Reinforcement learning (RL) faces challenges in trajectory planning for urban automated driving due to the poor convergence of RL and the difficulty in designing reward functions. Consequently, few RL-based trajectory planning methods can achieve performance comparable to that of imitation learning-based methods. The convergence problem is alleviated by combining RL with supervised learning. However, most existing approaches only reason one step ahead and lack the capability to plan for multiple future steps. Besides, although inverse reinforcement learning holds promise for solving the reward function design issue, existing methods for automated driving impose a linear structure assumption on reward functions, making them difficult to apply to urban automated driving. In light of these challenges, this paper proposes a novel RL-based trajectory planning method that integrates RL with imitation learning to enable multi-step planning. Furthermore, a transformer-based Bayesian reward function is developed, providing effective reward signals for RL in urban scenarios. Moreover, a hybrid-driven trajectory planning framework is proposed to enhance safety and interpretability. The proposed methods are validated on the large-scale real-world urban automated driving nuPlan dataset. Evaluated using closed-loop metrics, the results demonstrate that the proposed method significantly outperform the baseline employing the identical policy model structure and achieves competitive performance compared to the state-of-the-art method. The code is available at https://github.com/Zigned/nuplan_zigned .

Vehicle dynamics exhibit nonlinear behavior during limit-handling maneuvers and in the presence of environmental disturbances, which complicates motion control under uncertainty. Existing robust MPC schemes can provide feasibility and stability properties under bounded-uncertainty assumptions, but fixed worst-case tubes may be conservative when tire--road conditions change. Learning-enhanced MPC can improve nominal prediction accuracy, but directly inserting learned residual models into the prediction rollout may propagate biased residual predictions under closed-loop covariate shift. To address these issues, we develop a learning-scheduled tube LPV-MPC framework based on nominal--residual separation. CKF-estimated lateral tire forces are converted into recursively smoothed front/rear cornering stiffnesses, which update the LPV nominal model as physically meaningful scheduling variables. A robust polytopic LPV-MPC feedback law is then synthesized offline through a set of LMI conditions, and the online gain is obtained by trilinear interpolation. In parallel, a BNN quantifies the residual mean together with aleatoric and epistemic uncertainties; the resulting bounded residual estimate is used to schedule the robust tube rather than to correct the MPC prediction rollout. RT-HIL tests over 150 stochastic scenarios support the expected tracking and robustness benefits within the tested envelope. Compared with a robust Tube-MPC baseline, the proposed method reduces lateral tracking error by about 63.5% and 68.3% in non-stress and stress tests, respectively, while keeping the average computation time below the 50-ms control period. A supplementary real-vehicle lane-change test under medium adhesion further shows that the controller can be deployed on the physical platform; in this single test, it reduces the lateral and heading-error RMSEs relative to Tube-MPC.%% Remove this line from your manuscript.

Vehicle suspension systems are crucial for ensuring passenger comfort, vehicle stability, and overall safety. Magnetorheological (MR) suspension systems have garnered significant attention in the industry due to their fast response and broad adjustability. However, magnetorheological dampers (MRDs) exhibit substantial nonlinear saturation effects, including hysteresis, bi-viscosity, and the Stribeck effect. These nonlinearities limit the full potential of MRD performance and present significant challenges in MRD controller design. In this paper, a load-dependent multi-model affine H∞ robust controller (LDMA-H∞) is proposed to improve the performance of MR suspension system and overcome its nonlinear constraints within MRD. A novel multi-model segmentation approach using a Takagi–Sugeno fuzzy neural network is developed to transform the nonlinear constraints into manageable linear subsystem constraints. An affine H∞ robust controller based on a multi-model Lyapunov function is then designed. The affine term is incorporated into the feedback control law to address damping constraint asymmetry. Furthermore, the multi-model Lyapunov functions ensure system stability under varying loads, reducing the control conservatism. Finally, a real vehicle experiment is completed to identify the effectiveness of the proposed control method. The results show that the LDMA-H∞ controller effectively reduces control conservatism and enhances the ride comfort and handling stability under varying loads for vehicles.

With the rapid advancement of vehicle electrification and intelligence, automotive functions are progressively expanding, leading to the evolution of automotive electronic and electrical architecture towards a centralized approach. This shift necessitates higher demands for system software in terms of reliability, scalability, portability, and development efficiency. Building upon the AUTOSAR standard for automotive open system architecture, this study presents a designed system software architecture for large bus electronic parking brake systems (EPB). Through module division and software layering using a bottom-up development method, agile development of multifunctional EPB modules is achieved. Leveraging automatic code generation technology, the software is integrated into the Renesas RH850 hardware platform to validate the application software functionality of the electronic parking brake system through real-world vehicle experiments. The experimental results demonstrate that the developed control software effectively realizes EPB functionality while meeting national standards and design requirements. Additionally, our proposed software development methodology enables decoupling between software and hardware components facilitating convenient upgrades, transplantation, and maintenance; thus confirming its effectiveness and feasibility in advancing the development process of electronic parking systems. These findings hold significant implications for achieving cost-effective and efficient automotive software development.

Accurate estimation of the road adhesion coefficient (RAC) is essential for enhancing vehicle active safety control and extending dynamic performance boundaries. However, existing RAC estimation methods suffer from reduced accuracy under challenging conditions such as non-Gaussian noise, insufficient excitation, and low-quality image inputs. To address these challenges, a novel cross-domain fusion-based RAC estimation method is proposed, which fuses image-based perception with vehicle dynamics information. In the autonomous driving domain, a lightweight classification network based on EfficientNetV2-S is developed using a finely annotated 15-class road surface dataset, providing forward-looking RAC range predictions and confidence scores with Gaussian-weighted temporal smoothing. In the chassis domain, a square-root cubature Kalman filter enhanced by the Maximum Mixture Correntropy Criterion (MMCC-SCKF) is designed to improve the robustness of RAC estimation based on dynamic models under complex noise conditions. Meanwhile, a multi-level fusion strategy, comprising temporal-spatial synchronization, model-level fusion, and decision-level fusion, is introduced to leverage the complementary advantages of visual priors and dynamics-based inference. Extensive simulations and real-world tests conducted at a national standard proving ground demonstrate that the proposed method achieves high accuracy and robustness under complex road conditions and can effectively adapt to abrupt changes in road adhesion during transitions. Compared to conventional SCKF approaches, the fusion-based method achieves up to an 84.7% reduction in RMSE and exhibits faster convergence without requiring sufficient tire excitation. The results validate the effectiveness of the proposed cross-domain fusion-based method in enabling reliable, real-time RAC estimation for intelligent vehicle applications.

In the field of Active Suspension Control (ASC), traditional control algorithms often lack adaptability and require considerable time and effort for parameter tuning. Artificial Intelligence (AI) technologies offer a promising solution to address these limitations. However, most Deep Reinforcement Learning (DRL) algorithms in ASC are based on passive, reactive responses, which will cause the decline in control performance when encountering transient road surface changes. Hence this study proposes a Preview information-based DRL (P-DRL) for ASC. First, the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm is employed to train the neural controller, with a reward function defined according to the system's expected final state. Then, A new preview experience replay buffer is introduced to store system states at future time steps, and it is combined with the existing experience replay buffer to generate coupled predictive control states. Simulation results validate the proposed algorithm, showing a 70.29% improvement in spring acceleration.

Conventional active distribution network distributed generation planning methods mainly use IL (Interruptible Load) response method to complete the two-level planning solution, which is vulnerable to the dynamic change of planning constraints, resulting in poor planning effect. Therefore, A novel approach for planning distributed generation in active distribution networks needs to be formulated, leveraging an enhanced particle swarm optimization technique. Specifically, this involves constructing a model for distributed generation planning within active distribution networks and subsequently designing an algorithm for this purpose using the improved particle swarm optimization method. The implementation process is then outlined to ultimately achieve the planning objectives. Experimental findings demonstrate that the proposed planning method, grounded in enhanced particle swarm optimization, exhibits favorable performance indicators. This confirms that the designed approach offers superior planning effectiveness, reliability, and holds practical application value. Furthermore, it contributes significantly to reducing the operational costs of active distribution networks, thereby fulfilling the power supply and distribution demands of diverse regions.

As a core parameter within vehicle stability control systems, the vehicle sideslip angle (VSA) significantly influences both maneuverability and operational safety. However, the prevailing assumption in current research is that measurement noise is Gaussian distributed. In reality, factors such as sensor degradation, measurement anomalies, and external disturbances often result in heavy-tailed, skewed, and time-varying non-Gaussian noise characteristics. Under such conditions, conventional filters tend to diverge, resulting in significant degradation of estimation accuracy. To overcome these challenges, this study proposes a square-root cubature Kalman filter enhanced with the maximum mixture correntropy criterion (MMCC-SCKF) to improve vehicle speed and VSA estimation under non-Gaussian noise conditions. The proposed method maps observation data into a high-dimensional feature space via kernel functions, emphasizing samples with small errors near the mean while assigning lower weights to outliers or samples with large errors far from the mean. This method reconstructs the measurement noise covariance matrix and efficiently mitigates the impact of non-Gaussian noise on system performance. The hardware-in-the-loop (HIL) testing results under different road adhesion conditions demonstrate that the proposed algorithm maintains superior VSA estimation accuracy despite the presence of non-Gaussian noise. Specifically, compared to the SCKF algorithm, the proposed method reduces the RMSE of the VSA by 62.50% on high-adhesion roads and by 66.67% on low-adhesion roads. The outcomes demonstrate that the proposed algorithm achieves notable gains in estimation precision, robustness, and dependability.

The accurate estimation of vehicle mass is critical for optimizing energy-efficient driving strategies and ensuring chassis active safety control in new energy vehicles. However, while data-driven methods achieve high accuracy in training scenarios, they suffer from substantial performance degradation when encountering distribution shifts in input data. To address these challenges, this study proposes a novel deep neural network-based vehicle mass estimation model (tau-DNN) that incorporates input data confidence levels. The confidence levels are quantified by calculating the Euclidean distance between the input data and the clustering centers of the training data, which allows for weighted outputs of the estimation results. To enhance the model's ability to extract features from input sequences, this study integrates the multi-head attention mechanism with convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks, thereby achieving more accurate vehicle mass estimation. Finally, 14 loading conditions and six driving conditions were established to train the tau-DNN model using data collected from heavy-duty vehicles (HDVs) in real-world environments. Offline testing using the self-built dataset and online real-time verification demonstrate that the tau-DNN model can effectively mitigate error peaks when the confidence of input data is low, consistently maintaining the estimated error below 10 % and controlling the RMSE to within 1.09 t. Compared with the existing LSTM and cubature Kalman filter (CKF) methods, the proposed algorithm demonstrates superior estimation performance, exhibiting higher estimation accuracy, stability, and generalization capability.

A critical challenge in vehicle state estimation is managing unknown and non-Gaussian noise characteristics resulting from sensor degradation, measurement abnormalities, model inaccuracies, and external interference. However, existing research has problems with filter divergence and insufficient accuracy and robustness. To this end, we propose a robust adaptive filtering estimator, the variational Bayesian maximum correntropy square-root cubature Kalman filter (VBMCSCKF), for the joint estimation of vehicle sideslip angle and tire-road longitudinal/lateral forces. The proposed VBMCSCKF utilizes onboard inertial measurement unit (IMU) data to adaptively estimate noise covariance matrices via the variational Bayesian (VB) method and introduces the maximum correntropy criterion (MCC) for further correction to enhance its accuracy and robustness under unknown and abnormal noise. These features are embedded within the square-root cubature Kalman filter (SCKF) framework, and the estimator for tire force and sideslip angle is developed based on a zero-first derivative and vehicle model. Numerical simulations and Monte Carlo simulation of a double-lane change maneuver under varying speeds and road adhesion conditions demonstrate impressive estimation results for tire-road force and the vehicle sideslip angle. It effectively adapts to time-varying IMU noise and resists abnormal IMU measurements. Compared to MCSCKF and VBSCKF, the performance of VBMCSCKF improves by 44.0% and 28.5%, respectively. Notably, the increase in computation time for VBMCSCKF compared to the standard SCKF is almost negligible.

With the rapid development of intelligent automobiles, the chassis serves as an essential carrier of intelligence and a necessary condition for achieving high-level autonomous driving. Its electronic and electrical architecture is evolving toward centralized development, which is also significantly increasing the complexity of system functions. Meanwhile, with the integration of more sensors and an increase in data volume, stricter requirements have been placed on software scalability, portability, and maintainability. This paper presents a system software design and implementation approach for the chassis domain controller by integrating the AUTOSAR standard with model-based design (MBD). The developed software is subsequently deployed on a domain controller hardware platform based on the Renesas u2a16 chip for integrated testing. The software algorithm development, model-in-the-loop (MIL) testing, hardware-in-the-loop (HIL) testing, and real vehicle calibration processes are described in detail, focusing on the roll stability control software component in the chassis domain controller. A detailed definition of the toolchain for each development stage is also provided. The feasibility and effectiveness of the proposed chassis domain controller software system development process, based on the combination of the AUTOSAR standard and model-based design, are validated through test results. This method effectively achieves software–hardware decoupling and enhances software scalability, module reusability, and reliability, which is of great significance for improving the efficiency and iteration of chassis domain controller development.

Accurate mass estimation is crucial for improving vehicle active safety control performance. In the context of tractor-semitrailer systems, the precise estimation of mass presents a formidable challenge. This challenge is rooted in traditional methods affected by model uncertainty, model mismatch, and insufficient training data; furthermore, there is also a lack of algorithm generalization to account for real-world traffic scenarios. To address these challenges, a hybrid algorithm for vehicle mass estimation based on bidirectional long short-term memory (BiLSTM) networks and square-root cubature Kalman filter (SCKF) is proposed in this study. Driven by vehicle time series data, the vehicle mass estimator based on the BiLSTM network is constructed to address model uncertainty. To enhance both estimation accuracy and robustness, the hybrid BiLSTM-SCKF method is constructed by incorporating the BiLSTM mass estimation as the initial state value and measured value in the SCKF algorithm while also formulating a fusion rule. The experimental results under real environments show that the hybrid BiLSTM-SCKF algorithm outperforms single algorithms, particularly under medium and low loads, and the estimated error remains consistently below 10% across all working conditions. Additionally, this proposed method exhibits adaptability to various types of semitrailers, effectively enhancing algorithmic accuracy, reliability, robustness, and interpretability.

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles (AVs). The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer. To address this issue, this study proposes a safe motion planning and control (SMPAC) framework for AVs. For the control layer, a dynamic model including multi-dimensional uncertainties is established. A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set. A flexible tube with varying cross-sections is constructed to reduce the controller conservatism. For the planning layer, a concept of safety sets, representing the geometric boundaries of the ego vehicle and obstacles under uncertainties, is proposed. The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories. An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles. A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC. The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

The application of deep reinforcement learning in driving policy learning for automated vehicles is limited by the difficulty of designing reward functions. Most existing inverse reinforcement learning (IRL) methods make a structure assumption on the reward function and omit the uncertainty of neural networks. In view of this, this paper proposes a novel deep Bayesian IRL method that addresses both the reward function designing and the uncertainty-measuring issues by learning an approximate posterior distribution over the reward function. Furthermore, we propose to train uncertainty-aware human-like driving policies by maximising the predicted reward and penalising its uncertainty. Finally, the proposed methods were validated in simulated highway driving scenarios. The results show that AVRL models uncertainty and learns reward functions significantly outperforming the existing IRL method applied in automated driving. It was also found that penalising the uncertainty of the reward function during policy training improves the success rate and human likeness of the learned policy.

Road roughness significantly impacts vehicle safety and dynamic responses. For nonlinear suspension systems, the nonlinear characteristics often make it challenging for estimators to identify the actual road roughness accurately. This paper proposes a hybrid road roughness identification algorithm based on nonlinear auto-regressive with exogenous inputs (NARX) and a process noise adaptive square root cubature Kalman filter (PASCKF) to address this issue. Driven by vehicle acceleration data, an NARX-based road roughness identification system is constructed to mitigate the model uncertainties. Furthermore, a hybrid strategy is proposed. On the one hand, the accurate road roughness estimated by the NARX is converted into process noise covariance, enhancing the estimator’s accuracy and convergence rate. Another switching strategy is proposed to optimize the non-convergence issues of the PASCKF. Finally, simulation and actual vehicle experiment data demonstrate that this approach offers superior identification accuracy and adaptability compared to the standalone SCKF algorithm.

The characterization and understanding of random vibration response and fatigue life of solar panels under acoustic and vibration loads is critical to the qualification of solar panels to accomplish its task in space. In this paper, numerical model of a solar panel composed of composite materials is established by means of finite element and boundary element methods. The structural responses of the solar panel under signal acoustic load or signal random vibration load are calculated by using modal superposition method respectively. The numerical results are very in agreement with experimental results. Furthermore, the rain flow cycle counting method and the conventional S-N curves are used to estimate the fatigue life of the solar panel on the danger location. The solar panel is mounted to shaker and the bottom is fixed when it is subjected to random vibration excitation. In the opposite, the solar panel is free when it is subjected to acoustic load. Finally, the solar panel is subjected to two kinds of loads together. An equivalent fatigue life is calculated and analysed. The results show the fatigue life of the solar panel when it is subjected to vibrio-acoustic hybrid load is shorter than that when it is subjected to only vibration or acoustic load. The fluid-structure coupling in vibrio-acoustic hybrid load should be main reason to speed fatigue damage of the solar panel.

Strong nonlinearities and saturation constraints of magnetorheological (MR) dampers pose significant challenges for controller design. Traditional control methods relying on forced saturation mechanisms and 'clipped' schema often deteriorate suspension system performance. In this paper, a multi-model piecewise affine H∞ (MPA-H∞) robust controller considering the nonlinear damping constraints of MR dampers is proposed. Based on analysis of the nonlinearities of MR dampers, a multi-model piecewise approach is employed to partition and linearize damping constraint boundaries, transforming complex nonlinear constraints into manageable linear damping force constraints within subsystems. Subsequently, an H∞ robust controller with affine terms is designed, converting asymmetric constraints of the segmented multi-model system into linear matrix inequalities, enabling effective application in real vehicle control systems. Finally, a passenger car is selected to build a real vehicle test platform, and the real vehicle road test is carried out on the urban road surface. Simulation and real vehicle experiment results demonstrate that the MPA-H∞ robust controller maintains stable control effectiveness across different vehicle speeds, enhancing driving comfort and handling stability while satisfying nonlinear damping constraints.

This paper proposes a novel integrated path following control scheme for a 4-Wheel Independent Drive (4WID) autonomous vehicle that can adaptively change its mechanism according to the driving conditions. The proposed integrated system handles the lateral steering controller, longitudinal speed, and yaw moment controls considering tire force capacity of each corner. For the lateral controller, the cornering stiffness uncertainties and the transient performance are considered and combined into an H∞ robust controller based on linear matrix inequality (LMI) theory. A super-twisting sliding mode controller (STSMC) based longitudinal controller is designed to deal with disturbances and suppress chattering. When encountering extreme conditions, the active yaw moment controller with hierarchical structure is adaptively activated to prevent large deviation from the reference path and maintain the stability of vehicle. For the tire force allocation, an optimization algorithm is proposed, which has flexible equality constraints to coordinate the longitudinal and lateral motions according to the driving conditions. Simulations based on Carsim-Simulink co-simulation platform show that the proposed method is effective and has excellent performance in both normal and extreme driving conditions.

Strong nonlinearities under extreme conditions pose intractable challenges for the motion control of Automated Vehicles (AVs). Incapable or inaccurate modeling of nonlinearities, coupled with enormous cost of nonlinear controls, severely limit stability and performance enhancements in these scenarios. This paper proposes a novel modeling and robust control framework to address these issues. First, a novel hybrid modeling approach for trajectory tracking of AVs, combining a prior nominal model and a data-driven uncertain model based on Koopman theory, is proposed to enhance model predictive ability effectively. The finite approximation of Koopman operators captures the intrinsic characteristics of the nonlinear AV system via linear evolution in lifted observable space. Second, a Koopman-based Tube Robust MPC (K-TRMPC) is developed based on the hybrid model and zonotopic set theory. Koopman modeling error raised by the finite operators is considered a disturbance of the perturbed system. Tube-based design for constraint-tightening is developed for the nominal and lifted systems to guarantee closed-loop robustness. A reachability analysis on the future evolution of the perturbed system proves its convergence. Finally, the proposed framework is validated on real-time experiments and simulations, confirming the improved tracking performance on various surface conditions and vehicle stability in combined-slip scenarios.

A novel stochastic model predictive control (SMPC) scheme is proposed for automotive scenes based on high-performance and practical motion state prediction method. The significant properties of the proposed scheme are that: 1) it can accurately predict disturbances within the prediction horizon, and 2) the prediction results can be considered into the optimizing process to obtain a more efficient and accurate controller. As a result, the proposed adaptive cruise control (ACC) system can ensure driving safety and improve tracking accuracy and comfort performance while satisfying different driving styles. In detail, a large amount of naturalistic driving data is collected based on a real vehicle test platform at first. Then an adaptive optimization Gaussian process regression (AOGPR) is developed and trained with real measurements to predict the motion states of the preceding vehicle. The prediction module is embedded in SMPC to bind the collision conditions, tighten the states and finally construct a novel controller, i.e., AOGPR-SMPC controller. A bidirectional LSTM (BiLSTM) network is trained and tested for online recognizing driving styles to satisfy personalized car-following needs. The simulation and field tests verify and evaluate the proposed controller. The results demonstrate that the ACC system could realize personalized car-following according to the driver’s driving style, and the proposed controller can obtain better tracking accuracy and comfort performance compared with the GPR-SMPC controller and MPC controller.