traffic density, time, and traffic volume as input and output variables. The results obtained from this research study have shown an ANN model training and testing performance of 1.0000 and 0.99975. The evidence from this study suggests that the ANN predictive approach proposed could be used to predict and analyze traffic flow with a relatively high level of accuracy. Another significant evidence from this study suggests that the ANN model is an appropriate predictive model in modeling vehicular traffic flow at a signalized road intersection.

Determination of PCU values for mixed traffic conditions along the hilly road of East Sikkim

The road space in India is shared by different travel modes with different physical dimensions. On top of this, lane disciple is also not followed in India. All these factors influence the evaluation of traffic characteristics, roadway capacity, defining model for traffic flow and other transportation design factors. Passenger car unit (PCU) is a frequently used unit for calculating and expression of heterogeneous traffic volume to a simpler equivalent traffic unit where a passenger car is taken as a standard vehicle and all other classes of vehicle are expressed in terms of the standard car. For the present study, 11 km stretch of NH-10 in East Sikkim was taken into consideration. Data collection was done by video graphic survey. PCUs of different classes of vehicle are estimated using the speed area method by using traffic volume data. The PCU is calculated by multiplying the speed and area ratios of standard cars by the PCU of the subject vehicle type. The methodology adopted in this study is very much suitable for making proper PCU model under different conditions of heterogeneous traffic and road conditions of the study area..

ASSESSMENT OF STREET AIR POLLUTION THROUGH OSPM MODEL IN LAHORE, PAKISTAN

Traffic emissions are one of the main contributors in worsening the air quality of developing countries like Pakistan. The current research work is an effort to find out emission factor of different classes of vehicles as well as to model the certain air pollutants level on five different main streets of Lahore, Pakistan. The Town Hall air quality monitoring station at Mall road is taken as background station. The data of street configuration, background urban air pollution, meteorological conditions, diurnal traffic count and emission factor of vehicles was calculated to model the street air pollution. Operational Street Pollution Model (OSPM) was used to model the street pollutants. PM2.5 levels exceed National Environmental Quality Standards (NEQS) during all months of calendar year, while SO2 and NO2 exceed NEQS during entrance of heavy traffic at night in the city. Diesel vehicles have high SO2, NOx, PM2.5 and PM10 emission factors; while gasoline vehicles have high CO emission factors. Industrial region has relatively high level of SO2 emissions; while roadside have relatively high level of CO emissions. A significant correlation (rvalue > 0.5) was observed between modeled and observed results for all the streets for the NOx, SO2 and CO levels, except for NOx level at Gulberg (r-value= 0.42). The better modeling results have been observed for those streets which are relatively polluted spots and are close to the background station.

Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis

The rapid advancement in the field of deep learning and high performance computing has highly augmented the scope of video-based vehicle counting system. In this paper, the authors deploy several state-of-the-art object detection and tracking algorithms to detect and track different classes of vehicles in their regions of interest (ROI). The goal of correctly detecting and tracking vehicles’ in their ROI is to obtain an accurate vehicle count. Multiple combinations of object detection models coupled with different tracking systems are applied to access the best vehicle counting framework. The models’ addresses challenges associated to different weather conditions, occlusion and low-light settings and efficiently extracts vehicle information and trajectories through its computationally rich training and feedback cycles. The automatic vehicle counts resulting from all the model combinations are validated and compared against the manually counted ground truths of over 9 h’ traffic video data obtained from the Louisiana Department of Transportation and Development. Experimental results demonstrate that the combination of CenterNet and Deep SORT, and YOLOv4 and Deep SORT produced the best overall counting percentage for all vehicles.

The Sustainability of Concessioning Itobe-Ajaokuta Expressway to the Private Sector

The study focused on the Sustainability of Concessioning of Itobe-Ajaokuta Expressway to the private sector. The methodology adopted was the traffic volume count for 30 days, which enabled me to calculate the number of vehicles for each of the six different classes of vehicle plying the road daily. The study found that a total of 78,690 vehicles use the road daily. The vehicles were categorised into six (6) different groups, and tolls to be paid allocated to each of them. From the research carried out, a total of One hundred and thirty four million, four hundred and eighty thousand, six hundred naira (#134, 480, 600), could be realised from toll charges annually. It was also found that the road is 5.05Km long and that it would cost the sum of Six hundred and eight million, four hundred and fifteen thousand, nine hundred and twenty naira (#608,415,920), to construct the road. The key objective was to analyse whether concessioning the expressway to the private sector is sustainable, that is whether the toll charges will be able to construct and maintain the road within the concession period of thirty years. The study recommends that Kogi State government could approach the African Development Bank for loan to construct this very critical road in order to alleviate the sufferings of the Nigerian people plying the road. The study concludes that the Ajaokuta-Itobe Expressway concession project is a viable and sustainable project, and that it will increase the welfare of the people, and directly or indirectly improve the standard of living of the people, and that rehabilitating the 5.05km of Itobe-Ajaokuta expressway will reduce environmental pollution, vehicle operating costs and travel time on the road thereby promoting economic development through improved transport services.

Categorizing Merging and Diverging Strategies of Truck Drivers at Motorway Ramps and Weaving Sections using a Trajectory Dataset

Lane-changing models are essential components for microscopic simulation. Although the literature recognizes that different classes of vehicles have different ways of performing lane-change maneuvers, lane change behavior of truck drivers is an overlooked research area. We propose that truck drivers are heterogeneous in their lane change behavior too and that inter-driver differences within truck drivers exist. We explore lane changing behavior of truck drivers using a trajectory data set collected around motorway bottlenecks in the Netherlands which include on-ramp, off-ramp, and weaving sections. Finite mixture models are used to categorize truck drivers with respect to their merging and diverging maneuvers. Indicator variables include spatial, temporal, kinematic, and gap acceptance characteristics of lane-changing maneuvers. The results suggest that truck drivers can be categorized into two and three categories with respect to their merging and diverging behaviors, respectively. The majority of truck drivers show a tendency to merge or diverge at the earliest possible opportunity; this type of behavior leads to most of the lane change activity at the beginning of motorway bottlenecks, thus contributing to the raised level of turbulence. By incorporating heterogeneity within the lane-changing component, the accuracy and realism of existing microscopic simulation packages can be improved for traffic and safety-related assessments.

Multisized Object Detection Using Spaceborne Optical Imagery

This article addresses the highly challenging problem of vehicle detection from high-resolution remote sensing imagery by introducing a novel medium size annotated dataset named satellite imagery multivehicles dataset (SIMD) along with an adapted single pass deep multiscale object detection framework with the aim to detect multisized/type objects for catering above-ground perspective of vehicles. The dataset images are acquired from multiple locations in the EU/US regions available in the public Google Earth satellite imagery. Specifically, it comprises 5000 images of resolution 1024 × 768 and collectively contains 45 096 objects in 15 different classes of vehicles including cars, trucks, buses, long vehicles, various types of aircrafts, and boats. In the proposed architecture, we demonstrate the relevant modifications needed to translate the state-of-the-art object detection frameworks to solve the object detection problem from remote sensing imagery. The proposed architecture has been evaluated on SIMD and a public dataset VEDAI. The comparative analysis has been performed with existing off-the-shelf single-shot object detection models including YOLO and YOLT yielding superior performance measured with standard evaluation strategies. To ignite further research in this domain, the introduced SIMD dataset and the corresponding architecture is publicly available at this link: http://vision.seecs.edu.pk/simd .

Vehicle Speed and Length Estimation Errors Using the Intelligent Transportation System with a Set of Anisotropic Magneto-Resistive (AMR) Sensors.

Seeking an effective method for estimating the speed and length of a car is still a challenge for engineers and scientists who work on intelligent transportation systems. This paper focuses on a self-developed system equipped with four anisotropic magneto-resistive (AMR) sensors which are placed on a road lane. The piezoelectric polyvinylidene fluoride (PVDF) sensors are also mounted and used as a reference device. The methods applied in the research are well-known: the fixed threshold-based method and the adaptive two-extreme-peak detection method. However, the improved accuracy of estimating the length by using one of the methods, which is based on computing the difference quotient of a time-discrete signal (representing the changes in the magnitude of the magnetic field of the Earth), is observed. The obtained results, i.e., the speed and length of a vehicle, are presented for various values of the increment Δn used in numerical differentiation of magnetic field magnitude data. The results were achieved in real traffic conditions after analyzing a data set M = 290 of vehicle signatures. The accuracy was evaluated by calculating MAE (Mean Absolute Error), RMSE (Root Mean Squared Error) for different classes of vehicles. The MAE is within the range of 0.52 m–1.18 m when using the appropriate calibration factor. The results are dependent on the distance between sensors, the speed of vehicle and the signal processing method applied.

Automatic Vehicle Classification using Roadside LiDAR Data

This research presented a new approach for vehicle classification using roadside LiDAR sensor. Six features (one feature, object height profile, contains 10 sub-features) extracted from the vehicle trajectories were applied to distinguish different classes of vehicles. The vehicle classification aims to assign the objects into ten different types defined by FHWA. A database containing 1,056 manually marked samples and their corresponding pictures was provided for analysis. Those samples were collected at different scenarios (roads and intersections, different speed limits, day and night, different distance to LiDAR, etc.). Naïve Bayes, K-nearest neighbor classification, random forest (RF), and support vector machine were applied for vehicle classification. The results showed that the performance of different methods varied by class. RF has the highest overall accuracy among those investigated methods. Some types were merged together to serve different types of users, which can also improve the accuracy of vehicle classification. The validation indicated that the distance between the object and the roadside LiDAR can influence the accuracy. This research also provided the distribution of the overall accuracy of RF along the distance to LiDAR. For the VLP-16 LiDAR, to achieve an accuracy of 91.98%, the distance between the object and LiDAR should be less than 30 ft. Users can set up the location of the roadside LiDAR based on their own requirements of the classification accuracy.

Non-local multi-class traffic flow models

We prove the existence for small times of weak solutions for a class of non-local systems in one space dimension, arising in traffic modeling. We approximate the problem by a Godunov type numerical scheme and we provide uniform L ∞ and BV estimates for the sequence of approximate solutions. We finally present some numerical simulations illustrating the behavior of different classes of vehicles and we analyze two cost functionals measuring the dependence of congestion on traffic composition.