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This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seamless transition between different vehicle models is attained by observing the key parameters characterizing the lateral motion of the vehicle. The tests conducted using a prototype vehicle on a snow-covered track confirm the effectiveness and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

For vehicle state estimation, the conventional Kalman filter performs well under the Gaussian assumption, but in the real non-Gaussian situation (especially when the noise is non-Gaussian heavy-tailed), it shows poor accuracy and robustness. In this paper, an extended Kalman filter (EKF) algorithm based on the maximum correntropy criterion (MCC) is proposed (MCCEKF), and a lateral-longitudinal coupled vehicle model is established, while a state observer containing the yaw rate, vehicle sideslip angle, and longitudinal vehicle speed is designed using the easily available measurement information of on-board sensors. After analyzing the complexity of the proposed algorithm, the new algorithm is verified on the Simulink/CarSim simulation experimental platform by Double Lane Change and Sine Sweep Steer Torque Input maneuver. Experimental results show that the MCC-based EKF algorithm has stronger robustness and better estimation accuracy than the traditional EKF algorithm in the case of non-Gaussian noise, and the MCCEKF is more applicable for vehicle state estimation in practical situations. [ABSTRACT FROM AUTHOR]

This article deals with the control chassis dynamics of a light electric vehicle. In the frame of Global Chassis Control, it proposes a hierarchical control approach focuses on compensating the chassis dynamics against driver disturbances. It is based on a supervisory structure consists of two main levels, a global controller for regulating chassis variables and a local controller to each suspension system. Both controllers are designed using a fractional-order robust controller, namely CRONE controller, to provide an optimized solution for the performance-robustness tradeoff. The main objective is to improve ride comfort for passengers while respecting road holding and handling criteria. In contrast to other studies, random road disturbances are considered to investigate the limits of the proposed approach. Analysis in frequency and time domain has been done to evaluate the performance and robustness of the designed controller. Simulation results based on a full nonlinear 14 degree of freedom vehicle model show that the proposed strategy can effectively improve the ride quality in the field of interest where a significant performance in driver disturbance rejection is reported. [ABSTRACT FROM AUTHOR]

Longitudinal velocity, lateral velocity, and front wheel steering angle are crucial states for vehicle active safety features. However, direct measurement of these variables requires expensive measurement instruments and can be easily affected by vehicle nonlinear dynamics, outliers and noise pollution. Moreover, unknown inputs bring great challenges to their accurate estimation. Therefore, this paper develops a novel robust unscented M-estimation-based filter (RUMF) for vehicle state estimation with unknown driver steering torque. The nonlinear system model is constructed based on the vehicle dynamics model. Unscented transformation (UT) and statistical linearization are implemented to transform the nonlinear process and measurement function into a linear-like regression form with data redundancy to achieve outlier suppression. Then, an M-estimation-based iterated algorithm is designed to address process uncertainty and innovation and observation outliers for robust vehicle state estimation. The iteratively reweighted least-squares (IRLS) method based on the M-estimation methodology is utilized for unknown input estimation. Simulations and experimental results compared with extended Kalman filter (EKF) and particle filter (PF) have verified the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a novel estimation and identification approach for lateral vehicle dynamics. The algorithm leverages on a Linear Fraction Transform (LFT) reformulation of vehicle and tyre models, allowing for a simple and computationally efficient inclusion of complex and nonlinear dynamic models, like, for example, two-wheels, four-wheels or single-track as vehicle model, and Pacejka, brush or Fiala as tyre model. As a result, this technique can be easily adopted in the development of an online identification system, able to run on a standard embedded device, implementing a flexible identification procedure that can handle different driving conditions, up to the limits of handling, different vehicle modelling approaches, and different input measurements. Experimental results demonstrate the effectiveness of the proposal, either in a persistent excitation and in a non-persistent excitation scenario. [ABSTRACT FROM AUTHOR]

Path planning is a critical part for improving the driving safety and driver comfort of autonomous vehicles (AVs), especially in complex maneuvering conditions. In addition, different drivers have different preferences for AVs, thus, how to provide personalized trajectories for different drivers is a vital issue for AVs. The collision-free path planning problem in conditions with large road curvatures is investigated in this paper, with the consideration of environmental safety constraints, drivers’ comfort, vehicle actuator constraints, etc. Firstly, a Driver-Vehicle-Road (DVR) system is established based on the combination of the kinematic vehicle model and the two-point visual preview driver model, such that the driver's individual handling characteristics can be considered in the controller. The kinematic vehicle model is modified to have the similar understeering characteristics with those of the nonlinear full car models, and then the proposed DVR system can satisfy different groups of drivers and cars. Secondly, for environmental constraints, a new artificial potential field (APF) method is proposed, which can form a banana-shaped 3-D dangerous imaginary mountain and a lane boundary cliff suitable for arbitrary curvature roads to generate a collision-free evasive path. Finally, the Linear-Time-Varying (LTV) model predictive control (MPC) method is adopted to design the path planner. The CarSim-Simulink joint simulation illustrates that with the proposed planner, the host vehicle is capable of avoiding obstacles with a safer and more comfortable maneuver on large curvature roads. And the proposed path planner can provide individually safe trajectories for different drivers with good maneuverability. [ABSTRACT FROM AUTHOR]

In vehicle trajectory prediction, the difficulty in modeling the interaction relationship between vehicles lies in constructing the interaction structure between the vehicles in the traffic scene. Majority of existing models only focus on the interaction between the historical trajectory of the vehicle and the surrounding vehicles in the spatial domain, and do not pay attention to the interaction between the vehicle and the non-Euclidean correlation structure (graph structure) that exists in the environment. In order to overcome the deficiencies in the existing models, this paper proposes the Environment-Attention Network model (EA-Net) to obtain the full interactive information between the vehicle and its driving environment. In the proposed model, a new type of parallel structure consisting of Graph Attention network (GAT) and Convolutional social pooling containing Squeeze-and-Extraction mechanism (SE-CS), is constructed as the environmental feature extraction module and embedded in LSTM encoder-decoder. This structure solves the limitation of the dimension and structure influence when constructing the interaction model between the vehicle and the surrounding environment, making the extracted feature information comprehensive and effective. The prediction accuracy of the model with the RMSE loss function is tested on two public datasets– NGSIM and highD, and compared with several state-of-the-art trajectory prediction algorithm models. The results show that the prediction accuracy of the proposed Environment-Attention Network in the two datasets is more than 20% higher than that of the single structure model, which indicates that the proposed model proposed has superior performance and better adaptability to different traffic environments compared with the existing models. [ABSTRACT FROM AUTHOR]

In a typical traffic scenario, autonomous vehicles are required to share the road with other road participants, e.g., human driven vehicles, pedestrians, etc. To successfully navigate the traffic, an adaptive robust level-k framework can be used by the autonomous agents to categorize the agents based on their depth of strategic thought and act accordingly. However, mismatch between the vehicle dynamics and its predictions, and improper classification of the agents can lead to undesirable behavior or collision. Robust approaches can handle the uncertainties, however, might result in a conservative behavior of the autonomous vehicle. This paper proposes an adaptive robust decision making strategy for autonomous vehicles to handle model mismatches in the prediction model while utilizing the confidence of the driver behavior to obtain less conservative actions. The effectiveness of the proposed approach is validated for a lane change maneuver in a highway driving scenario. [ABSTRACT FROM AUTHOR]

In this study, a nonlinear predictive control method is developed for the active steering control of a sport utility vehicle. The method is tested on a nonlinear mathematical model of an 11-degree-of-freedom vehicle. The system performance is evaluated by considering that the control law must keep the actual yaw rate close to the desired yaw rate and minimizing the vertical load changes at each wheel. The latter is proposed for this work. The vertical load changes play an important role in the dynamics and the stability of the system. The effectiveness of the control method is demonstrated through numerical simulation by using a vehicle model that includes three case studies: rapid lane change at low and high velocities and the fishhook manoeuvre. The results show that the stability of the vehicle is maintained and its rollover propensity is decreased. In addition, the proposed controller is compared with a well-known linear model predictive controller. [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]

The aim of this work is to present a novel accurate estimator for vehicle dynamics. Following the multibody dynamics approach, a vehicle can be modeled with a high level of detail including non-linear dynamics. As a consequence, a rich simulation data-set is available for engineering analysis, richer than with vehicle analytical models. The proposed novel estimator is a new form of a dual Kalman filter. The first filter uses an indirect extended Kalman filter (i.e. the errorEKF) incorporating force estimation and using a vehicle multibody model. The second filter is an unscented Kalman filter (UKF) used to increase the accuracy of the errorEKF by estimating uncertain modeling parameters such as the mass of the vehicle and the tire-road friction coefficient. The performance of the proposed state-parameter-input (SPI) observer is tested in a simulation environment. The performance of the observer is demonstrated using two maneuvers, out of which one covers aggressive driving conditions. The results show that the new observer estimates with high accuracy the variables of interest for vehicle dynamics, such as the tire forces. [ABSTRACT FROM AUTHOR]

• The real-time vehicle body domain motion-mode identification method is developed. • The developed method requires less inputs and the inputs are easily approachable. • The developed method drives practical application of the conventional MEM theory. • Mode energy ratio percentage error of the developed method is under 5%. Vehicle body dominant motion-modes, including bounce, roll, and pitch, are highly related to vehicle ride comfort and handling performance, while are generally coupled. Real-time identification of dominant motion-mode with the three motion-modes benefits control on active suspensions. The conventional (7-DOF vehicle model based) motion-mode energy method (MEM) for motion-mode identification needs 14 vehicular states and 4 road information, blocking its real vehicle application. This study simplifies the MEM on a 3-DOF body model to identify the dominant motion-mode real-timely. This method requires less and easily accessible inputs compared with conventional methods in real time. This consequently enables practical application of instant dominant motion-mode identification. The proposed method gives a new way to decouple suspension deflection into each body dominant motion-mode, and only uses easily accessible suspension deflection, vehicle body angle, angle rate and vertical motion of a vehicle. Moreover, the relative error of mode energy ratio between the developed method and the traditional method is under 5% in simulation, which indicates high accuracy of the developed method. The real-time ability and reliability of the developed method are experimentally proved under different driving conditions. The experiment results confirm that the proposed method is effective regarding identification of the dominant vehicle motion-mode on typical driving scenarios. This method helps design of vehicle dynamic control strategy, and therefore improves active suspension performance. [ABSTRACT FROM AUTHOR]

Many challenges still need to be overcome in the context of autonomous vehicles. These vehicles would be over-actuated and are expected to perform coupled maneuvers. In this paper, we first discuss the development of a global coupled vehicle model, and then we outline the control strategy that we believe should be applied in the context of over-actuated vehicles. A gain-scheduled H ∞ controller and an optimization-based Control Allocation algorithms are proposed. High-fidelity co-simulation results show the efficiency of the proposed control logic and the new possibilities that could offer. We expect that both car manufacturers and equipment suppliers would join forces to develop and standardize the proposed control architecture for future passenger cars. [ABSTRACT FROM AUTHOR]