Vehicles In Scenarios Research Articles

Thermo-mechanical performance of 3D-printed TC4 hierarchical lattice-truss-core sandwich structures in high temperature conditions

Hierarchical lattice-truss-core sandwich structures have attracted increasing attention by virtue of superior performance in both mechanical properties and lightweight hierarchy topology. However, little work has been reported about the thermo-mechanical responses of such emerging structures. In this research, the effects of high temperature on the out-of-plane compressive properties and failure mechanisms of 3D-printed hierarchical lattice structures are experimentally and numerically investigated. Based on a single-stage pyramidal sandwich structure (P), two kinds of sandwich structures consisting of hierarchical pyramidal truss cores are designed: pyramidal-hollow (PH) and pyramidal-cylinder (PC), all kinds of the structures are integrally fabricated by 3D printing technique of titanium alloy (TC4). The thermo-mechanical coupling and responses during compression are experimentally evaluated from 25 °C to a high temperature of 350 °C. The relevant numerical simulations are carried out by finite element analysis (FEA), which are in well agreement with experimental measurements. It is evident that, compared with the single-stage lattice plate with a same weight, the 3D printed PC-1 type hierarchical configuration has a 131.93% increment in the internal heat-transfer area. Meanwhile, it has a 19.31% and a 26.58% increment in bearing capacity at room and high temperature, respectively. The superior integrated load-bearing and heat shielding efficiency are elucidated through the systematical analysis of the influence of hierarchical design on detrimental failure modes under high temperature: buckling and mixed buckling-plastic fracture of the basic and secondary hierarchy. This work could provide a fundamental knowledge for the future design of lightweight structures with thermo-mechanical superiority in high temperature conditions, such as hypersonic flight vehicles scenarios.

Joint Task Offloading and Resource Allocation for Multi-Access Edge Computing Assisted by Parked and Moving Vehicles

In the Internet of Vehicles (IoV) scenarios, vehicles are equipped with computing resources to support vehicle-oriented IoV applications. Meanwhile, these computing resources can be also leveraged to enhance the task processing performance for the devices in Multi-access Edge Computing (MEC) scenarios, and alleviate the load of edge servers. Existing research works rarely consider the resource utilization of moving vehicles, which can be an important complement to the MEC schemes with the assistance of parked vehicles. In this paper, a joint task offloading and resource allocation scheme is proposed for a parked-and-moving-vehicles-assisted MEC scenario consisting of multiple devices, parked vehicles, and moving vehicles covered by a Base Station (BS) equipped with an edge server. The tasks of the devices can be either offloaded to the BS or further offloaded from the BS to the vehicles. The service time that a moving vehicle can provide its task offloading service before it moves out of the coverage of the BS, are taken into account of our system model. The aim of our scheme it to minimize the total priority-weighted task processing delay for all the devices through offloading the tasks to the edge server or to the vehicles, allocating the wireless channels of the BS, and allocating the computing resource of the edge server and the vehicles. A generalized benders decomposition and reformulation linearization-based iterative algorithm is designed to obtain the optimal solution to the optimization problem, and a two-stage heuristic algorithm is also given to provide near-optimal solutions with low computational complexity. The simulation results demonstrate the superiority of our scheme in seven different scenarios by comparing it with three other schemes.

Combined Estimation of the Mutual Position of the Master and Slave Vehicle by Ultrasonic Receivers and Gnss-Module Data

The problem of determining the position of master vehicle when implementing a scenario of robotic vehicle as a part of caravanning driving system is studied. The mentioned scenario is expected the slave vehicle to replicate the motion trajectory of master vehicle represented by human operated vehicle, by another robotic vehicle or by human operator. To provide reliable operation of robotic vehicle within autonomous convoy a reliable and precise assessment of master vehicle position and trajectory regardless of environment is required. Implementation of ultrasonic receivers and emitters within information and measurement complex to determine master-slave vehicle mutual position allows to function at any time of the day inside and outside the premises in the presence of dynamic obstacles, smoke, precipitation, etc. Estimation of position and trajectory of master vehicle is accomplished by means of measuring arrival time of ultrasonic wave emitted by active beacon prior to spatial array of ultrasonic receivers. Similar indirect distance measurement between bacon and each receiver allowed to receive a system of equations to be solved by implementation of Kalman filter with Raugh - Tung - Striebel smoother. Major disadvantage of the approach is 20-meter range, which is enough for complex maneuvering. To increase the range of the complex operation, a combined estimation of the master's position based on measuring data of the spatial array of ultrasonic receivers and the module of global navigation satellite system is proposed.

Operational speed strategy opportunities for autonomous trucking on highways

This study examines the potential for cost and carbon emission reduction for autonomous trucks to adopt lower peak speeds than today's human driven trucks, where the hourly cost of the driver remains a significant trade-off. Adopting lower speeds presents an indirect savings opportunity in addition to savings associated with the removal of the driver. Benefits and trade-offs for varying target speed of autonomous trucks on highways were investigated with a parameterised analysis of transport and cargo cost components. A validated fuel consumption model was used to compare fuel consumption for different speed strategies across idealised highway drive-cycles and vehicle scenarios. A volume-limited tractor-trailer (50% cargo mass capacity) could reduce fuel consumption and carbon emissions by 26% when reducing target speed from 90 km/h to 70 km/h. When the trade-offs of driver, vehicle and cargo time are considered for the same 20 km/h target speed reduction, combined costs increase by 3% for the human driver scenario, but decrease by 4% for an autonomous truck (assuming medium cargo time value). Sensitivity to vehicle type, loading, drive-cycle and cargo value was examined. A UK supermarket chain case study was investigated and key differences between a UK and US context were compared. Mandatory rest breaks and Hours-of-Service restrictions were identified as additional barriers for human driven vehicles to adopt slower speed strategies.

Reconfigurable Intelligent Surface Assisted Multi-Carrier Wireless Systems for Doubly Selective High-Mobility Ricean Channels

Reconfigurable intelligent surfaces (RIS) constitute a revolutionary technique of beneficially reconfiguring the smart radio environment. However, despite the fact that wireless propagation is of time-varying nature, most of the existing RIS contributions focus on time-invariant scenarios for the following reasons. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Firstly</i> , it becomes impractical to instantaneously feed back the control signal based on the doubly selective non-line-of-sight (NLoS) fading scenario. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Secondly</i> , channel estimation conceived for the high-mobility and high-dimensional RIS-assisted links has to take into account the spatial-domain (SD), time-domain (TD), and frequency-domain (FD) correlations imposed by the angle-of-arrival/departure (AoA/AoD), the Doppler and the orthogonal frequency-division multiplexing (OFDM) operations, respectively, where none of the existing solutions can be directly applied. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Thirdly</i> , it is far from trivial to maximize the NLoS channel powers on all subcarriers by a common set of RIS reflecting coefficients. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fourthly</i> , in the face of double selectivity, it becomes inevitable to encounter either inter-symbol interference (ISI) or inter-channel interference (ICI) during the signal detection in the TD or in the FD, respectively. Against this background, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">firstly</i> , we focus our attention on line-of-sight (LoS) dominated unmanned aerial vehicle (UAV) scenarios. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Secondly</i> , we conceive new minimum mean squared error (MMSE) channel estimation methods for doubly selective fading, which perodically transmit pilot symbols embedded into the TD and FD over the SD in order to beneficially exploit the correlations in the three domains. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Thirdly</i> , the RIS coefficients are optimized by a low-complexity algorithm based on the LoS representation of the end-to-end system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fourthly</i> , tailor-made interference cancallation techniques are devised for improving the signal detection both in the FD and in the TD. Our simulation results are examined in six frequency bands licensed in 5 G, which confirms that the employment of RIS is capable of achieving substantial performance improvements.

The impact of different AR-HUD virtual warning interfaces on the takeover performance and visual characteristics of autonomous vehicles

Objective The objective of this study was to determine the different effects of the arrow-pointing augmented reality head-up display (AR-HUD) interface, virtual shadow AR-HUD interface, and non-AR-HUD interface on autonomous vehicle takeover efficiency and driver eye movement characteristics in different driving scenarios. Methods Thirty-six participants were selected to carry out a simulated driving experiment, and the eye movement index and takeover time were analyzed. Results The arrow pointing AR-HUD interface and the virtual shadow AR-HUD interface could effectively reduce the driver's visual distraction, improve the efficiency of obtaining visual information, reduce the number of times the driver's eyes leave the road, and improve the efficiency of the takeover compared with the non-AR-HUD interface, but there was no significant difference in eye movement indexes between the arrow pointing AR-HUD interface and the more eye-catching virtual shadow AR-HUD interface. When specific scenarios were considered, it was found that in the scenario of emergency braking of the vehicle in front, the arrow pointing AR-HUD interface and the virtual shadow AR-HUD interface had more advantages in takeover efficiency than the non-AR-HUD interface. However, in the scenarios of a rear vehicle overtaking the vehicle ahead and non-motor vehicles running red lights, there was no significant difference in takeover efficiency. For the non-motor vehicle invading the line, emergency U-turn of the vehicle in front, and pedestrian crossing scenarios, the virtual shadow AR-HUD interface had the highest takeover efficiency. Conclusions These research results can help improve the active safety of autonomous vehicle AR-HUD interfaces.

Mechanism of Road Capacity under Different Penetration Scenarios of Autonomous Vehicles

Mechanism of Road Capacity under Different Penetration Scenarios of Autonomous Vehicles

Learning Adaptive Spatial-Temporal Context-Aware Correlation Filters for UAV Tracking

Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of target-tracking tasks. Different from the target-tracking task in the general scenarios, the target-tracking task in the UAV scenarios is very challenging because of factors such as small scale and aerial view. Although the discriminative correlation filter (DCF)-based tracker has achieved good results in tracking tasks in general scenarios, the boundary effect caused by the dense sampling method will reduce the tracking accuracy, especially in UAV-tracking scenarios. In this work, we propose learning an adaptive spatial-temporal context-aware (ASTCA) model in the DCF-based tracking framework to improve the tracking accuracy and reduce the influence of boundary effect, thereby enabling our tracker to more appropriately handle UAV-tracking tasks. Specifically, our ASTCA model can learn a spatial-temporal context weight, which can precisely distinguish the target and background in the UAV-tracking scenarios. Besides, considering the small target scale and the aerial view in UAV-tracking scenarios, our ASTCA model incorporates spatial context information within the DCF-based tracker, which could effectively alleviate background interference. Extensive experiments demonstrate that our ASTCA method performs favorably against state-of-the-art tracking methods on some standard UAV datasets.

Performance Evaluation of VANET Routing Protocols in Madinah City

Traffic management challenges in peak seasons for popular destinations such as Madinah city have accelerated the need for and introduction of autonomous vehicles and Vehicular ad hoc networks (VANETs) to assist in communication and alleviation of traffic congestions. The primary goal of this study is to evaluate the performance of communication routing protocols in VANETs between autonomous and human-driven vehicles in Madinah city in varying traffic conditions. A simulation of assorted traffic distributions and densities were modeled in an extracted map of Madinah city and then tested in two application scenarios with three ad hoc routing protocols using a combination of traffic and network simulation tools working in tandem. The results measured for the average trip time show that opting for a fully autonomous vehicle scenario reduces the trip time of vehicles by approximately 7.1% in high traffic densities and that the reactive ad hoc routing protocols induce the least delay for network packets to reach neighboring VANET vehicles. From these observations, it can be asserted that autonomous vehicles provide a significant reduction in travel time and that either of the two reactive ad hoc routing protocols could be implemented for the VANET implementation in Madinah city. Furthermore, we perform an ANOVA test to examine the effects of the factors that are considered in our study on the variation of the results.

Large-scale scenarios of electric vehicle charging with a data-driven model of control

Transportation electrification is forecast to bring millions of new electric vehicles to roads worldwide this decade. Planning to support those vehicles depends on detailed scenarios of their electricity demand in both uncontrolled and controlled or smart charging scenarios. In this paper, we present a novel modeling approach to enable rapid generation of demand estimates that represent the impact of controlled charging for large-scale scenarios with millions of individual drivers. To model the effect of load modulation control on aggregate charging profiles, we propose a novel machine learning approach that replaces traditional optimization approaches. We demonstrate its performance modeling workplace charging control under a range of electricity rate schedules, achieving small errors (2.5%–4.5%) while accelerating computations by more than 4000 times. To generate the uncontrolled charging demand for scenarios with residential, workplace, and public charging we use statistical representations of a large data set of real charging sessions. We demonstrate the methodology by generating diverse sets of scenarios for California's charging demand in 2030 which consider multiple charging segments and controls, each run locally in under 50 s. We further demonstrate support for rate design by modeling the large-scale impact of a new, custom rate schedule for workplace charging.

Optimal probabilistic reliability-oriented planning of islanded microgrids considering hydrogen-based storage systems, hydrogen vehicles, and electric vehicles under various climatic conditions

Much attention has been paid to the deployment of Hydrogen storage systems (HSSs) and Hydrogen vehicles (HVs) in the modernized energy system. However, a research gap exists in the literature about optimal probabilistic planning of microgrids (MGs) equipped with HSS, considering the uncertainties of renewable energy resources and electric vehicle (EV) and HV owners' behaviors. The main purpose of this research is to fill such a gap by developing a new probabilistic optimization problem to determine the capacity of Hydrogen-based MGs' sub-systems. Another contribution is to consider the reliability constraints and loss of energy cost (LOEC) in the MGs' total net present cost (TNPC). The Monte Carlo simulation (MCS) and Flower Pollination Algorithm (FPA) are used to model stochastic behaviors and solve the proposed probabilistic optimization problem. This paper studies different actual climates of Iran based on historical data, while various coordinated/uncoordinated charging modes of EVs and HVs are examined. Test results infer that a significant inaccuracy (more than 4.66% depends on the climate conditions and vehicle scenarios) occurs due to neglecting the uncertainties. The sensitivity analyses imply that the reliability constraints, LOEC, and their interactions might affect the MGs’ optimal design.

New Energy Vehicle Battery SOC Evaluation Method based on Robust Extended Kalman Filterd

In actual charging/discharging scenarios of new energy vehicles, low temperature, natural disasters, extreme weather, electromagnetic radiation and other factors will cause strong noise interference in SOC evaluation. Aiming at the problem of SOC evaluation accuracy in extreme environments, a power battery SOC evaluation method based on robust Kalman filter is proposed. The experimental results prove that the method is suitable for the accurate assessment of SOC in the charging/discharging process of the power battery of new energy vehicles under the interference of complex and harsh environment noise, and it has good robustness.

Efficient IoT-Based Formal Model for Vehicle-Life Interaction in VANETs Using VDM-SL

VANETs have gained much attention from both industry and academia because of their characteristics, such as dynamic topology. There are various applications of VANETs that are classified on the basis of safety, efficiency, commercial usage, and productive areas. This paper presents an IoT-based formal model for vehicle-life integration enabling RSUs with the help of different approaches. We have developed a model that uses vehicle scenarios in smart transportation systems so that quick data transmission is provided between the source and destination vehicles. Further, fog-based RSUs provide a wide range to communicate with hospitals and emergency vehicles to deal with emergency situations. All the appropriate entities are connected to ensure a consistent traffic flow for the arrival of an emergency vehicle in emergency places. The UML, graph theory, and VDM-SL formal technique are used to represent this system. To model the network and discover appropriate paths for V2V communication, graph theory is applied. The system requirements are designed using a UML diagram. The VDM-SL, an object-oriented model-based formal technique, was utilized for this modeling procedure. This approach assures the safety and accuracy of systems by detecting flaws early in the design process. It also gives an exceptionally important answer to an issue and increases trust in the software’s quality.

System Optimization of Shared Mobility in Suburban Contexts

Shared mobility is a viable choice to improve the connectivity of lower-density neighbourhoods or suburbs that lack high-frequency public transportation services. In addition, its integration with new forms of powertrain and autonomous technologies can achieve more sustainable and efficient transportation. This study compares four shared-mobility technologies in suburban areas: the Internal Combustion Engine, Battery Electric, and two Autonomous Electric Vehicle scenarios, for various passenger capacities ranging from three to fifteen. The study aims to provide policymakers, transportation planners, and transit providers with insights into the potential costs and benefits as well as system configurations of shared mobility in a suburban context. A vehicle routing problem with time windows was applied using the J-Horizon software to optimize the costs of serving existing intra-community demand. The results indicate a similar fleet composition for Battery Electric and Autonomous Electric fleets. Furthermore, the resulting fleet for all four technologies is dominated by larger vehicle capacities. Due to the large share of driver cost in the total cost, the savings using a fleet of Autonomous Electric Vehicles are predicted to be 68% and 70%, respectively, compared to Internal Combustion and Battery Electric fleets.

Front vehicle detection based on improved fusion method for lidar and visual image

In order to ensure the detection accuracy, an improved adaptive weighted (IAW) method is proposed in this paper to fuse the data of images and lidar sensors for the vehicle object’s detection. Firstly, the IAW method is proposed in this paper and the first simulation is conducted. The unification of two sensors’ time and space should be completed at first. The traditional adaptive weighted average method (AWA) will amplify the noise in the fusion process, so the data filtered with Kalman Filter (KF) algorithm instead of with the AWA method. The proposed IAW method is compared with the AWA method and the Distributed Weighted fusion KF algorithm in the data fusion simulation to verify the superiority of the proposed algorithm. Secondly, the second simulation is conducted to verify the robustness and accuracy of the IAW algorithm. In the two experimental scenarios of sparse and dense vehicles, the vehicle detection based on image and lidar is completed, respectively. The detection data is correlated and merged through the IAW method, and the results show that the IAW method can correctly associate and fuse the data of the two sensors. Finally, the real vehicle test of object vehicle detection in different environments is carried out. The IAW method, the KF algorithm, and the Distributed Weighted fusion KF algorithm are used to complete the target vehicle detection in the real vehicle, respectively. The advantages of the two sensors can give full play, and the misdetection of the target objects can be reduced with proposed method. It has great potential in the application of object acquisition.

An Adaptive Route Planning Method of Connected Vehicles for Improving the Transport Efficiency

In recent years, the route-planning problem has gained increased interest due to the development of intelligent transportation systems (ITSs) and increasing traffic congestion especially in urban areas. An independent route-planning strategy for each in-vehicle terminal improves its individual travel efficiency. However, individual optimal routes pursue the maximization of individual benefit and may contradict the global benefit, thereby reducing the overall transport efficiency of the road network. To improve traffic efficiency while considering the travel time of individual vehicles, we propose a new dynamic route-planning method by innovatively introducing a bidding mechanism in the connected vehicle scenario for the first time. First, a novel bidding-based dynamic route planning is proposed to formulate vehicle routing schemes for vehicles affected by congestion via the bidding process. Correspondingly, a bidding price incorporating individual and global travel times was designed to balance the travel benefits of both objectives. Then, in the bidding process, a new local search algorithm was designed to select the winning routing scheme set with the minimum bidding price. Finally, the proposed method was tested and validated through case studies of simulated and actual driving scenarios to demonstrate that the bidding mechanism would be conducive to improving the transport efficiency of road networks in large-scale traffic flow scenarios. This study positively contributes to the research and development of traffic management in ITSs.

ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ С ПОМОЩЬЮ ВИРТУАЛЬНОГО ОКРУЖЕНИЯ РЕЖИМОВ УПРАВЛЕНИЯ ЛЕТАЮЩИМИ АППАРАТАМИ НА ЛУННОЙ СТАНЦИИ ДЛЯ ИЗУЧЕНИЯ ОПЕРАТОРСКОЙ ДЕЯТЕЛЬНОСТИ В ИЗОЛЯЦИОННЫХ ЭКСПЕРИМЕНТАХ

The survey is focused on the applicability of virtual reality(VR) and 3D immersive technologies for isolation studies of the human-robot interaction during intravehicular activities (IVA) and scenarios of pilotless vehicles (PVs) operations on a lunar station. The investigation resulted in covering the prototyped multicopter drones for inspecting closed environments on Earth and future creation of appropriate VR models, and proposing off-line PVs analogous to the NASA Free-Flyers Units Astrobee, and VirSim system for use on the lunar station. The imitative VR models and suggested research tasks include the following: 1) PVs scenarios, routing and scheduling, 2) modeling autonomous navigation and control (local positioning and navigation) inside the station, 3) manual piloting, 4) virtual and real control panels, 5) comparison of displays representing PVs cameras stream, 2D-electronic maps and real-time 3D-image, 6) PVs scenarios for potential danger to the lunar station, and support of response to unexpected events. Knowledge from the isolation studies can substantiate guidelines on PVs operations aboard habitable lunar stations.

Nonlinear Traffic Data Reconstruction in Large-Scale Internet of Vehicle Systems: A Neural Network Approach

Multi-Access Edge Computing (MEC) can reduce transmission costs and provide faster interactive responses in cloud server-centric Internet of Vehicle (IoV) scenarios. However, the resource space of MEC server is limited, and the large amount of data uploaded by RSU may limit the further development of MEC network. Compressed Sensing (CS) proves that for the data with certain internal structure, a complete data set can be reconstructed even if the sampling frequency is lower than the requirements of the sampling theorem. In the current works, traffic data compression is accomplished by directly reducing the sampling frequency in the process of collecting traffic data. However, CS reconstruction only utilizes the linear characteristics of data. Aiming to fully utilize the nonlinear characteristics of data, we propose a neural network based traffic data reconstruction model framework, called NRTD. NRTD instantiates the process of generalized matrix factorization and combines the potential features of multilayer perceptrons to improve the fitting degree of the model to the traffic flow data change curve. Parameter optimization and model comparison experiments are carried out with different missing rates and missing scenes. The experimental results show that the proposed method is superior to the conventional compressed sensing reconstruction model before improvement under different conditions.

Safe Driving Model Based on V2V Vehicle Communication

Along with the rapid development of connected vehicle communication technology, describing the vehicle following driving status becomes gradually complicated. Driver behavior, vehicle type, and road factors affect vehicle speed, and the following distance reflects variability. In this paper, a nonlinear following distance model is constructed to characterize this variability. The model is based on the full speed difference model (FVD), and introduces the headway time distance coefficient, the following vehicle type coefficient, the communication advance response parameter reflecting the driver’s personal characteristics, and the slope coefficient and curve curvature coefficient reflecting the road conditions, etc., and analyzes to obtain the stability conditions of the model. MATLAB is applied to numerical simulation experiments of the model, and the results show that the model can better describe the variability of following headway due to driver attributes, vehicle type, slope and curve in the connected vehicle scenario, thus providing a reference for traffic flow control and management.

Sustainable Renewal of Underground Parking Space in the Scenario of Shared Autonomous Vehicles

Shared autonomous vehicles (SAVs) are a major development direction in international scientific and technological innovation. One of the most popular features of SAVs in the urban space is that they can significantly reduce the need for parking. The urban underground parking space (UPS) is currently the largest static traffic space, especially in high-density urban centers. Under the SAV scenario, the need for the renewal of UPS will increase in the near future. However, renewal of the UPS is difficult due to its special form features, which are greatly restricted by the external environment, thus necessitating targeted methods and strategies. This research first conducted field investigations and data collection on the spatial morphology and service conditions of typical UPSs in different areas of Hangzhou city. Based on the driver status response and the multi-objective attribute models, the time-series evaluation method and function replacement decision model for the sustainable renewal of underground parking were established. The research also discusses appropriate design strategies for the combination of spatial characteristics and functional replacement goals of typical samples. The conclusions will provide scientific guidance for the future design practice of architects and urban designers in SAV.