With the increasing intelligence of vehicles, the growing network communication load introduces uncertain time delays in control signal transmission, posing a more serious challenge to the precise tracking and synchronization control of dual-motor steer-by-wire systems. To address this problem, this paper proposes a tracking and synchronization control strategy. Firstly, a dynamic model of the steering motor incorporating time delays is established. On this basis, an Online Identification Adaptive Smith Predictor (OIASP) is designed to accurately estimate the uncertain time delays in the system. Furthermore, an Adaptive Hybrid Active Disturbance Rejection Control (AHADRC) controller is proposed, which utilizes a Phase Advance Extended State Observer (PAESO) to reduce phase lag in the observed states. Combined with feedforward control and linear state error feedback, a FF-LSEF composite control law is constructed, significantly enhancing the dynamic tracking performance and robustness against complex disturbances. Theoretical analysis demonstrates the favorable stability of the proposed AHADRC. Additionally, a mean deviation coupling synchronization control structure is incorporated to further enhance the synchronization accuracy of the steering system. Finally, the effectiveness of the proposed control strategy in suppressing uncertain time delays and external disturbances was verified through simulation experiments and hardware-in-the-loop tests.

The development of intelligent driving and steer-by-wire (SBW) technologies has increased the computational complexity and the number of CAN bus nodes in SBW vehicles, leading to the presence of significant time-varying CAN network time delay (TV-CAN-TD) in the control inputs of the SBW system. For dual-motor steer-by-wire (DMSBW) vehicles, TV-CAN-TD not only severely affects vehicle stability but also further deteriorates the synchronization performance of two motors, potentially causing accidents. To ensure vehicle safety under TV-CAN-TD conditions, a hierarchical time delay control strategy is proposed. For the upper-level controller, a TD-H₂/H∞ mixed sensitivity stability controller is designed. Based on the operational mechanism of the CAN network, the random time delay uncertainties in the vehicle's lateral dynamics model are modeled in detail using a polytope model. Combined with H₂/H∞ robust control, the controller mitigates the adverse effects of time delay uncertainties on vehicle stability. For the lower-level controller, a synchronization strategy that combines the novel reaching law sliding mode control with a cross-coupling synchronization structure is designed to ensure synchronization performance under TV-CAN-TD conditions. Simulations and hardware-in-the-loop experiments demonstrate that the proposed hierarchical control strategy effectively enhances the stability and synchronization performance of the DMSBW vehicle under TV-CAN-TD.

To enhance path-tracking accuracy for four-wheel independent steering and drive (4WIS-4WID) vehicles on high-curvature paths under model mismatch uncertainties and potential actuator faults, this paper proposes a hierarchical robust control strategy consisting of a pseudo-control law solution layer and an over-actuated robust control allocation layer. The pseudo-control layer develops an enhanced tube MPC framework with a novel state-error-driven adaptive terminal constraint set, deriving a robust control law resistant to state matrix uncertainties. Addressing deviations induced by unknown real-time perturbations in the control matrix during angle and torque allocation, the subsequent allocation layer introduces an innovative convex quadratic cone optimization (CQCO) methodology. This approach reformulates the uncertain control allocation problem into a deterministic linear cone quadratic optimization problem, effectively suppressing time-varying perturbations and accommodating partial actuator faults to minimize deviation from the desired pseudo-control law. Hardware-in-the-loop experiments demonstrate that proposed method achieves significantly superior tracking performance under uncertainty and fault conditions compared to conventional approaches.

The integration of steering, driving, and other subsystems at the wheel end is an emerging trend for improving vehicle chassis performance. The kingpin steering system, featuring a mechanically decoupled four-wheel design, is a preferred solution for in-wheel integration. However, this integration inevitably increases the vehicle's unsprung mass, which under random road excitations leads to dynamic fluctuations in self-aligning torque and system uncertainty, significantly reducing steering angle tracking accuracy. To overcome these challenges, this paper proposes a steering angle tracking control method based on interval type-2 fuzzy logic and variable-boundary tube model predictive control (IT2FL-VBTMPC). The IT2FL is employed to construct a three-dimensional membership function space that establishes a real-time mapping between the self-aligning torque and the disturbance boundaries of the kingpin steering system, thereby estimating the disturbance boundaries online. Based on this, a VB-TMPC is designed, which couples the nominal steering system model with the real-time disturbance boundary estimates. Within this framework, a baseline MPC controller generates the optimal reference trajectory for the front wheel steering angle, while a tube robust controller compensates for the system uncertainties introduced by the negative effects of unsprung mass, thus enhancing steering angle tracking accuracy. Experimental results demonstrate that the proposed IT2FL-VB-TMPC reduces the maximum tracking error and the average tracking error by 38.8% and 38.5%, thereby verifying the effectiveness of the proposed method.

The dual-motor steer-by-wire (DMSBW) system significantly enhances the reliability of vehicle steering systems due to its high redundancy. However, in practical steering control, the limited bandwidth of the in-vehicle controller area network (CAN) communication network often leads to issues such as packet loss and congestion. These issues can cause asynchronous time delays (ATDs) in the control signals transmitted to the dual motors, resulting in inconsistent motor angles, which severely affects the tracking accuracy of the vehicle. To address the problem, this article proposes an event-triggered neural network (NN) dynamic surface consistency tracking control strategy for DMSBW systems. First, a nonlinear model of the DMSBW system with ATD is established, with the system’s nonlinearities approximated using NNs. Then, an event-triggered dynamic surface consistency controller is designed, incorporating an auxiliary variable and event-triggering mechanism within the backstepping control framework to mitigate the effects of ATD and limited bandwidth. Additionally, the use of dynamic surface and error compensation techniques reduces the computational burden and accuracy degradation caused by the iterative derivation in backstepping control. The introduction of the dual-motor consistency errors and finite-time terms ensures fast convergence and tracking consistency of the system. Finally, hardware-in-the-loop experiments demonstrate that the proposed control strategy improves the DMSBW consistency by 45% and reduces communication load by 53.7%, effectively ensuring consistency tracking accuracy under ATD while alleviating communication burdens.

For ordinary drivers, stabilizing drift during collision avoidance is challenging, potentially leading to serious accidents. To enhance driving safety and human-machine cooperative efficiency under such extreme conditions, this paper presents a human-centered drift-curbing (HCDC) control strategy considering the driver’s acceptance of the driving assistance system’s intervention. Specifically, the acceptance domain (AD), a framework for capturing and describing the driver’s intervention tolerance characteristics, is presented. To constrain the additional steering intervention within the driver’s AD, a soft-intervention (SI) strategy based on steering ratio control is formulated. On this basis, an HCDC controller is developed using nonlinear model predictive control (NMPC), with individualized constraints designed by referring to the driver’s AD. Driver- and controller-in-the-loop verification experiments are conducted in a low adhesion highway collision avoidance scenario prone to drift. The experimental results indicate that the proposed HCDC control strategy provides an acceptable “soft” intervention, enabling drivers to stabilize drift quickly while reducing steering effort.

In formulating a driver-oriented human-machine shared control (HMSC) system, an intelligent driving system (IDS) should ideally minimize interventions under normal driving conditions while providing timely and necessary interventions in hazardous situations. To achieve this balance, this paper proposes a novel driver-oriented active intervention control (DAIC) framework for automotive collision avoidance (CA). As a foundation, a spatiotemporal collision risk evaluation (SCRE) method is developed by integrating collision probability (CP) and time-to-collision (TTC) metrics, comprehensively quantifying collision risks in both spatial and temporal dimensions. Subsequently, a short-horizon driver behavior prediction network (SDBP-Net) based on bidirectional long short-term memory (BiLSTM) is proposed, enabling effective prediction of short-horizon driver input increment sequence and corresponding vehicle trajectories. Leveraging these developments, a partially cooperative game-based HMSC strategy is formulated to dynamically allocate control authority between the driver and IDS. Driver-in-the-loop (DIL) experiments validate the effectiveness of the proposed DAIC framework. The results demonstrate that the DAIC framework significantly reduces unnecessary IDS interventions while maintaining collision safety, effectively balancing driving safety and driver acceptance. This work highlights the practical potential of the feedforward HMSC system in achieving safer, more intuitive and driver-friendly collision avoidance assistance.

During high-speed platooning, if the leading vehicle experiences sudden failure (e.g., tire blowout or braking loss), the following vehicles must execute simultaneous deceleration and obstacle avoidance within a minimal timeframe. This process induces significant load transfer, leading to drastic variations in vertical load distribution and tire cornering stiffness, thereby critically compromising lateral stability. Conventional planning methods, reliant on static stability boundaries and fixed rollover thresholds, fail to adapt to such dynamic perturbations, resulting in degraded control performance. This paper proposes an intelligent trajectory planning framework for emergency obstacle avoidance in heavy-duty truck platoons. The framework incorporates a physics-informed neural network to predict real-time load transfer effects on tire cornering stiffness, combined with an Energy-based Rollover Index (ERI) for dynamic stability boundary assessment. Innovatively integrating the nonlinear effects of braking-induced load transfer into coordinated trajectory-speed optimization, the method enhances platoon maneuverability while ensuring safety. Simulation results demonstrate that the proposed approach increases maximum safe lane-change speeds by 0.5%-4.3% while reducing false rollover alarms to below 2%. Co-simulation using MATLAB/Simulink and TruckSim verifies a 4.2% improvement in overall planning performance while maintaining dynamic safety standards.

Accurately predicting the future trajectories of surrounding vehicles is crucial for enhancing driving safety in Internet of Vehicles (IoV). However, existing trajectory prediction models often suffer from reduced accuracy and stability when confronted with perception information uncertainty. To address this issue, a novel trajectory prediction method considering perception uncertainty (PU-TSI) is proposed, which weakens the propagation of invalid trajectory information, improves prediction accuracy and stability, and adapts to dynamic vehicle interaction scenarios. Specifically, perception uncertainty is modeled based on the Bi-GRU network and integrated into trajectory feature encoding through the proposed data-confidence attention mechanism that jointly accounts for the vehicle motion state and dynamic temporal-spatial interactions. The dynamic interactions are extracted using the graph dual-attention network. In the encoding stage, unlike traditional linear methods, this method fuses different trajectory features considering uncertainty, effectively capturing the complex coupling effects between features and utilizing the full spectrum of available trajectory information. The fused encoded features are passed to the decoding stage, where vehicle interactions guide the trajectory decoder to predict future trajectories. Finally, the proposed model is trained and validated on multiple public datasets. The experiment results demonstrate that PU-TSI effectively predicts the future trajectories of surrounding vehicles in various interaction scenarios, and achieves superior prediction accuracy and stability compared to existing models.

The integrated wheel-end module (IWEM) incorporates both driving and steering functions at the wheel end, enabling independent control of each wheel and significantly enhancing the vehicle’s agility and stability. However, during steering maneuvers, road excitations and variations in wheel motion may lead to air-gap eccentricity (AGE) in the in-wheel motor, resulting in the unbalanced radial force and torque (URF/URT). These disturbances degrade the tracking accuracy of the wheel-end module and subsequently impair the vehicle’s steering performance. To address this issue, a control-oriented model of the IWEM is developed, incorporating the coupled characteristics of longitudinal slip and lateral slip angle of the tire. Furthermore, an interval type-II fuzzy logic (IT2FL) driven self-tuning tube model predictive control strategy is proposed. First, the generation mechanism of URF/URT under AGE is analyzed in depth. To mitigate the adverse effects of these uncertain disturbances on the wheel-end dynamics, an online observation method based on IT2FL systems is designed to enable real-time estimation of URF/URT and its perturbation bounds. On this basis, a self-tuning tube model predictive controller is developed to dynamically adjust the Tube boundaries according to the real-time observed disturbance range, effectively reducing control conservatism and improving tracking accuracy. Hardware-in-the-loop simulations and real-vehicle experiments validate that the proposed method can effectively suppress the adverse effects caused by AGE and significantly enhance the tracking control performance of the integrated wheel-end system.

As heavy vehicles advance toward increased intelligence and modernization, the control of advanced driver assistance systems for ensuring driving safety faces significant challenges. To enhance the driving safety of heavy vehicles operated by drivers with varying driving styles, this article proposes a human–machine cooperative control (HMCC) strategy that combines steering and braking using deep deterministic policy gradient (DDPG) algorithm. First, a multiagent system is adopted as the framework for the driving safety assistance control system, wherein the active front steering (AFS) system and the differential braking control system (DBC) function as subsystems. These subsystems interact through control sequence information while managing yaw and roll stability. The optimal control performance of both the AFS and DBC is ensured using a distributed model predictive controller and Pareto optimality theory. Second, to analyze different drivers’ driving styles, safety characteristic parameters were collected from multiple drivers. By analyzing the effects of drivers on yaw and roll stability, drivers were classified into three types. Furthermore, an HMCC strategy based on DDPG is designed. Phase plane constraints that consider yaw and roll stability are incorporated into the design of the DDPG reward function, training the agents to allocate cooperative control weights between the driver and the AFS and DBC controllers. Finally, the proposed control strategy’s effectiveness is validated through the electro-hydraulic compound steering and braking hardware-in-the-loop test system, demonstrating its ability to improve driving safety for different driver characteristics.

The increased number of controller area network bus nodes in the four-wheel independent steer-by-wire (4WISBW) system introduces uncertain network communication delays in the steering mechanism’s control inputs, reducing tracking accuracy and synchronization performance. To address this issue, we propose a multi-agent adaptive formation control strategy, comprising a nonlinear time-delay estimator (NTDE) and a multi-agent formation controller (MAFC). The NTDE reduces high-frequency oscillations and steady-state errors in delay estimation using a nonlinear integral sliding surface and a chatter-free supertwisting delay equation, while deriving the implicit time-delay system of the steering mechanism through nonsingular transformations. The MAFC constructs a leader-follower formation topology for the 4WISBW implicit time-delay system and designs adaptive coupling coefficients to dynamically adjust the time-varying formation of steering mechanisms, compensating for tracking and synchronization errors caused by network delays. Hardware-in-the-loop testing validates the proposed strategy’s effectiveness.

Four-Wheel Independent Steering (4WIS) vehicles can independently control each wheel angle to achieve multiple steering modes, thereby expanding their motion space. However, existing trajectories do not match the multiple steering modes, making it difficult to leverage the stability of same-direction steering and the agility of opposite-direction steering of the front and rear wheels. This results in 4WIS vehicles facing challenges in generating trajectories that balance flexibility and stability under emergency obstacle avoidance scenarios. To address this, this manuscript proposes a multi-mode trajectory planning method for 4WIS vehicles under emergency obstacle avoidance scenarios. It decomposes the multiple steering modes of 4WIS vehicles into stable side-move motion and flexible yaw motion. By integrating these two motions, we establish a comprehensive kinematic model for 4WIS multi-mode operation. Based on this, the method combines side-move and yaw motions to construct a multi-mode risk field. The field is then iteratively optimized by the ILQR-MPC method to generate obstacle avoidance trajectories with sideslip and yaw angles, providing reference for the side-move and yaw motions across the multiple steering modes. Results show that the proposed method can generate trajectories that balance flexibility and stability, providing a foundation for safe and efficient obstacle avoidance.

The electro-mechanical braking (EMB) system, known for its integration and intelligence, is considered an ideal actuator for vehicles. However, accurate slip ratio tracking is hindered by uncertainties in the road adhesion coefficient and communication delays, which adversely affect anti-lock braking performance. This study proposes an interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy robust$H_{\mathrm {\infty }}$anti-lock braking control strategy to address these challenges. An IT2 T-S fuzzy anti-lock braking system (ABS) model is designed to enhance ABS linearization accuracy under uncertainties in the road adhesion coefficient. The vehicle speed is observed using an IT2 T-S fuzzy observer. A delay-product-type Lyapunov-Krasovskii (L-K) functional robust$H_{\mathrm {\infty }}$anti-lock braking controller is designed. The delay-product-type L-K functional effectively utilizes delay information to reduce the conservatism of the controller, improving the slip ratio tracking performance of the EMB system under input delay conditions. Additionally, a motor torque compensation strategy is introduced to optimize slip ratio control on different roads. Simulations verify that the proposed strategy improves the EMB system’s slip ratio control performance under conditions of uncertainty and input delay.

We will apply dual three-phase permanent magnet synchronous motors (DTP-PMSM) to the road sensing unit of the steer-by-wire system to improve redundancy and fault tolerance performance, meeting the requirements of intelligent transportation systems. However, open circuit faults inevitably lead to periodic torque ripple, parameter mismatch, and current harmonics, which pose a huge threat to drivers and traffic participants. Therefore, this article proposes a dynamic balancing strategy that can operate stably under both motor health and fault conditions. Firstly, this strategy does not require diagnostic fault information, thereby fundamentally avoiding performance degradation caused by diagnostic accuracy, time, model reconstruction, and strategy switching. The torque control module (TCM) is based on adaptive iterative learning control with a forgetting factor, which achieves torque tracking and suppression of periodic ripple after faults. The current control module (CCM) is based on ultra local model free predictive current control and extended state observer to track current and suppress the effects of parameter mismatch and current harmonics. Based on the multi-variable multi-objective sliding mode extreme value search strategy, the internal parameters and external weights of the two modules are dynamically adjusted to achieve dynamic balance between health and fault operation of TCM and CCM. The experimental results show that this strategy effectively improves the robustness of the system in a healthy state and the fault tolerance performance in a faulty state, effectively suppressing the periodic torque ripple and current harmonics caused by faults. Compared with the dynamic balancing strategy based on NSGA-II, this strategy increases the THD of torque by 13.45% and reduces the THD of current harmonics by 9.83%.

For intelligent vehicles, accurate prediction of the trajectories of surrounding vehicles is an important basis for identifying potential risks in advance and making optimal decisions. However, unsignalized roundabouts have multiple entrance and exit ramps with uncertain angles and numbers, which leads to more diverse driving intentions and more complex multi-vehicle interactions of surrounding vehicles, which significantly reduces the accuracy of trajectory prediction and even causes wrong decisions and serious collision accidents. To this end, this paper innovatively proposes a method for predicting the trajectory of surrounding vehicles in unsignalized roundabouts based on global-local historical information fusion (TP-GLIF). From a global perspective, a scene-adaptive dynamic and static feature information encoder is constructed, and a multi-head attention mechanism is introduced to capture environmental information closely related to driving intention, so that the model always focuses on the dynamic and static features that are most beneficial to improving prediction accuracy. From a local perspective, a novel dynamic heterogeneous graph attention multi-vehicle interaction encoder is constructed, and a novel hierarchical Gaussian mixture model-hidden Markov model-support vector machine driving intention recognition algorithm is designed to guide prediction decoding. A long short-term decoder LS-GRU is designed to decode the surrounding vehicle trajectories under the influence of complex road structure features, complex multi-vehicle interaction features and diverse driving intention features in different time periods to reduce the cumulative error. Experimental results show that TP-GLIF can achieve accurate trajectory prediction in different roundabout scenes, and has good computational efficiency and generalization ability.

To enhance the trajectory tracking capability of steer-by-wire (SBW) vehicles while reducing driver’s workload, a human-machine shared control (HMSC) strategy using improved reinforcement learning-based model predictive control (RL-MPC) is proposed. In this paper, two main contributions have been made: 1) for driver loop, a variable steering ratio (VSR) strategy applied to SBW system is designed based on an improved fuzzy controller, whose parameters are optimized through simulated annealing (SA) algorithm; 2) for intelligent control system loop, an improved RL-MPC method is proposed to realize the high precision steering tracking control for autonomous vehicles (AVs), in which MPC and deep deterministic policy gradient (DDPG) are deeply integrated to combine their short-term optimization ability and long-term value estimation capability. Moreover, to shorten the time of overall training and ensure that the optimal control strategy can be explored, the DDPG agent is pretrained before the parallel training of RL-MPC. CarSim-MATLAB/Simulink co-simulation results show that in the whole tracking process, the lateral position error and yaw angle error of the vehicle are significantly reduced, indicating that the tracking accuracy is greatly improved. Meanwhile, the steering wheel angle and speed are also reduced, which means that the driver will spend less energy during the steering process.

To improve the overall performance of human-vehicle cooperation and enhance the drivers’ confidence in the advanced driver assistance system (ADAS), an adaptive haptic assistance control scheme for the steer-by-wire (SBW) vehicle is presented in this paper. A comprehensive human-vehicle system model is built, including vehicle dynamics, the SBW model, and the driver’s arm neuromuscular dynamics model, as a foundation for controller design. An expert driver model based on a multi-layer feed-forward neural network (MLFN) is developed to generate the reference steering angle for haptic assistance design. The individual driver’s arm characteristics are identified and incorporated into the adaptive haptic assistance controller design to generate personalized torque assistance, facilitating a typical driver to achieve the same trajectory-tracking performance as experts. The nonsingular fast terminal sliding mode (NFTSM) is applied to calculate the assistance torque to ensure the fast finite-time convergence and robustness of the system. Simulations and driver-in-the-loop experiments are conducted, with results showing that the proposed haptic assistance controller can help drivers complete the trajectory-tracking task by providing personalized torque assistance while reducing their steering workload.

Aiming at the problem of low merging efficiency, poor platoon stability, and high collision risk when multiple connected and automated vehicles merge into the same target platoon, we propose a multi-vehicle self-organized cooperative control strategy for platoon formation, which includes vehicle self-organizing formation control and platoon cooperative merging control. The vehicle self-organizing formation control module organizes the merging vehicles within the V2V communication range into multiple local platoons. The dynamic self-adjusting critical interval and a fixed-topology second-order platoon consistency control protocol are proposed to divided the vehicles reasonably and make the states of local platoon vehicles consistent. The merging vehicles merge into the target platoon as a whole in the form of a local platoon, which transforms the complex multi-vehicle merge problem into a platoon cooperative control problem and improves the merging efficiency. The platoon cooperative merging control module adopts a distributed model predictive control (DMPC) theory to design two longitudinal cooperative merging controllers, which control the target platoon to split to create a merging gap and the local platoon to align with this gap longitudinally. The lateral merging controller controls the local platoon change the lane to merge into the target platoon safely and smoothly. Simulation experiments are conducted in typical scenarios, and it is verified that the proposed control strategy can enable multi-vehicles to merge into a platoon efficiently, safely, and stably.

In high-level autonomous driving, high-precision map construction is crucial, and map construction based on laser SLAM is one of the mainstream methods. Existing laser SLAM technology usually assumes that the environment is static, ignores dynamic targets, or removes their influence on map construction by directly removing dynamic targets. This paper proposed a factor graph optimization SLAM mapping method for autonomous vehicles considering dynamic target motion, aiming to improve the accuracy and reliability of map construction. First, the kinematic model of the autonomous vehicle was established, and the inertial measurement unit (IMU) and laser radar (LiDAR) were jointly calibrated. The laser point cloud distortion was corrected using the IMU and kinematic model. Then, a semantic spatiotemporal consistency method for dynamic target detection was proposed, and dynamic targets were effectively detected from the laser point cloud through a fully convolutional neural network (FCN), and the estimation of the motion pose of the dynamic target was optimized by combining the improved unscented Kalman filter (UKF). Finally, based on the motion estimation of the dynamic target, the laser point cloud was divided into static and dynamic parts, and the static part was directly registered, while the dynamic part was registered by introducing motion pose compensation. A multi-source asynchronous factor model was constructed through semantic segmentation results, IMU and global positioning system (GPS) data, and a graph optimization method was used for global optimization, which significantly improved the accuracy of map construction and eliminated the negative impact of dynamic targets on map construction.

In the process of human-machine collaborative driving, it is crucial to ensure that the driver and the machine operate the vehicle in a safe, stable, and efficient manner. However, most of the current studies focus on the lateral shared control under the condition of constant longitudinal speed, without considering the influence of longitudinal speed change on lateral control. Therefore, this article proposes a collaborative control framework of human-machine game in lateral and longitudinal dimensions considering dynamic allocation of driving authority to improve the collaborative performance of co-driving. First, a human-machine collaborative driving system model that adapts to the characteristics of co-driving mode is built as the basis of the shared control scheme. Then, the unconscious competitive relationship of human-machine is described as the game interaction relationship, with optimal control strategies for both sides being deduced theoretically at the game equilibrium. Additionally, a dynamic adjustment strategy of driving authority considering the longitudinal speed is established based on the assessment of lateral and longitudinal risks. Finally, the driver-in-the-loop test and co-simulation results show that the proposed control strategy has achieved good performance in terms of path tracking, driver’s driving burden, and vehicle stability.

The road information preceding the vehicle serves as a fundamental prerequisite for intelligent driving systems. In comparison to other data requirements of autonomous vehicles, road information exhibits a more two-dimensional character. Among the array of sensors currently equipped on autonomous vehicles, it is only the visual sensors that possess the capability to detect and interpret the road surface. The majority of road recognition algorithms rely heavily on multi-classification methods, which, however, tend to introduce significant data imbalance issues. The process of further refining pavement types exacerbates this problem. Based on the Road Surface Classification Dataset (RSCD), this study uses the diffusion model to generate category images with less data, and uses the traditional image augmentation method to augment the original image dataset. Furthermore, a convolutional neural network-based multi-label classification model, incorporating the Zero-bounded Log-sum-exp& Pairwise Rank-based loss functions, is trained to enhance the model's predictive performance and accuracy. This enhanced model ultimately achieved an average accuracy of 86.8%, demonstrating significant improvements in performance.

In order to promote the safety redundancy of steer-by-wire system, a dual-motor steer-by-wire (DMSBW) system is designed in this paper. However, when one of the steering motors fails seriously, the vehicle's angle tracking performance is affected, which is exacerbated by load fluctuation and limited CAN communication bandwidth in the control system. Therefore, we innovatively establish DMSBW switching control model and propose an event-triggered adaptive fuzzy switching fault-tolerant control strategy. Firstly, the interval type-2 fuzzy state observer is used to estimate the state of the nonlinear DMSBW system and the time-varying fluctuation of the load torque before and after switching. Then, the prescribed performance adaptive backstepping controller is designed to solve the control output by fusing the error and fault event-triggered controller to realize the unification of switching fault tolerance and accurate tracking while reducing communication resources. And the switching fault-tolerant performance under both transient and steady state is guaranteed by proving Lyapunov stability and boundedness. Finally, hardware-in-the-loop experiments show that the proposed method is effective.

Compared with other steering systems, Four-Wheel Independent Steering (4WIS) can control not only yaw motion but also their unique side-move motion to provide intelligent vehicles with stronger trajectory tracking capabilities. However, existing 4WIS wheel angle control often aims at zeroing the sideslip angle of the Center of Gravity (CoG), which inhibits the unique side-move motion of 4WIS vehicles. Especially under large-curvature turning conditions, relying only on yaw motion, the system is prone to entering a state of nonlinear instability; its trajectory tracking accuracy and stability are difficult to guarantee. In response, this paper proposes a 4WIS vehicle instantaneous center of rotation (ICR) tracking control method under large-curvature turning conditions. It converts traditional wheel angle control into ICR control. Through the coordinated control of side-move and yaw motion, trajectory tracking accuracy and stability are ensured under large-curvature turning conditions. The proposed ICR tracking control includes two parts: ICR control and trajectory decomposition. ICR control constructs a polar coordinate system with the vehicle CoG as the pole and establishes a decoupled mapping of side-move and yaw motion to the ICR. By predicting and optimizing the deviation between the ICR and its reference, the side-move and yaw motions are coordinated to achieve effective tracking of large curvature trajectories. Trajectory decomposition mainly converts the trajectory tracking target into the ICR control reference. By combining the particle model and trajectory curvature, polar coordinates are used to decompose the trajectory curvature into side-move and yaw curvatures, and through rolling optimization of lateral and heading errors, the target trajectory is decomposed into side-move and yaw control reference. HIL experimental results show that the proposed ICR tracking control strategy can effectively improve the trajectory tracking performance of 4WIS vehicles under large-curvature turning conditions.

In the stage of man-machine cooperative driving, accurately predicting the trajectory of the ego vehicle can help intelligent system understand future risk and adjust the control authority of the man-machine, thereby improving the performance of the man-machine system and eliminating man-machine conflicts. However, existing high-performance trajectory prediction methods are more focused on fully autonomous vehicles, and it is difficult to deal with the problem of driving trajectory prediction with different risks when the driver is in the loop. So, an ego vehicle trajectory prediction method based on time-feature encoding and physics-intention decoding (TFE-PID) is proposed. Through the bidirectional enhancement of the encoding and decoding process, it can accurately predict the trajectory of the ego vehicle by using only the state data of the vehicle and the driver. In the encoding stage, time and feature information are used for dual encoding, which makes the amount of information carried in the context vector used for decoding more abundant. In the decoding stage, context vector, physical prediction data, and driver’s intention are used to control the flow of information in the network, which enables the model to converge in a direction that is more consistent with the physical characteristics of the vehicle and driver’s intention. The experimental results show that TFE-PID can accurately predict the trajectory of the ego vehicle under different risky driving behaviors of drivers, and has good prediction stability and generalization ability.

This work proposes an active collision avoidance between autonomous driving vehicle and pedestrian with motion uncertainty under urban road. A candidate trajectory planning method considering spatial and time sequences is proposed, which combines the polynomial path planning and the velocity planning with variable safety velocity. Then, a pedestrian-vehicle interaction model is constructed, which takes the pedestrian’s uncertain motion as a superposition of the Markov process without interference and the motion caused by the vehicle, and predicts the pedestrian’s motion probabilistically. On these bases, the optimal trajectory is evaluated from the candidate trajectories by safety, stability, and efficiency, as well as different driving styles. The proposed collision avoidance strategy is verified in conventional and emergency simulation scenarios. Simulation results show that it can effectively plan a safe, stable and efficient trajectory under normal and emergency conditions.

On-ramp merging scenario has a great impact on traffic efficiency and fuel economy. At present, most research on-ramp merging focuses on the optimization of merging sequence in the single main lane scenario, which fails to give full play to the capacity of multi-lane roads. To overcome this problem, an efficient on-ramp merging strategy (ORMS) is proposed to coordinate vehicle merging in multi-lane traffic. First, we built a model of the unevenness of traffic flow between lanes. Based on this model, we established a lane selection model by reinforcement learning for the coordination of vehicles in multi-lane traffic. Before vehicles enter the merging zone, the decision of lane selection is made by analyzing the unevenness of traffic flow between lanes to relieve local congestion in the outside lane that may be caused by ramp vehicle inflow. Then, we adopted a vehicle motion planning algorithm based on the time-energy optimal control, so that all vehicles travel according to the optimal trajectory to reach the merging zone. The simulation results show that the traffic efficiency and fuel economy of the proposed on-ramp merging strategy are significantly improved compared with the existing optimal control algorithm.

In the switching process of driving authority for man-machine cooperative driving, it should be ensured that the driver can take over the vehicle safely, stably and efficiently. This paper proposes a driving authority allocation strategy based on real-time allocation domain (RAD), which mainly includes the establishment of RAD and the dynamic optimization. The RAD is a comprehensive dynamic allocation internal, whose range is determined by the corresponding allocation values of driver’s cognitive state, driver’s muscle state and environment state. The dynamic optimization is to search for the optimal driving authority in RAD at any time, and its optimization objectives and constraints all contain time-varying parameters. This work proposes an improved simulated annealing algorithm to solve this problem. Simulation results show that the proposed driving authority allocation strategy can effectively allocate the driving authority according to the current condition and driver’s state, and ensure the safety, stability and efficiency of the take-over process.

The prediction and estimation of the lane-changing state of the host car and surrounding cars are important parts of an advanced driving assistant system, which mainly depend on the understanding of the driver lane-changing behavior. To learn driver lane-changing maneuver well, this article provides a novel stochastic driver lane-changing model based on an improved input-output hidden Markov model (IOHMM) framework. First, an improved IOHMM is proposed to address the deficiency that the traditional IOHMM cannot remember previous data and describe continuous output. Then, based on the improved IOHMM framework, a driver lane-changing model is established considering the intention and behavior of the driver in the lane-changing process. The model parameters can be learned from the collected lane-changing data using the maximum likelihood estimation and generalized estimation-maximization methods. Finally, the model is applied to a real driver lane-changing process. It is verified that the proposed model has good performance in predicting the future motion maneuver of the host vehicle and estimating the current motion state of the surrounding cars.

In order to explore the nature of the human-vehicle game problem and analyze the torque-angle interaction between the two agents, i.e. the driver and advanced driver assistance system (ADAS), a human-vehicle game stability control strategy based on Nash negotiation principle is proposed. First a six-order vehicle dynamic model, a driver neuromuscular (NMS) model, etc., are set up to simulate drivers’ steering characteristics and vehicles’ response to the input of the two agents and external disturbance. Secondly, several significant parameters in NMS model are recognized, and an active rear steering (ARS) controller is designed using the sliding mode variable structure algorithm. Then, the Nash negotiation solution is worked out according to Nash negotiation principle, and a self-tuning method for the weight of ARS controller is put forward employing the fuzzy control theory. Finally simulations are carried out under the standard double-lane change maneuver. The results indicate that the stability control strategy proposed in this paper can effectively solve the game problem and achieve good vehicular stability control performance.

Connected automated vehicle (CAV) platoon control is becoming increasingly prevalent because of its unique advantages in reducing fuel consumption and improving traffic efficiency. A novel control framework for CAV platoon control is designed in this article. First, a model predictive control (MPC)-based method is proposed to obtain the optimal velocity of the whole platoon, in which both reducing fuel consumption and improving transport efficiency are taken into account in the optimization process. Then, a distributed adaptive triple-step nonlinear control strategy is investigated from the perspective of multiagent system control. The adaptive performance of the control strategy can guarantee the string stability of the CAV platoon under the premise of the existence of dynamic uncertainties. Various simulation conditions with heterogeneous dynamic disturbances are designed to validate the proposed control strategy, and the results show that the proposed control strategy can be robust to dynamic disturbances while ensuring the string stability of the CAV platoon.

In order to decide a safe and reliable trajectory for autonomous driving vehicles, the threat of surrounding vehicles need to be assessed quantitatively and consider the potential risk. This paper proposes a novel integrated threat assessment algorithm for the decision-making system. First, the motion of the surrounding vehicle is predicted probabilistic based on the interact multiple model (IMM) to consider the potential threat. Then, we build an integrated threat assessment function to assess the threat in each state quantitatively and objectively, which synthesizes the existing time-to-collision (TTC), time-headway (TH), and the original proposed time-to-front (TTF). Based on this, the decision-making system is established according to the Markov decision process (MDP) and the feedback value of each decision sequence is calculated by the integrated threat assessment function, thus the safest trajectory for the current moment can be determined by optimal search. Finally, the decision-making system is verified in the overtaking and cut-in scenario by Carsim and Simulink co-simulation. The results show that the proposed threat assessment algorithm for the decision-making system can help autonomous vehicles decide a safe trajectory in real-time and maintain good maneuverability.

This paper proposes a fault-tolerant control method for distributed-drive electric vehicles (DD-EV) considering individual driver steering characteristics that may vary among human drivers. Both vehicle and driver models are explicitly utilized for the fault-tolerant control design with considerations on modeling inaccuracies. A sliding-mode control scheme is generated to tolerate the DD-EV actuator fault and control the vehicle motions in a tailored cooperation with the specific driver. Such a personalized and fault-tolerant control method can specifically assist the human driver in post-fault vehicle motion control and reduce both physical and mental workloads of the driver. Co-simulation results of the controller using Matlab and CarSlm®indicate that the control law can provide appropriate control assistance to different drivers, achieving effective human-vehicle control cooperation in post-fault vehicle maneuvers.