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• A comprehensive vertical dynamics model of the electric vehicle considering the integrated powertrain system is proposed. • The limitations of traditional vertical dynamics model of electric vehicle in the high frequency range are revealed. • The effects of mounting stiffness and damping on the vibration of chassis and integrated powertrain system are explored. • A multi-objective optimization model is designed to minimize the vibration of chassis and integrated powertrain system. Integrated powertrain system, mainly composed of driving motor and reducer, is one of the excitation sources to electric vehicle vibration but is limited in coupling with the vertical vehicle dynamic in the existing research, which restricts the reliability of dynamics investigation of vehicle and integrated powertrain system. This work proposes a novel vertical dynamic model of the electric vehicle considering the integrated powertrain system, enabling the way to access the coupled vibration among the chassis, unsprung mass, and integrated powertrain system. The road irregularity, unbalanced magnetic pull, and gear mesh excitation are developed and are further applied to the novel vehicle dynamic model with ten degrees of freedom (DOFs). Then, the dynamic response between the novel model and traditional one is compared. Further, the effects of the stiffness and damping of suspension and mounting on the vibration of chassis and integrated powertrain system are explored by numerical calculation. Finally, a multi-objective optimization model is proposed and is driven by the dragonfly algorithm to minimize the vibration of chassis and integrated powertrain system. The research results shown that there will be present the new complex dynamic characteristics with considering the excitation from the integrated powertrain system. The suspension and mounting parameters have the nonlinear effects on the vehicle vibration. And the system vibration can be globally optimized with the optimization algorithm. [ABSTRACT FROM AUTHOR]

The accelerated adoption of Electric Vehicles (EVs) necessitates innovative and effective pricing strategies for charging infrastructure. This study leverages the MATSim (Multi-Agent Transport Simulation) framework to meticulously evaluate the performance of two distinctive EV charging pricing models: time-based charging and a hybrid model integrating both usage-based and time-based components. Driven by the pivotal question of how EV charging should be optimally priced—whether contingent on energy consumption or charging duration—the research endeavors to conduct two simulations. These simulations aim to provide a comprehensive comparative analysis, evaluating metrics such as total power derived from grids, utility revenue, charging station queues and served vehicles. In the time-based charging scenario, EVs incur charges based on their plugged-in duration, reflecting a pricing approach that correlates directly with the time a vehicle remains connected to the charging station. In contrast, in the hybrid model, EVs undergo initial billing based on usage until a predetermined battery charge point is reached, such as achieving a full battery. Subsequently, time-based pricing takes effect until the user disconnects the vehicle. The findings indicate that, in the combined approach, utility owners have the potential to generate more revenue. Conversely, the time-based approach demonstrates a capacity to serve a higher number of electric EVs, with comparable queue lengths observed in both approaches. Importantly, using the results of this paper, policymakers can suggest pricing schemes that maximize benefits for utility owners, reduce queues at charging stations, and ensure the security of power grids. [ABSTRACT FROM AUTHOR]

In this study, the existing agent-based simulation framework MATSim is extended towards a module for the simulation of trains and their interactions with the infrastructure. The proposed railway simulation module railsim offers several advantages over existing railway planning tools: railsim is open-source, easily adaptable, allows for a microscopic, mesoscopic or hybrid micro-mesoscopic simulation setup and is fully integrated in an existing simulation framework. Trains have a spatial dimension along several infrastructure segments. Trains accelerate and decelerate based on vehicle- and infrastructure-specific attributes as well as the available capacity ahead of the vehicle. The interaction of vehicles as well as the modeling of the infrastructure is adapted to the railway system. Trains are enabled to reserve infrastructure elements (fixed block segments, moving blocks), and reservation requests interact with each other. The illustrative Switzerland case study reveals that an overall unique capacity of 3 trains per section and 10 trains per station ensures most trains reach their destinations successfully, while lower capacities lead to insufficient infrastructure and train disruptions. [ABSTRACT FROM AUTHOR]

Taiwan has gradually increased its electric vehicle (EV) market share over the past few years. The increasing availability of affordable EV models has also contributed to the rising market share of EVs in Taiwan. This study provides a system dynamics model for evaluating the impact of various factors on the electric vehicle market share in Taiwan. The model incorporates key variables such as consumer preferences, financial acceptability, charging infrastructure and accessibility, EV performance, and customer acceptance to simulate the dynamic behavior of the EV market. Moreover, the model utilizes historical data and the government's target plan to calibrate and validate a model that accurately captures the current state of Taiwan's electric vehicle (EV) market share. Results indicate that charging stations and financial issues impact EV adoption significantly. Overall, this study demonstrates the usefulness of system dynamics modeling in evaluating policy interventions and identifying critical drivers for promoting the transition to a greener transportation system. [ABSTRACT FROM AUTHOR]

• Derive new closed-form solution for multi contact responses from a six-DOF vehicle model. • Identify track modulus from the first "plus" frequency of the dual-beam with a simple formula. • Detect rail damage from the instantaneous amplitude squared generated from the driving component of the contact response. • Contact-point spectrum performs better since it is free of the vehicle frequencies. • Parametric study for factors affecting the technique. The vibration data recorded for the test vehicle cannot be directly used to detect the rail and bridge frequencies due to the overshadowing effect of vehicle's frequencies. To overcome this problem, the vehicle-rail contact response may be used instead. However, current techniques on this are available only for simple test vehicles with single or two degrees of freedom (DOFs). Normally, a train car contains a car body and two bogies (each of two wheelsets), and should be modeled by a multi- DOF system. The objective of this paper is first to solve the four wheelset-rail contact responses from the six measured vehicle responses. Then the contact responses are employed to retrieve the track modulus , while detecting the rail damages, for their salient feature of being free of the vehicle's frequencies. The railway track is modeled by a dual-beam system, with the upper (rail) and lower (bridge) beams connected by a spring-dashpot layer. The track modulus is retrieved through identification of the first "plus" frequency of the dual-beam, and the rail damage is detected from the instantaneous amplitude squared (IAS) generated from the driving component of the contact response. Through comparison with results by the finite element method (FEM), it is confirmed that: (1) the proposed procedure for computing the multi contact responses from the multi-DOF vehicle responses is reliable; (2) track irregularity presents a limited influence even for high disturbance; (3) to trade-off between efficiency and accuracy, a medium vehicle speed is suggested for the field measurement; (4) for the dual-beam with sufficient length, the use of simply supported condition is acceptable for rails; and (5) the effect of track damping is small and can be ignored. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of vehicle routing problems (VRP), the traditional focus on cost minimization has significantly evolved due to the pressing need to address environmental concerns. With the logistics industry experiencing exponential growth and increased vehicle utilization, sustainability has become a central consideration both globally and in strategic planning. The Vehicle Routing Problem (VRP) remains a key challenge in logistics and transportation management, aiming to optimize routes and vehicle allocation to minimize transportation costs. This study introduces an innovative VRP model that intricately balances economic efficiency with ecological responsibility, addressing the urgent call for sustainable solutions amidst the ongoing environmental crisis. The proposed model features a diverse fleet of trucks, each designated for specific product types, creating a heterogeneous fleet. Originating from multiple depots, these trucks ensure comprehensive geographic coverage and enhance logistical efficiency. The core objective of the model is twofold: minimizing the financial transportation costs associated with supplying multiple products from multiple depots to customers and significantly reducing the carbon footprint, thereby addressing environmental sustainability. Two distinct solutions are proposed: one based on mixed-integer linear programming (MILP) and the other utilizing a heuristic approach. The choice of mixed-integer linear programming instead of linear programming is due to the need to make binary route decisions. Results are compared, focusing on outcome variation and computation time. Additionally, the solution demonstrates upto 100% variation in results when comparing the heuristic model with the optimal values derived from mixed-integer linear programming, though it requires less computation time. This research underscores the importance of seamlessly integrating economic and environmental considerations in the logistics industry. The proposed model embodies this principle, representing a substantial step towards a more sustainable future. By aligning objectives with global sustainability goals set by the United Nations in 2015, this research contributes to the discourse on sustainable development. It lays the foundation for future innovations in vehicle routing and logistics. [ABSTRACT FROM AUTHOR]

• DRLs are designed to be on during the day to increase the visibility of vehicles. • There have been no recent studies evaluating DRLs currently present in the fleet. • DRLs reduce non-night-time multi-vehicle crash involvement by 8.8% • Crash reductions were higher at dawn or dusk and in higher speed zones. • A DRL mandate would likely lead to reductions in the overall crash risk of the fleet. Introduction : Many studies have found that daytime running lights (DRLS) are effective in reducing daytime multi-vehicle crashes. From an Australian perspective, while studies exist using data from other jurisdictions, there has been uncertainty about how effective DRLs would be under Australian environmental conditions, which can differ from other parts of the world. In addition, in recent years DRLs have become a standard feature of many new vehicles. The objective of this work was to utilize Australian crash data to estimate the impact of DRLs on casualty crash risk reflecting the Australian crash population and local conditions. It also aimed to broadly examine the real-world crash-based effectiveness of DRLs currently present in the light vehicle fleet. Method : The study utilized police reported casualty crash data for crashes that occurred during 2010–2017. The analysis used induced exposure methods, which offers the potential to assess the relationship between crash risk and DRL fitment by intrinsically controlling for confounding factors. Results : It was found that DRL fitment can reduce the overall risk of being involved in a non-nighttime multi-vehicle crash where vehicle visibility may be a factor in crash causation by a statistically significant 8.8%. Estimated crash reductions were higher at dawn or dusk and in higher speed zones. Conclusion : Results provide clear evidence that mandating DRLs on all new vehicles would likely lead to reductions in the overall crash risk of the fleet through accelerating fitment through the fleet. Practical Application : DRL fitment can reduce the overall risk of being involved in a non-night-time multi-vehicle crash where vehicle visibility may be a factor in crash causation. Governments should consider a DRL mandate on all new vehicle models, including all variants to accelerate the process of fitment through the fleet. This would likely lead to reductions in the overall crash risk of the fleet. [ABSTRACT FROM AUTHOR]

The irregular ocean environment brings difficulties to observation and affects the movement state of underwater vehicles. Multisource information fusion can provide more reliable results on information-rich datasets. This paper proposes a multisource information fusion-based environment perception and dynamics model for underwater vehicles in irregular ocean environments. First, the multisource observation data is decomposed by the latent feature disentanglement learning model into the latent stable and dynamical features. The dynamical feature is predicted by the retrospect regression method and then integrated with the latent stable feature to predict the ocean field. After that, the environment-vehicle coupling effect is explored by thermodynamics and statics simulation. Then, a real-time dynamic model of the underwater vehicle is constructed by combining multisource information from physical principles, environmental perception, and sensor observations. Finally, extensive experiments are performed on a novel underwater glider and a publicly available South China Sea dataset to verify the proposed method. • An environment perception model is proposed for the underwater vehicle array. • A real-time dynamic model is proposed for the underwater vehicle. • Environment-vehicle coupling effect is explored. [ABSTRACT FROM AUTHOR]

• The floating bridge deck excites the vehicle vertically at frequencies below 0.02 Hz. • Wind excitations in lateral and horizontal directions are of the low frequencies, below 0.05 Hz. • With increasing vehicle speed, the vehicle's deviation from the intended path grows. • The risk of roll-over and traffic lane departure are significant when the vehicle drives onto the bridge. • The empty semitrailer has a higher risk of roll-over and path deviation when compared tothe laden semitrailer. The proposed floating bridge solution at Bjørnafjorden in connection with the E39 infrastructure upgrade is an enabler to realize efficient transportation. This bridge and the vehicles shuttling on it will be exposed to inclement weather conditions. The waves and wind excite the floating bridge to induce compound motion in addition to the aerodynamic crosswinds directly interacting with the vehicles. Methods to introduce the complex motion of the floating bridge (multi-post test rig) and aerodynamic crosswinds on a tractor semi-trailer have been established and presented in this paper. The environment-vehicle-driver system is enabled through a co-simulation between MATLAB/Simulink (primary) and Adams (secondary). This complex interplay is studied on the intricate 627-DoF Adams vehicle model coupled with the Adams driver model. Numerical simulations are performed for multiple constant vehicle speeds under laden condition on a road with friction of 0.7 for the 1-year storm weather condition. Vehicle stability and safety assessments such as lane violation, path following ability, rollover risk, and lateral side slip limit are evaluated to draw inferences. Subsequently, permissible vehicle speed for a laden tractor semi-trailer to operate on the floating bridge is suggested. Furthermore, a simpler 9-DoF tractor semi-trailer vehicle model developed in MATLAB/Simulink combined with the pure pursuit tracking based driver model is compared with the Adams model under identical environmental conditions for an unladen case. The simpler vehicle-driver model is validated against the detailed Adams vehicle-driver model through numerical simulations for different constant vehicle speeds. [ABSTRACT FROM AUTHOR]

• A three-dimensional cavity topology model for dynamic simulations is established. • The planning model of supercavitating vehicles is improved for more degrees of freedom. • A numerical algorithm to simulate supercavitating vehicles in three-dimensional space is developed. • Evolution of cavity shape and dynamic characteristics of vehicles are compared by fins deflection. The maneuvering motion of a supercavitating vehicle pitching and yawing inevitably occurs when navigating in three-dimensional space. In order to give some insights into design of underwater vehicles, the maneuvering motion in the longitudinal and horizontal planes is investigated in this paper. Considering the definition of multi-body coordinate frames, a three-dimensional cavity topology model is proposed to predict the development of the shape of cavity and interact with the supercavitating vehicle in the simulations. Based on the cavity model, an improved planning model is established to calculate the necessary critical parameters of the planning force. According to the mathematical model above, a numerical algorithm is developed to simulate the maneuvering motion of the supercavitating vehicle. The higher algorithm efficiency is validated by the relative error of 10% order of magnitude and the more stable performance. The real-time scene figures and time-varying curves of the dynamic characteristics are obtained through the developed algorithm to study the influence of fins deflection on the maneuvering motion. The developed algorithm is validated to some extent to be a better alternative than the empirical one and provides a new method for analyses of simulation results of supercavitating vehicles. [ABSTRACT FROM AUTHOR]

The automotive industry has to deal with wide-reaching challenges and changes. A continuous gain of significant competitive advantages in a highly competitive market requires automotive manufacturers to produce new products with a large diversity of variants at ever-shorter time intervals. As a result, the frequency of series launches has increased and the need for a sophisticated vehicle project management and an efficient handling of vehicle changes and model updates along the product process have gained significant importance. However, current project management practices and frameworks utilized by automotive manufacturers do not effectively address this demand, e.g., the goals of increasing operational efficiency and productivity as well as establishing continuous improvement are not sufficiently achieved. While these goals are traditionally common-place in certain fields of the automotive manufacturing, e.g., in production, logistics, or warehousing, their dedicated consideration in the vehicle project management or one of its neighboring areas like the vehicle change management has not been examined thoroughly in the required extend. Understanding the determinants of the needed change in the vehicle project management and its related fields is a central objective of the research presented in this paper. In this context, this paper proposes a novel approach on how to manage the transition and why this is a challenge to common project management practices utilized traditionally in the automotive industry. [ABSTRACT FROM AUTHOR]

• Very old vehicles continue to be driven on US streets and carry a relative risk of crash fatality that is approximately 4–17 times that of newer vehicles. • These very old vehicle crashes tend to occur on rural roads at speeds below highway speeds in fair weather conditions. • Older occupant age, lack of restraint use, and alcohol use are associated with fatality in these events. Introduction: Older vehicles, commonly referred to as "classic," "vintage," or "historic" vehicles (CVH), share the roadways with newer vehicles. Older vehicles lacking safety systems likely come with an increased risk of fatality, however there is no study examining the typical conditions for crashes involving CVH. Method: This study utilized information from crashes occurring in 2012 to 2019 to estimate fatal crash rates for vehicles grouped by model year deciles. Data from crashes documented in the National Highway Traffic Safety Administration's (NHTSA) FARS and GES/CRSS data sets were utilized to examine roadway, temporal, and crash types for passenger vehicles produced in 1970 or earlier (CVH). Results : These data show CVH crashes are rare (<1% of crashes), but carry a relative risk of fatality from 6.70 (95th CI: 5.44–8.26) for impacts with other vehicles, which was the most common crash, to 9.53 (7.28–12.47) for rollovers. Most crashes occurred in dry weather, typically during summer, in rural areas, most frequently on two lane roads, and in areas with speed limits between 30 and 55 mph. Factors associated with fatality for occupants in CVH included alcohol use, lack of seat belt use, and older age. Conclusions and Practical Applications: Crashes involving a CVH are a rare event but have catastrophic consequences when they do occur. Regulations that limit driving to daylight hours may lower the risk of crash involvement, and safety messaging to promote belt use and sober driving may also help. Additionally, as new "smart" vehicles are developed, engineers should keep in mind that older vehicles remain on the roadway. New driving technologies will need to safely interact with these older, less safe vehicles. [ABSTRACT FROM AUTHOR]

T-bone collision constitutes an emergency crash scenario that results in casualties and heavy losses; it is an excessively complicated scenario that cannot be handled by conventional control systems. This paper presents an innovative crash mitigation controller for application during unavoidable T-bone collisions to expand the vehicle-maneuverability envelope and minimize crash severity; this controller combines prior knowledge using an optimum expert-behavior policy and drift-operation mechanism based on an improved reinforcement learning algorithm, TD3. Vehicle and tire modeling are performed considering the nonlinear and coupled dynamics characteristics to improve control accuracy. Unlike conventional control systems and other reinforcement learning algorithms, the proposed controller realizes the optimum crash mitigation effect under different scenarios. It is expected to afford autonomous driving technologies with enhanced operating capabilities under extreme conditions. • A TD3-based crash mitigation controller is proposed. • Optimal expert behavior policy and drift operation mechanism are considered. • Crash severity factor is defined based on vehicle collision databases. • Results prove the superiority of the proposed crash mitigation controller. [ABSTRACT FROM AUTHOR]

Collaboration is key to addressing operational efficiencies, and in the context of last mile delivery, operational inefficiencies arising from empty trips, low load factor, and long dwell times require collaboration amongst multiple logistics service providers (LSPs). However, existing studies in the last mile delivery adopting collaboration as a means of sharing strategic infrastructure assets, such as distribution centers (DCs), satellites, and driving vehicles, might not be seen as favorable for logistics service providers due to ownership disputes and loss of control over assets. To address this limitation, a new routing method for addressing operational efficiencies in last mile delivery has been proposed in this study, allowing for collaboration across multiple logistics service providers without sharing strategic assets. We have formulated a two-echelon vehicle routing problem with collaboration Points (2E-VRP-CP) where the exchange of goods happens between second-echelon vehicles belonging to different logistics service providers. The method uses a mixed-integer linear programming model (MILP) that minimizes the total distribution cost and has been tested on randomly generated instances. Results suggest that the proposed collaborative approach of exchanging goods between second-echelon vehicles belonging to different logistics service providers can reduce costs by up to 10% relative to the non-collaborative approach and up to 9% compared to the existing collaborative approach that shares strategic assets. A four-phase heuristic algorithm has also been developed to tackle computationally expensive larger instances, which can obtain cost savings of up to 15% compared to a non-collaborative approach. Future work will involve developing a profit-allocation mechanism and will consider additional constraints to make the model more realistic in addressing a real-world problem. Overall, this model can help fleet managers achieve efficient fulfillment in last mile delivery, while the proposed heuristics can enable stakeholders to scale their solutions to real-world scenarios. • A new problem addressing collaboration among multiple logistics providers is studied. • It addresses efficient collaboration among logistics firms without shared facilities. • Synchronization and time constraints are excluded due to computational complexities. • We designed a MILP for small instances and a four-phase heuristic for larger ones. • Our approach cuts costs by 10% on small instances and 15% on large ones. [ABSTRACT FROM AUTHOR]

A precise vehicle dynamics model is critical for simulation and algorithm testing. Neural networks have been widely used to build high-fidelity vehicle dynamics models due to the excellent learning ability. However, data starvation is a common phenomenon in neural networks. With limited data, it is difficult for neural networks to achieve precise predictions. To address these problems, a hybrid model combining physics and dual attention neural networks is developed to model vehicle lateral dynamics. First, due to the interpretability of the physical model, linear lateral dynamics model is regarded as a prior model. However, due to the imperfect prior knowledge, there are residuals between the prior and the actual vehicle dynamics. Therefore, neural networks are used to characterize the residuals to achieve recalibration of vehicle dynamics model. Modeling vehicle residual dynamics with neural networks is a time series forecasting problem. The GRU with a dual attention mechanism and adaptive initial hidden states (DA-AGRU) is designed to capture spatial and temporal correlations in the vehicle dynamics data. In particular, considering the unique auto regressive structure of the hybrid model, a spatial attention mechanism with a feature fusion module is designed, so as to globally compute the weights of different channel features. The dataset used to train and validate the model is recorded from a vehicle platform, and the experimental results show that the proposed hybrid model can accurately predict vehicle dynamics states in a data-scarce environment. [ABSTRACT FROM AUTHOR]

The trust system is widely used to prevent malicious behaviors, and it is a key element for vehicles to establish interactions in the Internet of Vehicles (IoV). Nevertheless, trust and privacy remain unresolved concerns stemming from the distinctive features of the IoV. The IoV must thwart malicious attackers from spreading false data while ensuring that the vehicle's evaluation data is not leaked, which is of utmost importance. In this paper, we propose a blockchain-based trust model (BPS-V), which supports ciphertext computation of trust evaluation data submitted by different vehicles. Design a cooperative update method for vehicle trust, which utilizes an improved distributed two-trapdoor public-key cryptography algorithm to achieve cooperative computing of trust and reduce the risk of privacy leakage of evaluation data. On this basis, BPS-V introduces blockchain sharding technology to realize cross-domain storage and sharing of the trust. Simulation results show that our scheme can effectively protect the privacy of evaluation data and maintain a high detection rate and low false alarm rate in different road environments. Compared with traditional schemes, BPS-V can improve the efficiency of trust updates and the detection of malicious vehicles by 9.5% and 32%. [ABSTRACT FROM AUTHOR]

In U-turn bays near intersections, the conflict between U-turning vehicles and those going straight-ahead results in traffic accidents since straight-ahead vehicles cannot reliably anticipate the behavior of oncoming U-turning vehicles. However, previous studies on modeling U-turning behavior do not effectively capture the spatial–temporal interaction between the U-turning and surrounding vehicles. To address this issue, a deep-learning framework based on a temporal convolutional network (TCN) and multi-head attention mechanism is developed. The TCN is utilized to capture long-term dependencies of vehicles in the shared left- and U-turn lanes by extracting vehicle historical motion features. The self-attention mechanism extracts salient features related to the U-turn intentions, classifying the vehicles into left- and U-turning vehicles based on their driving intentions. A parallel TCN and spatial multi-head attention structure is constructed to model vehicle–vehicle interactions to further predict the future trajectory of U-turning vehicles. Finally, the obtained features are input into a Transformer-based decoder module and trajectory generator to predict the future displacement and body orientation of U-turning vehicles. The model is validated via comparison with state-of-the-art models and the observed trajectories under various scenarios. Ablation studies are conducted to quantify the efficacy of each module. Further, the effect of the surrounding homogenous and heterogeneous vehicles on U-turning vehicles in four different U-turn scenarios is quantified using spatial–temporal variation graphs and attention matrices. [ABSTRACT FROM AUTHOR]

• Flame radiation heat transfer and heat flux of vehicle fires beneath a tunnel arced ceiling are studied. • The transverse flame extension length of an arched ceiling is less than that of a horizontal ceiling. • The flame extension length beneath the arced ceiling decreases as the burner aspect ratio increases. • A correlation is developed to characterize the flame extension length beneath an arced ceiling. • A cuboid-arc plate flame model is proposed to predict near-field radiative heat flux upon below surface of rectangular fires. As a major form of heat transfer, flame thermal radiation has an important effect on the ignition of adjacent energy, fire spread and environmental damage. Forecasting the flame radiation heat flux can provide theoretical basis for fire hazard assessment, fire extinguishing and salvation. Meanwhile, the distinctive structure of the arc tunnel can result in significant variations in the local heat and mass transfer processes during tunnel fires compared to those in rectangular tunnels, leading to different fire characteristics parameters. In this study, experiments and theoretical analyses were conducted to investigate the flame length scale and near-field radiation heat flux on the lower surface of an arc-shaped ceiling induced by vehicle fires in a tunnel. The findings reveal that the flame length scale in the transverse direction beneath the arc-shaped ceiling decreases with an increasing burner aspect ratio and increases with the increasing heat release rate. A dimensionless model of the flame length scale under the arced ceiling with various burner aspect ratios was proposed based on the amount of remaining unburned fuel after flame impingement and the buoyancy component along the arc-shaped ceiling. Simultaneously, radiative heat fluxes on the lower surface also increase with rising heat release rate. The flame geometry beneath the arc-shaped ceiling is assumed to be a combination of a cuboid and an arc plate model based on the actual flame length scale. Subsequently, the view factor, radiative fraction, and flame surface area are analyzed to predict the radiative heat fluxes on the lower surface. The predictions of the cuboid and arc plate flame model show good agreement with the current experimental data. This work presents a dimensionless correlation of the flame length scale and calculates the view factor between the target and the arc plate to predict the near-field radiative heat flux more accurately. This approach also facilitates the understanding of flame spreading behavior and thermal hazards impacted by arced ceiling. [Display omitted] [ABSTRACT FROM AUTHOR]

The present paper is concerned with the wayset-based guidance of underactuated multirotor aerial vehicles (MAVs). A hierarchical guidance and control structure is first established, in which the guidance is realized as a supervisory loop. The lower-level stabilizing attitude and position control laws are assumed to be available. On the other hand, the outer-loop guidance is designed based on a fixed-horizon tube-based robust model predictive control (MPC), which conducts the MAV to visit a given sequence of waysets, without violating their state and control bounds, and allowing the vehicle to rest in each wayset for a specified period. The MPC is designed using a reduced-order closed-loop dynamic model describing the vehicle's translation, which is derived considering the stabilizing position and attitude control laws and the assumption of a time-scale separation between the closed-loop translational and rotational dynamics. This model is put into a discrete-time linear state–space representation subject to additive bounded random disturbance and measurement noise. The properties of the proposed method, which includes the MPC recursive feasibility and robust stability as well as the overall guidance feasibility, are analytically studied. The method is also numerically evaluated using a realistic quadrotor dynamic model, showing its effectiveness and confirming its properties. • Wayset-based supervisory guidance loop for underactuated multirotor aerial vehicles. • Wayset transitions based on dual-mode tube-based robust model predictive control. • Proof of recursive feasibility, robust stability, and overall guidance resolvability. • Obtained performance and low computational burden encourage real-time implementation. [ABSTRACT FROM AUTHOR]

This paper studies the problem of secure control of automated vehicles in a platoon-based driving pattern over a vehicular ad-hoc network (VANET) subject to various cyber attacks. The platoon under consideration is a convoy of a leader vehicle whose control input is unknown to its following vehicles and some follower vehicles with uncertain heterogeneous engine time constants, bounded disturbance and noise. First, a local estimator is developed for each follower vehicle so as to construct some confidence ellipsoidal estimation region always enclosing vehicular true state regardless of uncertain heterogenous engine time constants, bounded disturbance and noise. A convex optimization algorithm is proposed to find some optimal ellipsoidal sets and recursively solve out gain matrices of the local estimators. Then, a scalable control protocol employing the state estimates from its local and underlying neighboring estimators is designed to accomplish secure platooning control. Under the derived design technique, the resulting closed-loop platooning tracking errors are proven to remain in the vicinity of zero. Comparative studies are conducted to validate the efficacy of the proposed control method on achieving the satisfactory platooning performance by handling different attack strategies. • A scalable secure platooning control protocol is designed for automated vehicles. • A class of amplitude-bounded attacks is considered to represent various attacks. • A local set-membership estimator is designed for each follower vehicle. • A general model of vehicle longitudinal dynamics is considered. [ABSTRACT FROM AUTHOR]

This work solves the robust passive fault-tolerant control problem for autonomous electric vehicles based on an adaptive event triggered mechanism. Firstly, given the system uncertainties from the tire dynamics and the longitudinal speed, the T–S fuzzy model method is used to approximate the vehicle lateral dynamics. Secondly, taking the communication constraints caused by band-limited networks into account, an adaptive event-triggered scheme is introduced in the process of the control design. Moreover, the asynchronous constraint of the weight function between the controller and system is considered. Thirdly, considering that the actuator faults are inevitably encountered in the control system, a robust passive fault-tolerant control method is proposed to improve vehicle performances. Finally, simulations are carried out to illustrate the effectiveness and robustness of the proposed approach. • A fuzzy vehicle dynamic model is constructed with asynchronous constraints. • An improved AET mechanism is designed to reduce the transmitted number of signals. • The actuator faults are described by a polytope with finite vertices. • A new Lyapunov function is developed to solve the problem of limited delay range. [ABSTRACT FROM AUTHOR]

In this paper, the tracking control problem of non-minimum phase flexible air-breathing hypersonic vehicles (AHSV) is investigated subject to actuator fault, external disturbances and parameters uncertainties. The study is began with a series of control-oriented manipulations: first, the input–output dynamics are derived by using feedback linearization method and the internal dynamics of AHSV are constructed; then, the zero dynamics stability analysis is conducted to verify the non-minimum phase characteristic of AHSV. In order to realize output tracking of the non-minimum phase system with sufficient accuracy, an adaptive fault tolerant controller (FTC) is proposed based on an output-redefinition making the zero-dynamics with respect to the new output stable. Additionally, robust adaptive laws are utilized for the estimation of unknown parameters and actuator failure compensation of the AHSV model. Furthermore, the stability of the closed-loop system is analyzed based on the Lyapunov stability theory. At last, the numerical simulation results are provided to demonstrate the effective tracking performance of the proposed FTC scheme. • The proposed FTC scheme is based on multi-input multi-output (MIMO) hypersonic vehicle longitudinal model and without neglecting elevator-to-drag decoupling for the original model. • By employing least square parameter estimation method in conjunction with stable system center (SSC) method, ideal internal dynamics (IID) can be calculated online. • The designed tracking controller based on output-redefinition is applicable for other non-minimum phase systems subject to parametric uncertainties and actuator faults. [ABSTRACT FROM AUTHOR]

Three recently approved space missions are headed towards Venus, to help answer major questions about Venus atmosphere and geology. However, many existing questions cannot be properly addressed without direct in situ measurements from Venus surface or within the atmosphere. To this end, flapping wing vehicle concept is selected, optimized for Venus atmospheric flight, and evaluated using energy efficiency as performance criteria. Flapping wing vehicle computational model is derived based on discrete variational mechanics and quasi-steady aerodynamics, with all relevant aerodynamic phenomena included. Flapping wing vehicle computational model is then embedded within optimization algorithm, which is utilized to obtain energy efficient flapping patterns for forward flight in Venus surface atmospheric conditions. Numerical optimization is performed for different neutrally buoyant configurations, with wingspan ranging from 10 mm to 1 m. Different forward velocities are used as well, where maximum velocity is limited by an advance ratio of 0.5. Bumblebee and hummingbird-sized vehicles, with a wingspan of 30 mm and 30 cm, are selected as the most representative test cases and thoroughly studied. It is proved that flapping wing propulsion is a feasible and effective concept for Venus exploration purposes. Finally, based on a comparison of the selected test cases, general conclusions are drawn on the flapping wing dynamics and flight mechanics in the Venus atmosphere. Significant difference in the propulsion mechanism has been observed, based on the aerial vehicle size. In order to maximize propulsive efficiency, the smaller vehicle mostly exploited aerodynamic forces related to the leading edge vortex, while the larger vehicle relied more on added mass and rotational forces. • Flapping wing vehicles can be used for Venus atmospheric flight and exploration. • Quasi-steady forward flight model can be used to model Venus flapping flight. • Flapping wing dynamics can be efficiently optimized using DMOC framework. • Added mass and rotational forces have strong impact on Venus flapping flight. [ABSTRACT FROM AUTHOR]

The extraction of damage-sensitive features based on deep learning for the vibration response caused by vehicle-bridge interaction (VBI) has become a mainstream method in damage identification. However, most studies remain at the stage of qualitative damage assessment, not achieving precise numerical quantification of damage. They also typically involve multiple frequent runs using only a single dedicated vehicle. In this study, utilizing a deep neural network designed for multi-objective regression tasks, a novel method for bridge damage localization and quantification is presented with a multi-objective regressor designed to create a direct mapping between the curve of bridge deflection in time domain and the damage information matrix. The final quantitative damage values are obtained through statistical analysis. The method is evaluated on a simulated data set generated from VBI simulation using various 3D heavy vehicle models. The results demonstrate that this method can accurately locate and quantify damage even with unseen vehicle load conditions, damage scenarios, and measurement noise. The structure and depth affect the effectiveness of extracting damage-sensitive features from time-domain deflection during training. The study shows potential for real-time damage identification under normal operating conditions of real bridges in the rapid development of intelligent transportation systems. • Supervised learning for damage identification is advanced with a multi-objective regression task that quantifies and localizes damage intuitively. • By learning the differencing deflection, real-time bridge damage assessment under various vehicle scenarios was achieved. • Comparing some mainstream networks, the differences in time and space feature extraction on dynamic deflection are explored. • It explores how network depth impacts feature extraction in classic deep residual neural networks for damage detection. • Deep networks' generalization is utilized for damage identification under unseen vehicle and bridge conditions. [ABSTRACT FROM AUTHOR]

The automobile industry usually ignores the height of the path and uses planar vehicle models to implement automatic vehicle control. In addition, existing literature mostly concerns level terrain or homogeneous road surfaces for estimating vehicle dynamics. However, ground vehicles utilized in forestry, such as forwarders, operate on uneven terrain. The vehicle models built on level terrain assumptions are inadequate to capture the rolling or pitching dynamics of such machines as rollover of such vehicles is a potential risk. Therefore, knowledge about the height profile of the path is crucial for automating such off-road operations and avoiding rollover. We propose the use of a six-degrees-of-freedom (6-DOF) dynamic vehicle model to solve the autonomous forwarder problem. An adaptive linear tire model is used in the 6-DOF model assuming the vehicle operates in a primary handling regime. The force models are modified to include the three-dimensional (3D) map information. The calibration procedures, identifying actuator dynamics, and quantifying sensor delays are also represented. The proposed vehicle modeling contributed to realizing the continuous-discrete extended Kalman filter (CDEKF), which takes into account the 3D path during filtering and fixed-lag smoothing. Polaris (an all-terrain electric car) is used as a case study to experimentally validate the vehicle modeling and performance of the state estimator. Three types of grounds are selected — an asphalt track, a concrete track with a high elevation gradient, and a gravel track inside a forest. Stable state estimates are obtained using CDEKF and sparse 3D maps of terrains despite discontinuities in satellite navigation data inside the forest. The height estimation results are obtained with sufficient accuracy when compared to ground truth obtained by aerial 3D mapping. Finally, the proposed model's applicability for predictive control is demonstrated by utilizing the state estimates to predict future states considering (3D) terrain. • A 6-DOF dynamic vehicle model is used for autonomous driving in terrain. • The force models are modified considering 3D terrain. • CDEKF with fixed-lag smoothing is used for online state estimation. • State estimation is demonstrated in RTK-GNSS denied areas. • The 3D form of the future path is used in state predictions using the dynamic model. [ABSTRACT FROM AUTHOR]

To enhance vehicle stability and safety, the four-wheel independent steer-by-wire (4WISBW) system has garnered significant attention. However, the characteristics of corner modules, including model parameters uncertainty and disturbance torque, directly contribute to the deterioration of dynamic response in tracking control. And the differences in the characteristics leading to reduced synchronization performance in the 4WISBW system and hindering effective coordination. To enhance the synchronization and tracking control performance of the 4WISBW system, a novel control strategy, coupled with the fictitious master-generalized mean deviation coupling structure (FMGMDCS), is proposed. Firstly, the corner module dynamic model and the vehicle dynamic model are established. Subsequently, the impact of the differences in the characteristics on the system's tracking and synchronization control is analyzed. Next, the FMGMDCS and the angle synchronization controller based on a new reaching law sliding mode control (NRLSMC) are proposed to compensate for synchronization errors in the 4WISBW system caused by the differences in the characteristics of corner modules. Finally, a radial basis function neural network fast terminal sliding mode control (RBF-FTSMC) steering angle tracking controller is designed to enhance the tracking performance of corner modules. Simulation and experimental results indicate that the proposed control strategy can effectively solve the synchronization problem of the 4WISBW system and improve the system's tracking performance. [ABSTRACT FROM AUTHOR]

The goal of this work is the development of a general nonlinear single-track model for the cases of curved flat or banked road paths and the identification of the fundamental "handling bricks" or vehicle "DNA", extending an existing theory for flat road tracks. The handling bricks are related to specific design parameters of the vehicle. It is confirmed (both theoretically and numerically), that two structurally different vehicles, with the same handling bricks, exhibit very similar or identical handling behavior. The current nonlinear model accepts general steering input, and arbitrary axle characteristic functions and can be used for general (flat or banked) road tracks. If necessary, various types of constraints can be easily enforced, linking the dynamic parameters of the model. For example, given a complex steering input, the vehicle's (center of gravity) velocity and (or) the vehicle heading, can be related to the direction of the banked road path. On the other hand, the road path can be given independently of the steering data to check the deviation between the intended and the obtained road paths. An implicit nonlinear Crank-Nicolson time marching technique (with additional internal iterations at each time step), has been developed and implemented for the set of (nonlinear) ordinary differential equations (in a homemade VBA framework). The numerical results, using magic formula-based axle characteristic functions, confirm the robustness of the model formulation. • Development of a general nonlinear single-track model. • Identification of the fundamental "handling bricks" or vehicle "DNA". • Applications in curved flat or banked road paths. • General steering input and arbitrary axle characteristic functions. • Development of a novel implicit nonlinear Crank-Nicolson time marching technique. [ABSTRACT FROM AUTHOR]

Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from connected vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, driver dynamics while reconstructing the trajectories are ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction on freeways. The approach introduces a calibration algorithm for time-varying shockwave speed. The shockwave speed calibrated by the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionally, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption by applying a vehicle movement model. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption. • Integrate Lagrangian and Eulerian observations to reconstruct trajectories. • Calibrate time-varying short-term shockwave speeds using the two types of data. • Reconstruct trajectories for non-connected vehicles based on shockwave theory. • Optimize trajectories by adding driver dynamics for better energy estimation. • Evaluation on real-world datasets shows excellent performances from several aspects. [ABSTRACT FROM AUTHOR]

Electric vehicle routing problem (EVRP) is a special vehicle routing problem, which owns several specific characteristics of EV technology including the limited charging stations and the limited cruising range of EVs. This paper proposes a two-stage heuristic algorithm for EVRP with a kind of pseudo node, termed EVRPPN-TSH, which not only contributes to the model innovation but also to the algorithm design. In model innovation, the pseudo node is introduced into the EVRP model, forming EVRPPN, to reduce the search space and time complexity for obtaining service orders of customers from O (n) to O (1). Moreover, different from other research works which only use total distance as the fitness function, an improved fitness function is proposed, which takes all the distance, the electricity and capacity constraints compliance into consideration. In algorithm design, we first adopt the two-stage idea, which divides EVRP into the capacitated vehicle routing problem (CVRP) and a fixed route vehicle charging problem (FRVCP). Then we design a two-stage heuristic algorithm, termed as TSH. Specifically, for CVRP, a route division heuristic algorithm and a depot move heuristic algorithm are designed to divide routes. Further, an adjustment strategy with three operators is designed for adjusting the customer order. For FRVCP, an existing heuristic algorithm is adopted and enhanced by a variable extent shaking algorithm to avoid local trapped. Experimental results show that EVRPPN-TSH generally outperforms other state-of-the-art algorithms. Moreover, EVRPPN-TSH can obtain the best-known solution in all the 7 test cases, and update the best-known solution in test case E101 from 834.84 to 834.22. • In model innovation, we introduce pseudo node in EVRP model to reduce complexity. • An improved fitness function which considers constraint compliance is proposed. • In algorithm design, we propose two-stage heuristic approach to improve performance. • Illustrate the superiority of the improved model and the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent years, the challenge of distinguishing vehicles of the same model has prompted a shift towards leveraging both global appearances and local features, such as lighting and rearview mirrors, for vehicle re-identification (ReID). Despite advancements, accurately identifying vehicles remains complex, particularly due to the underutilization of highly discriminative text regions. This paper introduces the Text Region Attention Network (TANet), a novel approach that integrates global and local information with a specific focus on text regions for improved feature learning. TANet uniquely captures stable and distinctive features across various vehicle views, demonstrating its effectiveness through rigorous evaluation on the VeRi-776, VehicleID, and VERI-Wild datasets. TANet significantly outperforms existing methods, achieving mAP scores of 83.6% on VeRi-776, 84.4% on VehicleID (Large), and 76.6% on VERI-Wild (Large). Statistical tests further validate the superiority of TANet over the baseline, showcasing notable improvements in mAP and Top-1 through Top-15 accuracy metrics. [ABSTRACT FROM AUTHOR]

The drive-by bridge dynamic measurement using an instrumented vehicle during its passage over the bridge can be an indirect bridge structural health monitoring system. The identification of the contact-point (CP) responses between vehicle wheels and bridge structure from vehicle responses offers an efficient and economical way for the modal identification and condition assessment of short- to mid-span road bridges. This paper proposes a novel method for the contact-point displacement response reconstruction of the vehicle-bridge interaction (VBI) system in a joint input-state estimation manner based on Bayesian Expectation-Maximization (BEM). An analytical model of VBI including the road approach in front of a simply-supported bridge is presented firstly. Then the augmented state-space model of the vehicle with the contact-point responses included as variables along with vehicle states is established. A new method by integrating the augmented Kalman filter (AKF) with a BEM strategy is proposed to solve the state estimation problem without knowing the vehicle axle responses. The vehicle states and CP displacement responses are identified simultaneously. The effects of the measurement noise, road surface roughness and vehicle speed on the identified results are investigated using numerical simulations. The experimental tests are used to further verify the feasibility and accuracy of the proposed method. The results show that the proposed method has potential for drive-by bridge condition assessment using on-board vehicle response measurements for a large population of short- to mid-span bridges. • A method integrating AKF and Bayesian Expectation-Maximization (EM) strategy is proposed to identify the CP responses. • The CP responses and its derivatives are included to the vehicle states to develop an augmented state-space model. • The process and measurement noises can be tracked by the EM strategy making the identification robust to the noises. • Numerical and experimental studies are conducted to verify the proposed drive-by bridge structural monitoring method. [ABSTRACT FROM AUTHOR]

Environmental managers attempt to increasingly incorporate precautionary principles into decision making. The literature lacks Machine Learning -based approaches for forewarning lifecycle environmental impacts. This paper proposes a method to support electric vehicle design. The main innovation of the work lays in merging Life Cycle Assessment (LCA) and Machine Learning foundations to provide support and awareness to designers. The present approach overcomes the present literature because it provides a method for the design phase, is based on a well-established methodology (LCA) and provides quantitative results from little inputs. The approach exploits machine Learning Methods to develop models with the design features of a generic electric vehicle (such as vehicle mass and distance traveled) in six phases (Problem definition; Data collection; Data Preparation; Modeling; Model evaluation; Model interpretation). Differently from existing environmental analyses, all stages of the product life cycle have been considered in building the database; moreover, the model provides quantitative results. Regression models and supervised algorithms were used. The obtained model can be used by product engineers, as well as those not experts on LCA. Moreover, the model guarantees the database and hypothesis's uniqueness, ensuring the results coherence and comparability. The level of accuracy obtained in the case study (error or 17%) is comparable with studies handling full environmental analysis (that should be more accurate), and outstanding, as the present case is for the design phase. Future works will focus on additional significative indicators, similar electric vehicle design and integration with prospective LCA approaches. [ABSTRACT FROM AUTHOR]

Vehicle shimmy could occur in the running process when the wheel encounters external disturbance, which affects the vehicle safety and stability. Four types of passive inerter-based suppression mechanisms are proposed to enhance the vehicle shimmy performance, the steering linkage clearance is considered as the nonlinear factor existing in the vehicle system. The vehicle shimmy model using inerter-based suppression mechanism and considering steering linkage clearance is established according to the Lagrange theory, the vehicle shimmy performance is studied based on the bifurcation analysis theory, the stable area and limit cycle oscillation (LCO) magnitude are acquired and compared for different suppression mechanisms. The effect of steering linkage clearance and structural parameters of the inerter-based suppression mechanisms on the vehicle shimmy performance is investigated. The results show that the inerter-based suppression mechanism can reduce the unstable area dominated by left and right steering shimmy significantly, shrink the vehicle shimmy occurred speed range and decrease the maximum LCO magnitude of the front wheel swing motion. When the steering linkage clearance exists and increases, the unstable area, vehicle shimmy occurred speed range and maximum LCO magnitude increase significantly, the vehicle motion can change from periodic to quasi-periodic or even chaotic, which degenerates the vehicle shimmy performance. Furthermore, larger inertance of the inerter and smaller stiffness of the auxiliary spring, larger contact stiffness and friction damping in the steering linkage clearance is better to suppress the vehicle shimmy. In addition, the inerter-based suppression mechanism can decrease the wheel swing angle around longitudinal axis and vertical dynamic tire load, which improves the suspension performance and further shows its advantage. Therefore, the inerter-based suppression mechanism can effectively suppress the vehicle shimmy and gives guiding significance for vehicle damping component design. • The vehicle shimmy model is established using the Lagrange theory. • The vehicle shimmy performance is studied based on the bifurcation analysis theory. • The influence of steering linkage clearance on the vehicle shimmy performance is revealed. • The effect of inerter-based suppression mechanism on the vehicle shimmy performance is studied. [ABSTRACT FROM AUTHOR]

• A navigation accuracy compensation algorithm for INS is proposed for low-cost USVs. • An event-triggering mechanism is introduced, and the thrust models of the USVs are adopted to design the event trigger. • According to different events, various weights can be assigned to various integrators and PF. • Field experiments are conducted to verify the navigation performance of our algorithm. Integrated navigation is often used for navigation of unmanned surface vehicles (USVs). For a class of cost-sensitive USVs, it is desirable to compose integrated navigation by low-cost inertial navigation systems (INS) and global positioning systems (GPS). In the cases of GPS signal failure or interference, only INS onboard can be used for navigation, which often leads to the navigation accuracy deteriorating and may fail to meet application requirements. Therefore, a navigation accuracy compensation algorithm for low-cost USVs is proposed. An event-triggering mechanism is introduced, and the thrust models of the USVs are adopted to design the event trigger. According to different events, various weights can be assigned to the integrator based on INS, and measurement-based linear velocity V INS and heading angle ψ I N S can be obtained. Moreover, based on dynamic models, the model-based linear velocity V m and heading angle ψ m also can be calculated. Meanwhile, the event trigger is combined with the Particle Filter (PF) algorithm to accurately estimate the linear velocity V ^ P F and heading angle ψ ^ PF for navigation. Finally, field experiments are conducted and the differential GPS (DGPS) is used as the benchmark for comparison. The results show that our algorithm can improve navigation performance for the low-cost USVs. [ABSTRACT FROM AUTHOR]

The demand for used cars is ever-increasing globally, mainly due to availability and affordability. However, even with such high demand in the used car market, most buyers lack knowledge of the factors that can be used to estimate the quality of a used vehicle before the actual purchase. Thus, buyers often tend to obtain the support of a qualified vehicle inspector to evaluate the quality of the car and find out under which grade the vehicle falls before the transaction. This inspection process is intensive, and the vehicle inspectors frequently make mistakes when allocating a class. The proposed research uses deep learning techniques to mitigate the preconceptions of users during manual inspection of used vehicle sales. In this four-step procedure, at first, an image classification model will be used to determine whether the front, back, and two sides of a vehicle appear in all input photos. If it is satisfied, two parallel operations will be executed. The first parallel operation uses a pre-trained deep learning model to identify the vehicles make and model. The original input images are analyzed in the second parallel task using an object detection model to identify vehicle components. After the second parallel step, an extra deep learning model is used to classify the identified attributes, and damages into eight categories. In the last step, a grade is proposed using the NAMA (National Association of Motor Auctions, UK) vehicle grading method based on the specifics of the damaged component. Results reveal a 94% classification accuracy for vehicle orientation and a 97% classification accuracy for make/model using a bespoke VGG16 model. Further, after numerous model comparisons, component identification, and damage assessment have been obtained with 88.1% and 83.7% mAP (Mean Average Precision) values. [ABSTRACT FROM AUTHOR]

This paper studies the problem of path tracking of Autonomous Ground Vehicles (AGVs) in the presence of sideslip angles. An observer is designed to estimate both the sideslip angle and the vehicle yaw rate, based on which an observer-based controller is established such that the closed-loop system is stable and the vehicle follows a desired path accurately. In particular, the nonlinear vehicle dynamics model is reformulated as a Linear Parameter Varying (LPV) system, and a finite frequency H ∞ criteria is satisfied such that the disturbances are attenuated effectively, the parameter-dependent gain matrices are calculated simultaneously by solving a convex optimization problem. Simulation results show the effectiveness of the method proposed. • The measurement of vehicle states including positions and headings is apt to be affected by external disturbances. • An observer-based path tracking controller is designed for autonomous vehicles. • The vehicle model is reformulated as an LPV system. • Both the observer and controller gains are parameter-dependent and are obtained simultaneously. [ABSTRACT FROM AUTHOR]

The paper presents the first classical internal model control (IMC) design in the context of railway carbody roll control. We propose a simple control approach for a recent hydraulically actuated vehicle body roll concept that offers limited carbody roll. The IMC approach addresses both preview- and nulling-type tilt setups, highlighting related benefits and limitations. The design provides a model simplification process that facilitates PI and PID-type control structures without the need for complex optimization. A simple, yet practical, tool for the industrial rail rolling stock engineer in vehicle control design is offered. Vehicle roll performance is rigorously studied on the deterministic (curving acceleration response) and stochastic (ride quality) trade off. Simulations are performed on an in-house multibody dynamics software package employing a realistic nonlinear railway vehicle model and allow to appropriately assess the performance of preview and nulling type tilt performance. The results obtained confirm that preview tilt control offers the better tilt performance as it utilizes a tilt command reference, and highlighted that nulling-type tilt performance remains at a relatively comparable level with the former. [Display omitted] • The first Internal Model Control design related to railway carbody roll is presented. • A simple control design, for carbody roll with hydraulic actuation, is proposed. • The control design provides a model simplification process. • PI/PID-type control structures without adopting complex optimization are designed. • Performance of preview and nulling-type tilt setups are compared. • Vehicle roll performance is studied on the deterministic and stochastic trade off. [ABSTRACT FROM AUTHOR]

In this study, fuel consumption map was created by obtaining a large number of data from gasoline engine passenger car through high-speed data acquisition with ISO-15765–4-CAN Protocol. Subsequently, CO 2 map was also generated theoretically. To obtain precise results, ANN was used while creating the maps. Later, conventional vehicle model was created with Matlab/Simulink. Afterwards, hybrid vehicle model was developed by implementing electrical system in compliance with single-shaft pre-transmission parallel configuration with one electric motor. Hybrid powertrain control algorithm was created by considering efficiencies of mechanical and electrical power sources and sinks. Charge-neutral simulations were run with conventional and hybrid models over WLTC and a local route which is created by collected in-city speed data. The hybrid and conventional vehicles were enabled to be compared with the developed method. Detailed comparison of ICE operations both between two vehicle models and two cycles was presented. Moreover, obtained results, including overall electrical system efficiencies, were compared over WLTC and the local route. Significant improvements both in consumption and CO 2 values were concluded. It is also concluded that using the hybrid system in local route with higher proportion of in-city driving conditions caused higher improvements despite a slight decrease in overall electrical system efficiency. [ABSTRACT FROM AUTHOR]

Motivated by challenges in the motor manufacturing industry, a solution to reduce computation time and improve minimisation performance in the context of optimisation of battery electric vehicle powertrain is presented. We propose a cascade optimisation method that takes advantage of two different vehicle models: the proprietary YASA MATLAB® vehicle model and a Python machine learning-based vehicle model derived from the proprietary model. Gearbox type, powertrain configuration and motor parameters are included as input variables to the objective function explored in this work while constraints related to acceleration time and top speed must be met. The combination of these two models in a constrained optimisation genetic algorithm managed to both reduce the amount of computation time required and achieve more optimal target values relating to minimising vehicle total cost than either the proprietary or machine learning model alone. The coarse-to-fine approach utilised in the cascade optimisation was proven to be mainly responsible for the improved optimisation result. By using the final population of the machine learning vehicle model optimisation as the initial population of the following simulation-based minimisation, the initial time-consuming search to produce a population satisfying all domain constraints was practically eliminated. The obtained results showed that the cascade optimisation was able to reduce the computation time by 53% and still achieve a minimisation value 14% lower when compared to the YASA Vehicle Model Optimisation. [ABSTRACT FROM AUTHOR]

• One reliability-based design with uncertain manufacturing accuracy is proposed. • Lightweight optimization models of vehicle structures are established and validated. • The optimal design variables and maximum allowable deviation range are provided. • More design options considering uncertain manufacturing accuracy are provided. Structural lightweight optimization is one of the key themes to continuously improve vehicle performance. However, most of the studies so far have not considered reliability and manufacturing issues brought about by the uncertainty of tolerances in structural lightweight optimization. In this study, a reliability-based design for lightweight vehicle structures with uncertain manufacturing accuracy is proposed. This method can provide not only the optimal design variables but also the maximum allowable deviation range. The main design variables lacking sample information are regarded as interval variables and other design parameters as probabilistic values. A progressive reliability-based design with uncertain manufacturing accuracy is established for lightweight vehicle structures by employing the transformation method of the interval model and the decoupling strategy of the probabilistic model. Firstly, the general test function is used to analyze the accuracy and efficiency of the reliability decoupling method. Then, lightweight optimization models of a classic cantilever beam and two vehicle structures (parking robot frame, energy absorption box) are established respectively, and the effectiveness of the present optimization method is verified. The optimized design results show that the optimal variables and the corresponding maximum allowable deviation range under acceptable manufacturing requirements can be obtained. The requirements for manufacturing accuracy can be reduced by appropriately increasing the deviation range of the design variables, which may lead to a reduction in manufacturing cost. This study aims to provide feasible design options by optimizing lightweight structures considering uncertain manufacturing accuracy. [ABSTRACT FROM AUTHOR]

• A novel car-following model is put forward to study the impact of backward looking effect on autonomous vehicles flow. • The conditions for judging the stability of traffic flow of autonomous vehicles are obtained. • The Burgers, KdV and mKdV equations are derived based on the nonlinear analysis. • The simulation results are consistent with the theoretical analysis results. Autonomous vehicles can obtain abundant road traffic information and communicate with each other using intelligent transportation system. For the purpose of detecting the influence of backward looking effect on the traffic flow of autonomous vehicles and better providing driving decisions for autonomous vehicles, in this study, a novel car-following model is presented accounting for the backward looking effect on the basis of the two-velocity difference model. The stability condition of this novel model in autonomous vehicles flow is established by incorporating the linear stability theory. The Burgers equation, Korteweg-de Vries equation, and modified Korteweg-de Vries equation are inferred based on nonlinear analysis to describe triangular wave, soliton wave, and kink-antikink wave, corresponding to the stable, metastable, and unstable regions of autonomous vehicles flow separately. Subsequently, numerical simulation is performed, exhibiting that this novel model can strengthen the traffic flow stability of autonomous vehicles and simulate the above three different density waves of traffic flow. In addition, the numerical simulation also further verifies the accuracy of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This article mainly researches the problem of distributed finite-time coordinated path-following for under-actuated autonomous surface vehicles (ASVs) within a network swarm. Each vehicle in swarm system suffers from velocity restrictions and multiple uncertainties including parameter perturbations and time-varying environment disturbances. Based on the constructed bionic swarm pattern and potential function, the swarm velocity guidance (SVG) with self-organization and collision avoidance is developed to guide ASV surge velocities and heading angles simultaneously. A distributed observer by adding correction terms to the vehicle model is involved to identify the lumped uncertainties, and the estimations are utilized as feed-forward compensation to weaken the uncertainty impact, thus achieving high tracking precision. By using asymmetric barrier Lyapunov function, the uncertainty observer based distributed surge and heading kinetics controllers under physical restrictions are devised to guarantee that the guided signals generated by SVG are tracked within finite time. Through simulation studies of swarm path-following, it is demonstrated that the designed control approach is feasible and efficient for multiple uncertain under-actuated ASVs. • Coordinated path-following control for uncertain under-actuated autonomous surface vehicles is addressed. • A swarm velocity guidance method is employed based on the constructed swarm pattern. • Finite-time observer is developed to estimate multiple uncertainties. • Finite-time kinetic controllers using barrier Lyapunov function are proposed. [ABSTRACT FROM AUTHOR]

This paper presents the results of nonlinear model identification of an unmanned vehicle in the Gazebo simulator based on a neural network autoregressive model. The dynamic characteristics of the control object can vary significantly that complicates the problem. The training sample of movement along the spatial path was obtained at the Robotics Center of the FRC CSC RAS. The model parameters were found by the particle swarm optimization method. Using the identified model, real-time control methods were experimentally compared. For a more accurate assessment of the methods, the model was subjected to random disturbances, and the tracking path of the control object was significantly complicated. [ABSTRACT FROM AUTHOR]

Advanced driver assistant systems in vehicles, such as anti-lock brake system, electronic stability program, and traction control, heavily rely on the traction force of the wheels, which is determined by the wheels' longitudinal slip ratio. A common method used to describe this dependency is the well-known Pacejka (magic) formula. The Pacejka (magic) formula suggests the existence of a unique optimal slip ratio, denoted as λ opt , for which the maximum traction force is achieved. Although the λ opt value slightly varies with changes in the tire-to-road interface, most studies tend to neglect the variation of λ opt due to its relatively low impact on the introduced error. Instead, most studies focus on estimating the maximum friction coefficient μ max. In this paper, we address this oversimplification and propose an estimation method for λ opt. This paper introduces two novel approaches for real-time estimation of the slip ratio λ opt based on wheel dynamics. Both approaches were derived using a nonlinear twin-track model implemented in MATLAB & Simulink. The first approach uses recursive least squares, whereas the second approach employs the Unscented Kalman filter algorithm. In addition, a traction force estimator is presented because both estimators rely on either a measurement or an estimation of the traction force. To validate the performance of the proposed estimators, a high-fidelity twin-track vehicle model was initially employed. Finally, real-world experiments are presented in which the proposed algorithms are validated using an RC subscale platform. • Realtime estimation of the wheel optimal slip ratio of vehicles. • Kalman Filter-based estimator for optimal slip ratio estimation. • Recursive least squares-based estimator for optimal slip ratio estimation. • Comparison of both proposed methods on demonstration platform. [ABSTRACT FROM AUTHOR]

High-end vehicles have been equipped with several electronic control units (ECUs), which provide upgrading functions to enhance the driving experience. The controller area network (CAN) is a well-known protocol that connects these ECUs because of its modesty and efficiency. However, the CAN bus is vulnerable to various types of attacks. Although the intrusion detection system (IDS) is proposed to address the security problem of the CAN bus, most previous studies only provide alerts when attacks occur without knowing the specific type of attack. Moreover, an IDS is designed for a specific car model due to diverse car manufacturers. In this study, we proposed a novel deep learning model called supervised contrastive (SupCon) ResNet, which can handle multiple attack classification on the CAN bus. Furthermore, the model can be used to improve the performance of a limited-size dataset using a transfer learning technique. The capability of the proposed model is evaluated on two real car datasets. When tested with the Car Hacking dataset, the experiment results show that the SupCon loss reduces the overall false-negative rates of four types of attack by an average of five times compared to the vanilla cross-entropy loss. In addition, the model achieves the highest F1 score on both the vehicle models of the survival dataset by utilizing transfer learning. Finally, the model can adapt to hardware constraints in terms of memory size and running time to be deployed in real devices. [ABSTRACT FROM AUTHOR]

Transitioning from internal combustion power to passenger electric vehicles presents a highly complex problem for governments. During the past decade, there has been a slow uptake of passenger electric vehicle sales in Australia, a mere 3.6% per annum. This study explores the electric vehicle adoption policies and practices in Australia through an analysis of various stakeholder perspectives. A mixed methods approach comprising program analytics and vector-algebraic study of vehicular stakeholders was used to interrogate a range of electric vehicle stakeholders' data sets with 1, 317 stakeholder statements being analysed. The results of this study reinforce the critical importance of direct financial incentives and economic support measures valued up to A$21,000 per vehicle. In addition, the analysis exposed strong electric vehicle consumer sentiment to install up to 5800 new fast charging stations (151 km of road route per unit) by 2040 at a cost of A$1.2 Billion, while ensuring provision of vehicle model performance data and charging network information programs. Importantly, governments must show leadership with cooperative revisions to national vehicular standards, public fleet electrifications, and setting national passenger electric vehicle purchase targets. The study posits important actions that suborn passenger electric vehicle transition as a foundation for future forms of electric mobility. • There is a slow uptake of electric vehicle sales in Australia. • Analysis suggests beneficial incentives policies for EV of A$21,000 per vehicle. • Estimated fast charging network of 5800 sites requires over A$1.2 Billion investment. • Governments must install national EV standards, public EV fleets and EV purchase targets. • Passenger EV transition supports multiple applications of future electric mobility. [ABSTRACT FROM AUTHOR]

Fuel cell (FC) technology has been identified as a technically attractive solution to decarbonize the transportation sector, especially for heavy-duty vehicles. In this context, the industry and the scientific community are in need of advanced fuel cell systems (FCS) models that are able to replicate real-world operating conditions. Due to the scarcity of said models in the open literature, this study aimed to develop a comprehensive methodology to calibrate and validate multi-physics dynamic FCS models. Therefore, the key contribution of this paper is the detailed description of the calibration process for each component and the calibration order. The specific focus here was to accurately describe the behavior of the FC stack as well as the cathode, anode, and cooling circuits of the balance of plant. The model was calibrated with the aid of experimental data from a Toyota Mirai FC electric vehicle, which was predominantly retrieved from the vehicle's Controller Area Network (CAN) bus system thereby negating the need for major intrusion into the powertrain system. The validation process was deemed successful with the model being able to truthfully replicate the characteristics of the FC vehicle operated on the World-wide harmonized Light duty Test Cycle (WLTC) 3b and US06 driving cycle. The time-resolved physical parameters such as the cathode pressure, mass flow, or the FC stack temperature were captured with high fidelity, while the overall performance parameters such as the H 2 consumption in the stack and the system, and the compressor energy consumption were predicted accurately with a deviation lower than 0.47%, 1.75% and 1.89% with respect to the experimental data, respectively. • A comprehensive methodology to calibrate multi-physics dynamic FCS models is proposed. • The calibration method can use data from the CAN bus of an FCV in real operation. • The methodology is compatible with both warm and cold realistic driving. • The transient evolution of the key physical variables was predicted accurately. • The key performance indicators were predicted with a deviation lower than 2%. [ABSTRACT FROM AUTHOR]

• A refined numerical model including shield tunnel and vehicle, and the model of shield tunnel includes anti-collision wall, segment rings, bolts connecting segments, reinforcement cage inside the segment lining. • High level accuracy of various simulation cases of collision can be achieved. • Structural response of all components of shield tunnel can be obtained for analysing the failure mechanism of shield tunnel structure. • Contribution degree of all designing factors in the simulation can be quantitative evaluated. Shield tunnels account for an essential part of the urban highway system, which are more likely to encounter terrible traffic accidents during daily operation. There is a risk of collision between the errant vehicle and the shield tunnel when a traffic accident occurs. This paper establishes a refined numerical model of the shield tunnel based on the Wuhan Yangtze River Tunnel and the vehicle numerical model. Considering the four factors (vehicle weight, speed, collision angle, ground reaction coefficient), an orthogonal test scheme designing 9 simulation cases is proposed. During the collision, the dynamic responses (opening and dislocation of segment joints, stress, and plastic damage of shield tunnel lining) are studied. Results show that the anti-collision wall and segment lining are damaged to varying degrees, and bolts and reinforcement bars in the collision area reach their yield strength; meanwhile, segment joints appear to have irreversible opening and dislocation. According to the extremum difference analysis of orthogonal simulation results, vehicle weight and speed are the two leading factors in structural responses, the contribution degree of which is significantly larger than collision angle and ground reaction coefficient in structural responses. [ABSTRACT FROM AUTHOR]

This paper describes a novel method for detecting bridge damage that uses a partially instrumented vehicle fleet, each vehicle being instrumented with just one accelerometer. Importantly, no prior knowledge of the bridge or individual vehicle properties is required. Firstly, a model simplification concept is proposed to calculate the displacement under the vehicle wheel from single acceleration measurements on a half-car model. Then, optimisation is used to find individual vehicle properties using simulated noisy measurements from a fleet of vehicles. Finally, accelerations from the fleet are used to find the 'apparent profile difference', which contains bridge deflection data. The difference is found to be independent of surface profile but dependent on vehicle axle weight differences and a moving reference influence function (MRIF). When the axle spacings and the relative axle weights are reasonably consistent in a subset of the fleet, the MRIF can be simplified into a compound MRIF. Even when the MRIF and individual axle weights are not available on-site, the shape of the compound MRIF can be determined through an iterative process and used to monitor bridge health condition. Bearing damage is represented in this paper as an increase of rotational resistance of the bearings. The numerical results show that the damage severity and location can be identified. • A new drive-by fleet monitoring concept is introduced using a partially instrumented vehicle fleet without knowing individual vehicle properties. • A new concept, vehicle model simplification, is introduced to enable monitoring bridges using partially instrumented vehicles. • Vehicle wheel displacement is successfully obtained from vehicle accelerations and used in structural health monitoring. • The relation between bridge deflection under the wheel and vehicle load is modelled as a moving reference influence function. This influence function is simplified to monitor bridges. [ABSTRACT FROM AUTHOR]