Yan Wang

Driving on icy roads poses significant challenges for automated electric vehicles (AEVs) motion planning and control due to uncertain dynamics, multiple constraints, and temporal rules satisfying problems.This paper proposes a hybrid data-driven Model Predictive Path Integral (MPPI) control framework with temporal logic constraints for real-time safe motion planning and trajectory tracking on slippery roads. To handle modeling uncertainties and mismatches in vehicle dynamics, a hybrid model is constructed by combining a nominal physics-based baseline with a 1-Lipschitz residual neural network pretrained via the collected vehicle data. This model captures both nominal dynamics and unmodeled residual effects, providing a reliable system representation of AEVs for controller design with robustness performance guaranteed. The pretrained model is embedded into a sampling-based predictive controller, where state constraints and task specifications expressed by temporal logic formulas are incorporated into the cost function. So that, our control strategy ensures that the generated trajectories adhere to both safety constraints and high-level behavioral objectives with real time solving guaranteed. Real electric vehicle experiments validate the effectiveness of the proposed method and demonstrate its superiority in achieving safe and reliable planning and tracking control on icy roads.

Distributed drive vehicles provide enhanced actuation flexibility, making longitudinal–lateral coordinated stability control essential for improving vehicle handling and safety under complex driving conditions. Nevertheless, the existing coordinated control strategies commonly employ stability reference models with fixed tire–road friction coefficients, which restrict their adaptability to time-varying adhesion environments. In addition, conventional sliding mode-based lateral stability controllers may exhibit limited performance when confronted with strong nonlinear coupling and external disturbances. To address these issues, this paper proposes an integrated longitudinal–lateral coordinated stability control framework for distributed drive vehicles. A dual unscented Kalman filter-based estimator is developed to identify the tire–road friction coefficients and construct a friction-adaptive reference model for yaw rate and sideslip angle. An adaptive fractional power speed controller with resistance compensation is designed to generate the total longitudinal driving torque, while an adaptive neural sliding mode controller produces the corrective yaw moment for lateral stability enhancement. Furthermore, a pseudoinverse-based torque distribution strategy is employed to allocate the longitudinal torque and yaw moment to individual wheels. Simulation results demonstrate that the proposed framework significantly improves vehicle stability and tracking accuracy compared with conventional control methods under varying road conditions.

Model Predictive Control (MPC) stands out as a prominent method for achieving optimal control in autonomous driving applications. However, the effectiveness of MPC approaches critically depends on the availability of accurate dynamic models and often necessitates substantial computational overhead for real-time optimization procedures at every iteration. Recently, the research community has been increasingly drawn to the concept of cloud-assisted MPC, which harnesses the capabilities of powerful cloud computing to provide users with on-demand computational resources and data storage services. Within these cloud-assisted MPC frameworks, control signals are merged with a cloud-based MPC, which leverages the substantial processing power of cloud infrastructure to determine optimal control actions using detailed nonlinear models for greater accuracy. Simultaneously, a local MPC runs on simplified linear models constrained by limited on-device computing resources, delivering prompt control responses at the cost of reduced model accuracy. To achieve an effective trade-off between rapid response and model fidelity, this work presents a new model-free deep reinforcement learning structure designed to merge cloud and local MPC outputs. Tests conducted on path-following scenarios show that the introduced method achieves superior control performance compared to existing reinforcement learning baselines and conventional rule-based fusion strategies.

Active suspension systems have gained significant attention for their capability to improve vehicle dynamics and energy efficiency. However, achieving consistent control performance under diverse and uncertain road conditions remains challenging. This paper proposes a prior-guided residual reinforcement learning framework for active suspension control. The approach integrates a Linear Quadratic Regulator (LQR) as a prior controller to ensure baseline stability, while an enhanced Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm learns the residual control policy to improve adaptability and robustness. Moreover, residual connections and Long Short-Term Memory (LSTM) layers are incorporated into the TD3 structure to enhance dynamic modeling and training stability. The simulation results demonstrate that the proposed method achieves better control performance than passive suspension, a standalone LQR, and conventional TD3 algorithms.

In an intelligent driving system, the rationality of driving decisions and the trajectory planning scheme directly determines the safety and stability of the system. Existing research mostly relies on high-definition maps and empirical parameters to estimate road adhesion conditions, ignoring the direct impact of real-time road status changes on the dynamic feasible domain of vehicles. This paper proposes an intelligent driving decision-making and trajectory planning method that comprehensively considers the influence factors of vehicle–road interaction. Firstly, real-time estimation of road adhesion coefficients was achieved based on the recursive least squares method, and a dynamic adhesion perception mechanism was constructed to guide the decision-making module to restrict lateral maneuvering behavior under low-adhesion conditions. A multi-objective lane evaluation function was designed for adaptive lane decision-making. Secondly, a longitudinal and lateral coupled trajectory planning framework was constructed based on the traditional lattice method to achieve smooth switching between lateral trajectory planning and longitudinal speed planning. The planned path is tracked based on a model predictive control algorithm and dual PID algorithm. Finally, the proposed method was verified on a co-simulation platform. The results show that this method has good safety, adaptability, and control stability in complex environments and dynamic adhesion conditions.

Most collision accidents are caused by drivers’ errors. To more effectively assist drivers in avoiding collisions, the human-machine shared driving system has become increasingly integrated into intelligent vehicles as consumer technologies and electronics. Furthermore, in collision avoidance scenarios, existing shared systems frequently exhibit excessive intervention when implementing corrective maneuvers for driver-induced errors, thereby inadvertently elevating the driver’s workload. To address these issues, this paper proposes a novel human-machine shared framework based on hybrid system theory. The shared framework contains a trajectory planning layer and a multi-state shared control layer. At the trajectory planning layer, the optimal collision-free trajectory is selected from the candidate trajectories based on natural driving data and collision constraints. The optimal trajectory is used as the reference trajectory for collision avoidance. At the shared control layer, a shared control strategy based on a hybrid automaton model performs multi-state shared control. The proposed humanmachine interaction criterion based on driver error governs the multi-state transitions. A model predictive controller is utilized to track the reference trajectory for collision avoidance. Simulation and experimental results show that the proposed method outperforms shared control methods based on steering angle deviation and control authority allocation. It reduces the drivers physical workload and automation intervention, enhancing cooperation satisfaction.

This paper focuses on enhancing master cylinder pressure control in pressure-sensorless Electro-Hydraulic Brake (EHB) systems. A novel control strategy is developed, integrating a Piecewise Sliding Mode Controller (Piecewise-SMC) with an Extended Sliding Mode Observer (ESMO) based on a newly derived pressure–position–velocity model that accounts for rack position and velocity effects. To handle external disturbances and parameter uncertainties, the ESMO provides accurate pressure estimation. The nonlinear EHB model is approximated piecewise linearly to facilitate controller design. The proposed Piecewise-SMC regulates motor torque to achieve precise pressure tracking. Experimental validation under step-change braking conditions demonstrates that the Piecewise-SMC reduces response time by 31.8%, overshoot by 35.8%, and tracking root mean square error by 9.6% compared to traditional SMC, confirming its effectiveness and robustness for pressure-sensorless EHB applications.

Emissions from urban transportation significantly impact the environment and public health, including air pollution, increased carbon emissions, and various health issues. Public transportation is vital in alleviating traffic congestion and reducing overall emissions. However, due to the complex urban environment, bus emissions exhibit spatial heterogeneity at fine scales, and the influencing variables have nonlinearity and complexity, making it difficult to predict real-time emissions accurately. To address this challenge, we used real-time emission data from hybrid electric vehicle buses (HEBs), which are widely used in China, to train four models: linear regression (LR), random forest (RF), XGBOOST, and graph embedding-based generative adversarial network (GE-GAN). These models predicted real-time emissions at fine scales and analyzed the interactions between emission variables. Using graph embedding and GAN algorithms, the GE-GAN model effectively captures semantic and spatial information between variables, understanding complex interactions. Among all the models, GE-GAN achieved the best results in validation tests. For CO2 prediction, the root mean square error (RMSE) was 0.98, and the mean absolute error (MAE) was 1.31. For CO prediction, the RMSE was 0.03, and the MAE was 0.08. For NOx prediction, the RMSE was 0.43, and the MAE was 0.57. The experimental results demonstrated that GE-GAN can accurately predict real-time bus emissions at a fine scale in mixed urban areas. The accurate predictions can assist planning departments in determining pollutant distribution in urban areas, optimizing public transportation routes, and contributing to reducing city carbon emissions.

Accurate and real-time acquisition of vehicular system dynamic states, road surface conditions, and motion states of surrounding participants is crucial for the safety, passenger comfort, and operational efficiency of autonomous vehicles (AVs) and connected automated vehicles (CAVs). In recent years, a significant amount of research has contributed to the field of state estimation for vehicles, roads, and pedestrians. From the systemwide perspective of intelligent transportation systems to a focused view on “vehicle-road-pedestrian”, this survey aims to provide a comprehensive review and summary of recent state estimation techniques for vehicle motion, road surface, and pedestrian motion. A thorough analysis of the reviewed literature, relevant datasets, evaluation metrics, and experimental platforms in this field is also conducted. Finally, existing challenges and future research directions about methods and performance evaluation are further discussed. This survey is expected to contribute to the advancement of research in dynamic state estimation of vehicle-road-pedestrian, thereby facilitating the development of efficient and safe intelligent transportation systems.