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This paper presents a generic analytical framework tailored for surrogate safety measures (SSMs) that is versatile across various highway geometries, capable of encompassing vehicle dynamics of differing dimensionality and fidelity, and suitable for dynamic, real-world environments. The framework incorporates a generic vehicle movement model, accommodating a spectrum of scenarios with varying degrees of complexity and dimensionality, facilitating the estimation of future vehicle trajectory evolution. It establishes a generic mathematical criterion to denote potential collisions, characterized by the spatial overlap between a vehicle and any other entity. A collision risk is present if the collision criterion is met at any non-negative estimated future time point, with the minimum threshold representing the remaining time to collision. The framework's proficiency spans from conventional one-dimensional (1D) SSMs to extended multi-dimensional, high-fidelity SSMs. Its validity is corroborated through simulation experiments that assess the precision of the framework when linearization is performed on the vehicle movement model. The outcomes showcase remarkable accuracy in estimating vehicle trajectory evolution and the time remaining before potential collisions occur, comparing to high-accuracy numerical integration solutions. The necessity of higher-dimensional and higher-fidelity SSMs is highlighted through a comparison of conventional 1D SSMs and extended three-dimensional (3D) SSMs. The results showed that using 1D SSMs over 3D SSMs could be off by 300% for Time-to-Collision (TTC) values larger than 1.5 s and about 20% for TTC values below 1.5 s. In other words, conventional 1D SSMs can yield highly inaccurate and unreliable results when assessing collision proximity and substantially misjudge the count of conflicts with predefined threshold (e.g., below 1.5s). Furthermore, the framework's practical application is demonstrated through a case study that actively evaluates all potential conflicts, underscoring its effectiveness in dynamic, real-world traffic situations. • A generic and adaptable analytical framework for SSMs. • A generic mathematical criterion for characterizing potential collisions. • High-dimensional and high-fidelity SSMs. [ABSTRACT FROM AUTHOR]

• NG-NRMM software tools will integrate state-of-the-art 3D large soil deformation/flow terramechanics models and vehicle multibody dynamnic models in order to predict ground vehicle mobility on any given terrain. • The requirements for NG-NRMM macro-scale complex terramechanics models are presented. • A prototype software tool which satisfies the NG-NRMM requirements is presented. In part 2 of this paper, the Complex Terramechanics (CT) software tools requirements recommended by the NATO research task group RTG-248 are presented along with example simulations from a CT prototype software tool which attempts to satisfy the requirements. [ABSTRACT FROM AUTHOR]

• An overview of the spectrum of vehicle mobility terramechanics models is presented. • A literature review of macro-scale vehicle mobility terramechanics models is presented. • The requirements for NG-NRMM macro-scale terramechanics models are presented. • A prototype software tool which satisfies the NG-NRMM requirements is presented. The US army along with NATO member and partner nations' militaries need an accurate software tool for predicting ground vehicle mobility (such as speed-made-good and fuel-consumption) on world-wide terrains where military vehicles may be required to operate. Currently, the NATO Reference Mobility Model (NRMM) is the only NATO recognized tool for assessing ground vehicle mobility. NRMM was developed from the 1960s to the 1980s and relies on steady-state empirical formulas which may not be accurate for new military ground vehicles. A NATO research task group (RTG-248) was established from 2016 to 2018 to develop the NG-NRMM (next-generation NRMM) software tool requirements and an NG-NRMM prototype which uses high-fidelity "simple" or "complex" terramechanics models for the terrain/soil along with modern 3D multibody dynamics software tools for modeling the vehicle. NG-NRMM Complex Terramechanics (CT) models are those that utilize full 3D soil models capable of predicting the 3D soil reaction forces on the vehicle surfaces (including tires, tracks, legs, and under body) and the 3D flow and deformation of the soil including both elastic and plastic deformation under any 3D loading condition. In Part 1 of this paper, an overview of the full spectrum of terramechanics models from the highest fidelity to the lowest fidelity is presented along with a literature review of CT ground vehicle mobility models. [ABSTRACT FROM AUTHOR]

This paper presents a robust H ∞ output-feedback vehicle lateral motion control strategy considering network-induced delay and tire force saturation. The unavoidable time delay in the in-vehicle networks degrades the control performance, and even deteriorates the system stability. In addition, the tire lateral force suffers saturation phenomenon, which also deteriorates the control effect in extreme driving conditions. To handle the network-induced control delay and tire force saturation, a robust H ∞ controller is presented to regulate the vehicle lateral motion. An output-feedback control schema, which does not need the vehicle lateral velocity, is designed to achieve the desired control performance and reduce the cost of control system. The tire cornering stiffness uncertainty and external disturbances are also considered in the controller design to improve the robustness of the proposed controller. The comparative simulation results based on Carsim-Simulink joint simulation verify the effectiveness and robustness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Global adoption of electric vehicles (EVs) faces many challenges such as range anxiety, high cost of EVs, and inadequate charging infrastructure. EV-sharing platforms resolve such concerns by setting up an optimal configuration for charging infrastructure and optimizing the charging decisions for depleted EVs. These platforms manage the vehicles' flow to different charging stations and decide when and to what energy level the depleted vehicles should be recharged. Station-based platforms are one of the mainstream vehicle sharing systems where the customer picks-up and drops-off the vehicle at the designated stations. If a vehicle's battery energy level falls below a threshold after completing the customer trip, it is charged either partially or fully at the charging station. This study addresses various operational and strategic decisions (such as the number of chargers, vehicle repositioning, and partial charging policy) for a one-way station-based EV-sharing platform using a stylized three-stage analytical framework. We use vehicle dynamics to model the EV powertrain and regenerative braking under different traffic conditions and simulate them using AVL CRUISE™. We model the platform operations using an open queuing network and provide a mixed-integer non-linear optimization program using inputs from the queuing network and vehicle dynamics simulation. We also provide a bound-based heuristic to solve this NP-hard optimization problem. We generate various managerial insights for an efficient implementation of the partial charging policy for EV-sharing platforms. The increase in the partial charging probability (the fraction of depleted vehicles charged partially) reduces the effective charging demand, resulting in fewer chargers and a higher profit. On the other hand, if we increase the target battery energy level for partial charging, the platform's profit decreases due to higher effective charging demand dominating the benefits of lower charging frequency of vehicles. • Assess performance of EV-sharing platforms by considering vehicle dynamics. • Understand the effect of partial charging policy on platform profits. • Analyze the effect of distance among stations and parking capacity on profits. • Determine the optimal number of chargers and repositioning flows in the network. [ABSTRACT FROM AUTHOR]

The demand of accurate and real-time dynamics model has recently become critical for implementing effective vibration control in vehicles. The multibody dynamics method provides an appealing alternative for accurate modelling of closed-loop vehicles. In this work, vibration-suppression strategies for a vehicle traversing a bumpy road are proposed, which use a semi-recursive multibody model and a series of control algorithms. First, a 17-degree-of-freedom vehicle is modelled using a semi-recursive multibody method to perform real-time simulation. The vehicle multibody model is verified by using the results from a commercial software package. Second, the PID, fuzzy, and optimal control algorithms are tailored using the obtained vehicle states for vibration suppression. By applying different initial speeds and driving torques, the simulations of the vehicle traversing the bumpy road are performed. The results show that the controllers effectively suppress the vehicle's vibration, and the optimal controller has the best performance. Furthermore, the effect of mass uncertainty on vibration-suppression is discussed. Numerical experiments are performed to verify the effectiveness of the optimal control strategy. The vibration-suppression method based on the vehicle multibody model and the optimal control algorithm can be used to improve the ride comfort in worse road conditions. • An accurate semi-recursive multibody model is developed and verified for vehicle dynamics. • Three control strategies are tailored based on the multibody model to suppress the vibration. • The vibration-suppression method is studied in different speed and road conditions. • The effect of mass uncertainty on vibration-suppression is discussed. [ABSTRACT FROM AUTHOR]

A non-conventional drive-by-wireless technology for guidance and control of a redundantly actuated electric car supported by an on-board wireless network of sensors, actuators and control units is proposed in this article. Several optimization-based distributed feedforward control schemes are developed for such powertrain infrastructures. In view of the limitations of the commercial off-the-shelf wireless communication technologies and the harshness of the in-vehicle environments, a pressing design and implementation aspect, in addition to the robustness against information loss, refers to fulfilling the hard real-time computational requirements. In this work, we address such problems by introducing several distributed event-based control schemes in conjunction with adaptive scheduling at the protocol level. Hereby we obtain a simple tuning mechanism to compromise between the outcome accuracy and computation efficiency (i.e., communication traffic intensity). Using simulative evaluations, we demonstrate the viability of the proposed algorithms and illustrate the impact of external interferences in an IEEE 802.15.4 based wireless communication solution. [ABSTRACT FROM AUTHOR]

The paper presents a unique application of artificial neural networks (ANN) for replicating the predictions of Persson's flash-temperature viscoelastic friction model. This allows the efficient calculation of the coefficient of friction between a tyre and the road surface, given the operating conditions, the tread material properties, and the power spectral density of road roughness. This is important because it allows the update of friction in a computationally efficient manner and therefore presents an opportunity for carrying out vehicle simulations on several road surfaces, without the requirement for tyre testing on every surface. To this end, a method is also proposed for integrating the most successful ANN configuration with a tyre model that had its baseline parameters identified by flat-track testing on a sandpaper surface. It is shown that the ANN-enhanced tyre model operates several times faster than real-time, predicting reduced peak and asymptotic tyre forces, as expected in most cases when moving from sandpaper to regular asphalt. The enhanced tyre model is further integrated with a vehicle model to illustrate the significant effect of reduced friction in stopping distance and handling dynamics. [ABSTRACT FROM AUTHOR]

We provide an ice friction model for vehicle dynamics of a two-man bobsled which can be used for driver evaluation and in a driver-in-the-loop simulator. Longitudinal friction is modeled by combining experimental results with finite element simulations to yield a correlation between contact pressure and friction. To model lateral friction, we collect data from 44 bobsleigh runs using special sensors. Non-linear regression is used to fit a bob-specific one-track vehicle dynamics model to the data. It is applied in driving simulation and enables a novel method for bob driver evaluation. Bob drivers with various levels of experience are investigated. It shows that a similar performance of the top drivers results from different driving styles. • Development of a bob-suitable vehicle dynamics ice friction model for driving simulation. • Analysis and modeling of lateral ice friction which seems to be a research gap in the area of ice friction for wintersports. • A novel method for bob driver evaluation using real world measurement data and the proposed ice friction model. • Application of the driver evaluation method on a data set containing different drivers showing that a similar overall performance results from different driving styles. [ABSTRACT FROM AUTHOR]

A fast calculation algorithm for the wear evolution of wheel profiles is proposed and results presented with a high-speed train as an example. The dynamics simulation includes the calculation of the vehicle running on a short straight track with random irregularities and the vehicle passing various curves without track excitation. The penetration curves of wheel/rail contact are assumed as the shape of wear distribution in a contact patch, which are calculated after the update of wheel profiles and interpolated in each step of wear calculation. The total wheel wear is the sum of the wear caused by track irregularities and the wear under smooth tracks, such as the straight and curved lines. After each dynamic simulation, several loops for wear calculation and profile update are carried out to smooth the wear curve. Simulation results show that the calculated distribution and average of the wear depth and the equivalent conicity in a reprofile cycle are in good agreement with the measured results. The predicted and measured wear shapes agree reasonably well in the area near the tape circle. Moreover, the proposed method is at least 15 times faster than the traditional wear calculation method and it can consider the influences of the stochastic parameters of the wheel/rail contact. • A fast algorithm for the wheel wear evolution is proposed using the wheel/rail penetration curves as wear distribution. • The nonlinear vehicle model and simplified dynamic simulation method are used to improve the speed of wear prediction. • The stochastic wheel/rail interactions are considered as the vital part in the wheel wear prediction. • There are good agreements in the wear evaluation between simulated and measured results with a travel distance in 300k km. [ABSTRACT FROM AUTHOR]

The vehicle dynamics simulation and preview control require a dedicated vehicle model, such as multibody dynamics model. However, the multibody model has higher computational complexity which affects the response time of the vehicle controller. This issue can be alleviated by using a data-driven vehicle dynamics model due to its effective generalization and computational speed. In this work, we propose a data-driven modeling approach based on deep neural networks (DNNs) for computing and predicting the vehicle characteristics. The high-fidelity simulations of a validated vehicle multibody model are performed for data acquisition. This data is then used for training and testing the proposed model. The DNN inputs comprise the initial speed of the vehicle and the torque applied on front wheels to imitate vehicle acceleration and deceleration. The DNN outputs comprise the driving distance and the longitudinal velocity of the vehicle. The dynamics characteristics resulting from both the data-driven model and the multibody model are investigated and compared. Furthermore, the accuracy of the data-driven model is analyzed in terms of various error functions. The data-driven model is verified by using the results obtained from a commercial software package. The simulation results show that the data-driven vehicle model predicts the accurate velocity and driving distance in real-time. The data-driven model can be used for real-time simulation and preview control in autonomous vehicles. • A semi-recursive multibody formulation is used for vehicle dynamics and data acquisition. • A DNN-based approach is proposed to predict the vehicle characteristics for preview control. • Different initial speeds and torques in a large range are used for a robust DNN model. • The DNN model is verified by the results obtained from a commercial software package. [ABSTRACT FROM AUTHOR]

• A unified reconfigurable vehicle dynamics model is developed in this study. • A novel approach in vehicle modeling is presented to develop the proposed model. • The model can be configured for different four- and three-wheeled vehicles. • Wheels and actuators can readily be added or removed using the defined matrices. • Included actuators are active steering, differential braking and torque vectoring. This study presents a novel approach in vehicle modeling to provide a unified reconfigurable vehicle dynamics model. The introduced vehicle model is applicable for analysis, design, and control of a wide variety of vehicles and can be reconfigured for four- and three-wheeled vehicles. Active steering, differential braking, torque vectoring, and active camber are included in the model as the actuators. The proposed set of model equations is reconfigurable and can be adjusted for various vehicles without need for new derivation. Two matrices are defined called the corner reconfiguration matrix and actuator reconfiguration matrix that are responsible for wheel and actuator configurations of the vehicle, respectively. The model can be reconfigured by setting the diagonal elements of these two matrices. The proposed reconfigurable model is compared with different high-fidelity vehicle models in CarSim for validation. Simulation results show that four-wheeled and three-wheeled vehicle models can all be drawn from the single unified reconfigurable model. In addition, the introduced model properly matches the high-fidelity CarSim models for four-wheeled and three-wheeled vehicles. [ABSTRACT FROM AUTHOR]

• A semi-recursive vehicle model is validated in modeling and accuracy. • Constant and adaptive time-step algorithms for real-time simulation are presented. • An accurate real-time simulation of a vehicle is completed in an affordable laptop. • A benchmark problem for the multibody dynamics community is put forward. Semi-recursive formulations and their various versions have made it possible to describe complex nonlinear systems such as vehicles precisely while still solving the relevant equations of motion in real time. An optimal combination of an efficient multibody formulation and a fast numerical time integration scheme are needed to accurately simulate complex systems in real time. This paper introduces a double-step semi-recursive multibody formulation and analyzes its performance with high-order numerical time-integration algorithms for real-time simulation. The Runge-Kutta, Gill, Runge-Kutta-Fehlberg, Adams-Bashforth-Moulton, and adaptive time step Runge-Kutta numerical methods are explained and compared. Results are verified against a commercial multibody software solution. A 15-degree-of-freedom sedan vehicle model serves as a benchmark to verify the theoretical results. The results highlight the differences between the numerical algorithms and suggest appropriate approaches for a nonlinear vehicle dynamics model, particularly for cases where simulation times are long. [ABSTRACT FROM AUTHOR]