Deep reinforcement learning (DRL) has demonstrated strong potential for energy management systems. However, existing approaches struggle with the complex nonlinear dynamics of hybrid energy storage systems (HESS), especially due to gradient attenuation under extreme conditions, which undermines control stability. This paper proposes a Residual-enhanced Proximal Policy Optimization (ResPPO) algorithm, which integrates residual connections into the policy network. This design enhances gradient flow propagation and mitigates vanishing gradients, leading to better training stability and optimization performance. Simulation experiment results under UDDS conditions show that ResPPO achieves a 31.7% reduction in battery degradation costs compared to standard PPO while delivering superior supercapacitor state-of-charge (SOC) regulation.

Three-dimensional lane detection is a fundamental yet highly challenging task in autonomous driving, as the presence of interference and blurring in images often impedes accurate detection. To address these challenges, we propose the MRDALane framework, which introduces two novel modules: the Dual Channel Attention Module (DCAM) and the Multi-Resolution Context Augmentation (MRCA). The DCAM utilizes a dual-channel attention mechanism to effectively suppress noise and emphasize salient features, significantly enhancing lane detail capture in complex environments. The MRCA incorporates multiple dilated convolutions with a sawtooth dilation rate design, enabling diverse feature learning across branches and improving robustness across different road conditions. Experimental results demonstrate that MRDALane outperforms state-of-the-art (SOTA) 3D lane detection models, such as LATR and CurveFormer, on both the Apollo and OpenLane datasets. Notably, in robustness experiments under adverse conditions, MRDALane achieved an F1 score improvement of up to 13.66% compared to the LATR model. This comprehensive performance evaluation demonstrates our model’s superior detection accuracy across various challenging scenarios. These advancements provide new insights for future research in 3D lane detection, contributing to the ongoing development of autonomous driving technology and road safety. Our code will be released at https://github.com/Dcelysia/MRDALane.git.

To mitigate the impact of model uncertainties and the nonlinear dynamic characteristics of actuators on the control of heterogeneous vehicle platoon, this study proposes a distributed data-enabled predictive control (DeePC) strategy. This strategy can effectively guide unknown dynamic systems to move along expected trajectories while satisfying system constraints. Firstly, a non-parametric model of the vehicle system, which takes into account the nonlinear dynamic characteristics of the actuator, is established using input-output (I/O) vehicle trajectories. Then, the regularized DeePC and the distributed control are integrated and applied to the data-driven control of vehicle platoon based on the non-parametric model. Furthermore, a local cost function, considering other vehicles information and constraints, is designed to optimize the control problem of vehicle platoon. Finally, the simulation results verify the effectiveness of the distributed data-enabled predictive control strategy in vehicle platoon control. Compared with distributed nonlinear model predictive control, the proposed distributed data-enabled predictive control exhibits stronger robustness.

Guaranteed safety and performance under different cases have significant influence on the development of lateral control of autonomous vehicles. This paper proposes a robust nonlinear controller using barrier Lyapunov function within the constraints. In the case of unknown bounded uncertainty, the barrier Lyapunov function is appropriately arranged into the controller to restrict the state variables of the designed safe region in the process. Thus, the proposed method is composed of the nonlinear controller using barrier Lyapunov function and kalman filter. The kalman filter is designed as an observer to estimate the state variables. At the meantime, the method we proposed meets the constraints of output and the external disturbance. Moreover, the validity of the proposed method is validated in the co-simulation of the MATLAB/Simulink and CarSim.

Faults in braking actuators can compromise the safety and stability of urban rail train operations. Existing fault-tolerant control methods for trains struggle to guarantee both transient and steady-state braking performance quantitatively. In this paper, we propose a cooperative fault-tolerant braking control with prescribed performance for urban rail trains. A coupled multi-agent braking model is first developed, where each vehicle is treated as an independent and controllable agent subject to various uncertainties, input saturation and different levels of actuator faults. Further, incorporating a prescribed tracking performance function, a distributive adaptive terminal sliding mode controller is developed to ensure safe and reliable train braking control. The control input saturation nonlinearity is addressed by employing a smooth hyperbolic tangent function for approximation. Adaptation laws are introduced to mitigate the effects of parameter uncertainties and external disturbances. The efficacy of the proposed control scheme is validated through comprehensive numerical simulations.

In a mixed traffic environment of human and autonomous driving, it is crucial for an autonomous vehicle to predict the lane change intentions and trajectories of vehicles that pose a risk to it. However, due to the uncertainty of human intentions, accurately predicting lane change intentions and trajectories is a great challenge. Therefore, this paper aims to establish the connection between intentions and trajectories and propose a dual Transformer model for the target vehicle. The dual Transformer model contains a lane change intention prediction model and a trajectory prediction model. The lane change intention prediction model is able to extract social correlations in terms of vehicle states and outputs an intention probability vector. The trajectory prediction model fuses the intention probability vector, which enables it to obtain prior knowledge. For the intention prediction model, the accuracy can be improved by designing the multi-head attention. For the trajectory prediction model, the performance can be optimized by incorporating intention probability vectors and adding the LSTM. Verified on NGSIM and highD datasets, the experimental results show that this model has encouraging accuracy. Compared with the model without intention probability vectors, the impact of the model on NGSIM dataset and highD dataset in RMSE is improved by 57.27% and 58.70% respectively. Compared with two existed models, evaluation metrics of the intention prediction can be improved by 7.40-10.09% on NGSIM dataset and 2.17-2.69% on highD dataset within advanced prediction time 1s. This method provides the insights for designing advanced perceptual systems for autonomous vehicles.

Estimating accurate torque and speed is critical to control the operation of permanent magnet synchronous motors (PMSM). But the temperature factors are usually neglected in existing studies, which degrades estimation accuracy. In this paper, a thermal-aware digital twin model is proposed for PMSM to estimate motor torque and speed with the motor temperature and d-q axis current and voltage. Firstly, the motor parameters related to torque and speed are extracted by the Spearman correlation coefficients. Moreover, the stator winding temperature is selected as the input feature. Secondly, a digital model based on BP neural networks (BPNN) is established to estimate torque and speed. Thirdly, the parameters of the BPNN model are optimized by the whale optimization algorithm to accelerate the convergence speed and avoid local optima. Finally, experimental results show that the mean square error (MSE) of the BPNN model considering the temperature factors is reduced by 8.3%, which verifies that there is an effect of temperature on the torque and speed estimation. The MSE of the proposed method is reduced by 11.7% on average, which confirmed the higher accuracy of the proposed method compared with the classical BPNN model.

The accurate estimation of adhesion force between wheels and rails is an important task as it helps to the wheel-slip prevention (WSP) system preventing the wheels from locking and reducing the stopping distance. Influenced by a changing external environment, the adhesion force estimation process is complex. Thus, an extended state observer (ESO) is proposed to accurately estimate the adhesion force of railway vehicles with the modeling deviation and measurement noise. With the estimated modeling error information, an auxiliary compensation part is designed to eliminate the steady-state estimation error causing by the modeling deviation. Further, a Fal function filter is added to the ESO to deal with the effect of measurement noise. The convergence of the proposed estimation method is analyzed theoretically. The effectiveness of the designed algorithm is corroborated by simulation comparisons to other standard approaches.

Precision stopping of trains requires special attention to brake control because a pneumatic brake system of a train is highly nonlinear, hybrid and uncertain. Existing solutions to automatic train stop control ignore the pneumatic brake system or simply treat it as a system delay, which is quite far apart from the real train stopping dynamics. Moreover, the service life of pneumatic brake systems decreases fast due to the frequent changes in output of existing controllers. Thus, an adaptive nonlinear sliding mode control method is developed in this paper, which has strong applicability to nonlinear and hybrid system control synthesis due to its natural variable structure characteristic. A nonlinear integral sliding surface with adaptive updating parameters is proposed to improve the control precision and the robustness. Extensive simulations are performed to validate the effectiveness of the proposed method. The results show that the proposed algorithm outperforms a PID control algorithm in terms of stopping error and expected lifetime of pneumatic brake systems.

With the rapid development of inspection techniques, more emphases should be placed on the improvement of the reliability, safety and intelligence of the robot system. In this paper, a framework for the patrol robot that automatically finishes complex task and motion planning in the indoor substation is proposed. To realize real-time response to the environmental changes, the proposed framework keeps an ongoing interaction with the environment as a Reactive System (RS). The RS employs the Transition System (TS) and Nondeterministic Biichi Automaton (NBA) to create a discrete controller that bounds the acts of the patrol robot in the safe and reasonable specifications. What's more, the environment signals are treated as the trigger condition of task switching. If a new environment information is detected, our approach can automatically give a feasible plan. Then, the sensor-based mechanism of continuous controllers is guided by the discrete controller, which results in a hybrid system satisfying the high-level specification. The experiment within the LTLMoP toolkit verifies the proposed framework.