To improve the path tracking accuracy of the four-wheel-steering vehicle while maintaining driving stability, a path-tracking method that fuses model predictive control and extension control is proposed in this paper. First, the front and rear tire forces are linearized and segmented, and a vehicle–road model with four-wheel steering is established. Then, considering the characteristics of path tracking and stability control of the four-wheel-steering vehicle across different vehicle–road states, the extension controller is designed to determine the relationship coefficients between the front and rear wheel steering angles of the four-wheel-steering vehicle based on the theory of extension control. Building upon the established vehicle–road model, a model predictive controller is developed through rigorous optimization to generate coordinated steering commands for the front and rear wheels. This control architecture strategically incorporates lateral slip angles of the front and rear wheels as real-time constraints, while also accounting for path tracking precision and vehicle stability. The co-simulation results of CarSim and Simulink show that the proposed method effectively handles different vehicle–road environments and improves path-tracking accuracy while maintaining vehicle stability.

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.

In the parallel steering coordination control strategy for path tracking, it is difficult to match the current driver steering model using the fixed parameters with the actual driver, and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’ input. In this paper, we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance. Firstly, the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response, and the driver characteristic parameters are fitted after collecting the actual driver driving data. Secondly, considering that the vehicle is susceptible to the influence of external disturbances during the driving process, the Tube MPC (Tube Model Predictive Control) based path tracking steering controller is designed based on the vehicle system dynamics error model. After verifying that the driver steering model meets the driver steering operation characteristics, DQN (Deep Q-network), DDPG (Deep Deterministic Policy Gradient) and TD3 (Twin Delayed Deep Deterministic Policy Gradient) deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’ input of the vehicle. Finally, the tracking accuracy, lateral safety, human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments, and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments. The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation, and the TD3-based coordination control strategy has better overall performance.

Unreasonable path planning will make the vehicle prone to traffic accidents when driving at a limited maximum speed in the case of the low-speed situation and large curvature curve conditions. Considering the defects of neural network model based on the data-driven may cause unexpected results, an improved driver model was proposed to enhance driving safety. In this paper, the Dempster/Shafer evidence theory was used to detect critical features of lane lines for situation detection. And an observer was established to observe and analyze the model output based on the vehicle space motion safety and driving stability characteristics. Then, an optimizer was established to optimize the output and provide the optimal driving trajectory according to the analyzed situations. Finally, it is verified that the proposed algorithm can help the vehicle safely pass the ample curvature curves by the simulation platform and real vehicle in the laboratory.

In view of the generalized functional safety problems faced by intelligent vehicles, this paper focuses on vehicle driving safety and functional insufficiency of the steering system, and proposes a path tracking coordinated control method with TD3 reinforcement learning based on vehicle danger recognition. Based on the parameters of vehicle driving stability, the concept of vehicle danger level (DL) is established by integrating the parameters of path tracking accuracy. Affinity propagation (AP) clustering algorithm is used to cluster the vehicle driving status under different road adhesion coefficients, speeds and curvatures. The results of DL recognition are added to the design of TD3 reinforcement learning reward function, and the coordination control of automatic steering and differential braking under different conditions is realized, so as to achieve high-precision path tracking control under the premise of ensuring vehicle safety. The results of CarSim/Simulink co-simulation and hardware-in-loop test show that the vehicle DL can be more accurately recognized by using AP clustering algorithm and the reinforcement learning control strategy combined with DL recognition is superior to the traditional fixed parameter MPC controller and the reinforcement learning control strategy without considering the clustering method in improving the path tracking accuracy and lateral stability of the vehicle. The training convergence speed is improved in both the variable curvature road and the docking road, in which the functional insufficiency problem of the steering system is fully alleviated in the variable curvature road, and the energy loss caused by the additional yaw moment is less in the docking road.

In the longitudinal driving of a heterogeneous platoon, external disturbances of local vehicles can cause safety problems of the intended functionality. Therefore, a cooperative control method based on situation assessment is proposed. First, the minimum safe distance is determined according to the state of adjacent vehicles. A novel situation assessment model is established to characterize the stability of the platoon. Vehicle spacing, speed, and acceleration are selected as evaluation indicators. Then, an improved active disturbance rejection controller (IADRC) is designed to improve platoon stability through feedback compensation control. In addition, considering the unnecessary continuous involvement of the IADRC, its intervention time is determined by the situation assessment results. Finally, simulation and hardware-in-the-loop (HIL) experiments are carried out under two communication topologies. The results show that the proposed method can effectively improve the stability and safety of the platoon.

The control goal of the vehicle platoon is to maintain the same speed and desired distance. Most current studies are based on simplified vehicle models, and the leader’s state is also rarely considered. However, under complex working conditions, such as low adhesion or curves, the lateral stability of the platoon will be difficult to guarantee, and tracking errors of desired speed and spacing may further increase. To solve the above problems, a new hierarchical coordinated control strategy is proposed. Taking distributed drive electric vehicles (DDEVs) as research objects, the upper control level establishes a stability situation assessment model according to the vehicle’s dynamic characteristics. At the medium control level, variable weight model predictive control (MPC) coordinates conflicts between longitudinal tracking and lateral stability. A correction term is also introduced to revise the prediction model. At the same time, the weight of different control objectives of the leader and following vehicle was adjusted, respectively. Torque distribution is carried out at the lower level controller. Finally, the control strategy is tested on a hardware-in-the-loop (HIL) platform. The results show that the proposed control strategy can ensure lateral stability while improving the tracking performance of the vehicle platoon.

The driver authority decision for driver-initiated takeover is closely related to vehicle driving safety. In this paper, a lateral driver-automation driver authority decision method considering the safety of the intended functionality is proposed to improve vehicle driving safety. Based on systems-theoretic processes analysis, functional safety analysis of driver initiative takeover is carried out to clarify the safety of the intended functionality issues caused by the unreasonable decision. Then, a method for defining the safe driving area is developed to assess the safety level of the driving area around the ego vehicle. Taking the driver-vehicle state parameters and vehicle-vehicle state parameters as input, a deep neural network model is constructed to determine the intention of the driver to take over the vehicle. The lateral driver-automation driver authority decision method is designed to make the driver-machine take-over decisions, which considers the safety of the intended functionality. The effectiveness of the proposed method is evaluated via numerical simulation and hardware-in-the-loop experiments. The results show that the designed method not only improves the control ability of the driver to the maximum extent but also integrates the perception ability of the driver into the vehicle control system to improve further vehicle driving safety. The driver authority decision for driver-initiated takeover is closely related to vehicle driving safety. A lateral driver-automation driver authority decision method considering the safety of the intended functionality is proposed to improve vehicle driving safety. The obtained experimental results show that the designed decision integrates the perception ability of the driver into the vehicle control system to improve further vehicle driving safety. image

Distributed drive electric vehicles can reduce range anxiety through regenerative braking. However, if the wheel motor torque output fails, it will form an additional yaw moment to the vehicle, causing instability, or deviation and threatening its safety. To solve this problem, the research object is an electric vehicle driven by a four-wheel hub motor. A braking force compensation distribution strategy for front and rear axles is proposed, which combines electronic hydraulic braking (EHB) system compensation control and deviation auxiliary control. Firstly, a fault detection module is established, and the motor’s output torque is estimated by designing a torque observer to obtain the fault degree information of the motor. Secondly, to fully use the motor’s regenerative braking force, the fault-free and faulty electro-hydraulic braking force distribution strategies are designed in the coordinated distribution layer of the electro-hydraulic braking system. The corresponding electro-hydraulic braking force compensation method is selected according to the fault degree of the regenerative braking function, the position of the faulty wheel, and the braking strength. Then, a deviation auxiliary controller is designed based on the model predictive control, and the intervention time of the auxiliary controller is determined according to the vehicle’s state. Finally, the control method is verified based on CarSim/Simulink co-simulation and hardware-in-the-loop (HIL) platform. The test results show that the designed control method can effectively compensate for the regenerative braking failure of random wheel and ensure the braking safety of the vehicle.

针对大多数换道决策研究中存在的相邻车道的交通状态分析不足致使换道安全性难以保证的问题,提出一种基于相邻车道安全态势划分的换道决策方法.根据车-车相对位置关系,建立关联车辆分类准则,确定当前车道、相邻车道和相邻第二车道上需要监测横、纵向运动状态的车辆.设计深度神经网络和基于左、右换道差异的横向偏离判断标准对关联车辆的车道保持、车道偏离和换道的横向运动行为进行预测.考虑关联车辆不同横向运动行为对自车相邻车道安全态势的影响,结合车-车相对纵向运动状态,设计自车相邻车道的安全态势划分方法,确定自车相邻车道的安全等级.在此基础上,设计换道决策准则,实现车辆换道的精准决策.仿真结果表明,所提出的换道决策方法能够准确的预测关联车辆的横向运动行为,可以在不同行驶环境下实现更加精准的换道决策,提高了车辆换道的安全性,并通过实车试验,进一步验证了所提出的换道决策方法的有效性.

The existing 3D lidar-based obstacle detection and tracking methods are inaccurate. Furthermore, as the number of obstacles increases, particularly in the agricultural machinery working scene with many machineries, ridges, and pedestrians, these methods become difficult to track. To address the aforementioned issues, a new obstacle detection and tracking method based on grid map is proposed. Firstly, a dynamic clustering algorithm that combines region-growing and density-clustering is developed. Secondly, geometric features and point cloud density are used to associates obstacles. Thirdly, a Kalman filter is designed to estimate the motion states of obstacles. Finally, a platform for agricultural machinery has been used to conduct comparative experiments. The proposed method improves detection accuracy by 4.86% and reduces average time by 47.8% when compared to Density Based Spatial Clustering of Applications with Noise (DBSCAN). And the tracking error of moving obstacles is less than 5%.

This paper presents a fault-tolerant human-machine shared scheme for collision avoidance considering driver error and actuator fault. A fault-tolerant method of trajectory planning and decision-making is developed for dealing with the actuator fault. We calculate the remainder trajectory tracking capability of the faulted actuator and utilise the motion redundancy in the system to generate feasible fault-tolerant trajectories for collision avoidance. In addition, an assessment criterion of driver error is conducted to supervise the driver's steering response to the potential collision and actuator fault. The shared system dynamically adjusts the driving authority based on the driver error. A model predictive control (MPC) algorithm is employed for tracking the fault-tolerant trajectory. Simulation and hardware-in-the-loop (HIL) experiment results demonstrate the effectiveness of the presented approach, reducing traffic accidents due to driver error and actuator fault.

An unreasonable vehicle spacing can easily lead to traffic accidents such as collisions. The safe vehicle spacing is determined by vehicle motion characteristics, ground conditions, and other factors, and it is also effected by traffic situations. In order to improve driving safety, the spacing that host vehicle needs to maintain by studying the braking process of the vehicle and under the interference of the related vehicles is analyzed in this paper at first. Then, the environmental factors that may have impacts are considered to construct mathematical model of traffic situation. The spacing data were analyzed by using analysis of variance models to verify the effect of environmental factors on vehicle spacing. Then, according to the interaction of different traffic situations, the influence levels of the traffic situations are determined. Safe vehicle spacing under different levels are optimized based on the time to collision. Finally, the proposed method is simulated on the hardware-in-the-loop simulation platform to verify that optimized method can effectively help to reduce the probability of traffic accidents.

This paper proposes an adaptive friction torque compensation scheme for the vehicle steer-by-wire (SBW) system. Firstly, the overall system structure is analyzed into a steering wheel subsystem and an active steering subsystem. Specifically, the road sense torque of the SBW system is represented by a plant model from the road sense motor input current to the steering wheel torque. Next, a road torque expression related to vehicle speed and road conditions is designed for the steering wheel subsystem. Furthermore, the torque expression has been corrected using damping model and coulomb friction model. Then, an adaptive friction compensation controller is designed for the SBW system, which adopts a LuGre dual observer and backstepping method (BS) to improve tracking accuracy by dealing with complex friction parameters estimation problems, and use an adaptive sliding mode control (SMC) component to deal with external disturbance and chattering. The stability and robustness of the adaptive friction compensation control system are verified through Lyapunov and frequency domain analysis. Finally, numerical simulations, hardware-in-the-loop (HIL) and vehicle experiments are carried out under various conditions. Numerical and experimental results demonstrate that the designed road sense conforms to the operating standard, and the proposed adaptive friction compensation controller has not only high compensation tracking accuracy but also high robustness. The proposed approach effectively reduces the steering wheel torque fluctuation caused by the mechanical friction, and can adaptively match different actuators.

Appropriate vehicle lateral stability control is the key to ensure vehicle driving safety, whereas accurate lateral stability recognition can help improve the performance of vehicle control. In this article, the vehicle stability recognition and coordinated control are studied. Firstly, the vehicle dynamic model is established, through vehicle simulation software Carsim, the attribute dataset representing the vehicle lateral stability is further obtained. Then, clustering by fast search and find of density peaks method (CFSFDP) based procedure for the classification of the lateral stability as ‘Absolutely stability’, ‘Nearly stability’ and ‘Hardly stability’ is applied. The brain emotional learning network combined with genetic algorithm (GA-BEL) model is used to train datasets to recognize vehicle stability categories during driving. For the different recognition results, a coordinated control strategy based on active front steering control (AFS) and direct yaw moment control (DYC) is designed finally. Through the Carsim/Simulink co-simulations and Hardware-in-the-Loop tests under several typical driving conditions, the superiorities of the stability recognition method and the coordinated control strategy proposed in this paper are verified.

Recent developments have demonstrated that the brake pedal simulator (BPS) is becoming an indispensable apparatus for the break-by-wire systems in future electric vehicles. Its main function is to provide the driver with a comfortable pedal feel to improve braking safety and comfort. This paper presents the development and control of an adjustable BPS, using a disk-type magnetorheological (MR) damper as the passive braking reaction generator to simulate the traditional pedal feel. A detailed description of the mechanical design of the MR damper-based BSP (MRDBBPS) is presented in this paper. Several basic performance experiments on the MRDBBPS prototype are conducted. A return-to-zero (RTZ) algorithm is proposed to avoid hysteresis and improve the repeatability of the pedal force. In addition, an RTZ algorithm-based real-time current-tracking controller (RTZRC) is designed in consideration of the response lag of the coil circuit. Finally, an experimental system is established by integrating the MRDBBPS prototype into a self-developed automotive MR braking test bench (AMRBTB), and several control and braking experiments are performed. This research proposes a RTZRC control algorithm which can significantly increase the tracking accuracy of the brake pedal characteristic curve, particularly at a high pedal velocity. Additionally, the designed MRDBBPS prototype can achieve an effective and favorable control of the AMRBTB with a good repeatability.

为提高平衡重式叉车在急转工况下抗侧翻能力,在叉车结构分析的基础上建立两阶段侧翻动力学模型,基于两阶段横向载荷转移率LTR1、LTR2进行叉车侧翻机理分析与稳定域划分,将叉车稳定性状态分为稳定域、相对稳定域、危险域和异常危险域,提出基于稳定域划分的叉车防侧翻分级控制策略,根据不同的稳定域选择不同的防侧翻控制执行机构:动平衡块、液压支撑油缸和转向油缸.设计叉车防侧翻控制器,由上层稳定域识别控制器、中层模型预测控制器(Model predictive control,MPC)与下层执行控制器组成;上层稳定域识别控制器基于两阶段横向载荷转移率进行叉车稳定域识别,中层MPC控制器以车身侧偏角和横摆角速度为控制目标计算所需控制力矩,下层执行控制器采用改进链式递增分配方式对动平衡块、液压支撑油缸和转向油缸进行控制,以满足目标控制力矩.基于Matlab/Simulink进行仿真与实车试验验证,结果表明基于稳定域划分的平衡重式叉车防侧翻控制可大大降低叉车侧翻危险,提高叉车的安全性.

Vehicle detection plays a crucial role in the decision-making, planning, and control of intelligent vehicles. It is one of the main tasks of environmental perception and an essential part of ensuring driving safety. In order to capture unique vehicle features and improve vehicle recognition efficiency, this paper fuses texture features of image and edge features of LIDAR to detect frontal vehicle targets. First, we use wavelet analysis and geometric analysis to segment the ground and determine the region of interest for vehicle detection. Then, the point cloud of the vehicle detected is projected into the image to locate the ROI. Moreover, the edge feature of the vehicle is guided to extract according to the maximum gradient direction of the vehicle’s rear contour. Furthermore, the Haar texture feature is integrated to identify the vehicle, and a filter is designed according to the point cloud’s spatial distribution to eliminate the error targets. Finally, it is verified by real-vehicle comparison tests that the proposed fusion method can effectively improve the vehicles’ detection with not much time.

A damping fuzzy proportional-integral-derivative (PID) control method for electronically controlled air suspension shock absorbers was proposed to reduce the RMS values of the body vibration acceleration and improve vehicle ride comfort. Depending on the operating mode of the electronically controlled air suspension shock absorber, a mechanical model of the air suspension shock absorber was established. A fuzzy PID control technology for damping control of the shock absorber is designed by combining a PID control algorithm with the fuzzy control concept. The adaptive expansion factor of the cybernetics domain for the fuzzy PID controller is calculated by considering nonlinear characteristics and time delay to improve the controller’s adaptability. Then, the control parameters are adjusted based on the control deviation and deviation rate parameters. The nonlinear detection platform produced by an enterprise is selected to build an experimental environment for analysis. Simulation results reveal that when the vehicle speed is 30 km h −1 , the RMS values of the body vibration acceleration upon applying the proposed method is approximately 0.02 and 0.05 km h −2 , respectively, on Class B and C road surfaces. Furthermore, the RMS values of the body vibration acceleration using the proposed method has little fluctuation; when the vehicle speed is 60 km h −1 , the RMS values of the body vibration acceleration is 0.022 and 0.028 km h −2 , respectively, on Class B and C road surfaces, indicating that the proposed method can effectively reduce the RMS values of the body vibration acceleration and improve rider comfort.

Safe and comfortable driving experience includes the improvement of handling performance and stability control. This paper proposed a coordinated controller based on the function allocation for the handling performance and stability of distributed drive electric vehicles. The proposed controller has three layers. The upper control layer designs a dynamic stability envelope boundary suitable for various driving conditions through the phase plane method, and on this basis, the function allocation rules of torque vector control (TVC) strategy and electronic stability control (ESC) strategy were formulated. The medium control layer used the robust [Formula: see text] dynamic output feedback control method and the improved particle swarm optimization (PSO) parameter self-adjusting method to calculate the additional yaw moment required by the TVC strategy and the ESC strategy, respectively. The two types of additional yaw moment are implemented by the in-wheel motor and the hydraulic brake mechanism respectively. The lower control layer optimized the four-wheel torque and braking force based on the optimal tire load rate using the quadratic programming method. The proposed coordinated controller was performed in the CarSim/Simulink co-simulation platform and tested in a real vehicle platform. The results show that the proposed controller can improve the vehicle dynamic response according to the driver’s intention, thus bringing the better handling performance and stability.