Moving Horizon Estimation (MHE) is widely used for state estimation due to its ability to effectively incorporate historical information. As a single-model approach, its accuracy depends not only on the quality of the model but also on factors such as sensor noise. However, a single model often struggles to accurately represent the complexity of real-world hybrid systems. Meanwhile, the noises are typically unknown and time-varying. In this regard, inheriting the advantages of interacting multiple model (IMM), this paper further develops an interacting multiple model-based moving horizon estimation with variational Bayesian (IMM-VBMHE). This approach enables real-time updates of noise covariance matrices (NCMs), thereby fulfilling the high-precision state estimation requirements for hybrid systems. It consists of four key aspects: interaction of combined NCMs, mode-matched VBMHE with adaptive NCMs update, model probability updating, and estimation fusion. In the traditional interaction step, the NCMs are interacted, deepening the interaction between models to improve response speed during model switching and increase the probability of selecting the most suitable model for the current system. The variational Bayesian method is applied within the MHE for each model, enabling adaptive updates of the NCMs and the calculation of initial values and state estimates at each timestep within the horizon. Subsequently, the parameter selection and numerical stability analysis of the proposed IMM-VBMHE are discussed. Numerical simulations and front vehicle state estimation experiments are implemented to verify the effectiveness of the proposed algorithm. Compared to estimation methods based on a single model, the proposed IMM-VBMHE effectively identifies the model that best matches the current system, thereby ensuring greater estimation accuracy. In comparison to multiple model estimation methods, the proposed algorithm achieves higher estimation accuracy, owing to the adaptive updating of the NCMs.

Motion prediction of surrounding vehicles in complex environments is essential for autonomous vehicle trajectory planning. Accurate motion prediction requires accurately estimating the state information of the surrounding vehicles. For this purpose, a moving horizon estimation with interacting multiple model (IMM-MHE) algorithm is first proposed here. The algorithm can match multiple vehicle maneuvers, but also fully utilizes the historical information obtained during the driving process, achieving a high estimation accuracy. Second, a moving horizon estimation with variable structure interacting multiple model (VSIMM-MHE) framework is designed. Time-domain adaptation is proposed to solve the problem that the fixed time domain of some models cannot be filled due to model activation and elimination. A new interaction method is proposed to solve the problem that models cannot interact because the starting timesteps of their time domains are different. The proposed framework reduces not only the computational burden, but also the final estimation error caused by the model not matching the current maneuver. Third, based on a model set consisting of different kinds of intention models, a VSIMM-MHE algorithm is proposed. This algorithm introduces residual information into the model classification method, reducing the dependence on the accuracy of the model probabilities. It can not only accurately estimate the state information of surrounding vehicles in a complex environment, but also identify the model that best matches the current maneuver and effectively predict the motion trajectories of surrounding vehicles through model probabilities. Finally, joint simulation with SCANeR studio, Carsim and Simulink and hardware-in-the-loop experiment demonstrate the effectiveness of not only the two proposed estimation algorithms but also the motion prediction of surrounding vehicles using the model probabilities in the VSIMM-MHE algorithm.

Connectivity technologies in intelligent transportation systems offer unprecedented opportunities to enhance mobility, fuel economy, and safety for automotive systems. However, the uncertain driving behavior of surrounding vehicles in real-world traffic scenarios can significantly undermine these benefits. To tackle this challenge, this article develops a stochastic predictive-adaptive cruise control (P-ACC) system that effectively addresses uncertainties and automatically adapts to various driving scenarios. The proposed system employs a Gaussian process (GP)-based velocity predictor as its foundation, accurately capturing the driving dynamics of the preceding vehicle while accounting for prediction uncertainty using variances. The real-time feasibility is assessed in a dSPACE rapid prototyping system. In addition, the developed stochastic-model predictive control (S-MPC) approach incorporates the predicted velocity variance into the probabilistic chance constraints, conservatively narrowing the optimization space of the velocity planning domain, thereby enabling more reliable control. To further enhance the system’s performance in adapting to different driving conditions, a scenario-based parameter self-learning (PSL) technique is introduced in the S-MPC controller, utilizing Bayesian optimization (BO). Finally, the performance of the proposed controller is comprehensively evaluated by leveraging a high-fidelity simulator and on-board actual vehicle testing data. Simulation results demonstrate that the proposed method achieved a boost in tracking performance and driving comfort while maintaining fuel-saving benefits.

Traffic sign recognition is crucial for enhancing the safety and efficiency of intelligent transportation systems (ITS). This paper proposes a hybrid structure named DK-former, a lightweight and robust deep kernel Gaussian process transformer network, aiming to tackle non-linear small samples, high model parameters, and environmental variations, providing a distinctive solution that enhances robustness and adaptability in intelligent transportation systems. The whole structure is trained end-to-end based on the Bayesian framework. First, introducing convolutional random Fourier features facilitates effective non-linear sample feature mapping, capturing complex patterns inherent in traffic signs and enhancing computational efficiency. Second, the transformer model refines feature representations with its powerful self-attention mechanism to capture the global relationship between traffic sign pixels. Furthermore, the hybrid structure enhances the generalization capability of the model, allowing it to adapt to diverse scenarios, such as varying lighting conditions and weather fluctuations commonly encountered in real-world traffic environments. Extensive experiments have been conducted to validate the effectiveness of the proposed DK-former for traffic sign recognition. The model has achieved an outstanding recognition accuracy of 99.09% on the German Traffic Sign Recognition Benchmark (GTSRB) dataset, superior to current state-of-the-art deep kernel learning traffic sign models with only 8.3 million parameters. Experiments on Indian and Chinese datasets comprehensively demonstrate the generalization of the model across different geographies and environments. The code is publicly available at:https://github.com/w-tingting/DK-former.

Under extreme conditions, such as low friction surfaces, violent steering and urgent acceleration/deceleration, vehicle states change rapidly and are influenced by nonlinear and coupled vehicle dynamics. To improve vehicle stability under extreme conditions, a hierarchical control strategy is proposed for DDEVs. In the upper layer controller, a combined-slip tire model is adopted to improve the model accuracy under extreme conditions. A nonlinear model predictive control based controller is then designed to generate the desired tire slip ratios with the main objectives of tracking the desired yaw rate and suppressing the lateral velocity and tire slip ratios. In the lower layer controller, the disturbance on the driver’s torque requirement, which is disregarded by existing studies, is taken into account. Next, a linear predictive controller is designed to track the desired tire slip ratios by adjusting the motor torques. To improve the computational efficiency of the nonlinear predictive controller, a PMP-based, fast solving algorithm is proposed. The effectiveness of the proposed solving algorithm is checked by comparing the control performance with IPOPT. The proposed control strategy is evaluated by a series of HIL experiments. The HIL results show better performance in overall stability improvement and minimize the disturbance on the driver’s torque requirement.

Under extreme conditions, vehicle states change rapidly between stable and unstable, resulting in dynamic requirements for the vehicle’s overall safety stability. Simultaneously, the coupled nonlinear characteristics of vehicle dynamics cannot be ignored in controller design. To address the above problems and improve vehicle longitudinal and lateral stability integrally, an envelope-based model predictive control (MPC) strategy with dynamic objectives is proposed for four-wheel independent motor-drive electric vehicles (4WIMD EVs). First, according to the current driving behavior and the collected road information, the envelope control regions concerning vehicle side-slip angle and yaw rate are obtained online, and divided into stable, critically stable, and instable regions with different safety requirements. Then, the safety dynamic requirements are constructed in the designed MPC-based control structure. A nonlinear vehicle dynamics model with a combined-slip tire model, which integrates the longitudinal and lateral dynamics, is utilized to predict vehicle states. The switching of requirements is reflected in the variation of weighting factors and constraint values. Finally, CarSim and Matlab/Simulink co-simulation, and hardware-in-the-loop simulation test results show better satisfactory performance in improving overall vehicle stability under extreme driving conditions.

In this paper, a data-driven predictive controller is designed for the start-up process of vehicles with automated manual transmissions (AMTs). It is obtained directly from the input-output data of a driveline simulation model constructed by the commercial software AMESim. In order to obtain offset-free control for the reference input, the predictor equation is gained with incremental inputs and outputs. Because of the physical characteristics, the input and output constraints are considered explicitly in the problem formulation. The contradictory requirements of less friction losses and less driveline shock are included in the objective function. The designed controller is tested under nominal conditions and changed conditions. The simulation results show that, during the start-up process, the AMT clutch with the proposed controller works very well, and the process meets the control objectives: fast clutch lockup time, small friction losses, and the preservation of driver comfort, i.e., smooth acceleration of the vehicle. At the same time, the closed-loop system has the ability to reject uncertainties, such as the vehicle mass and road grade.