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Lane-changing is a typical traffic scene effecting on road traffic with high request for reliability, robustness and driving comfort to improve the road safety and transportation efficiency. The development of connected autonomous vehicles with V2V communication provide more advanced control strategies to research of lane-changing. Meanwhile, four-wheel steering is an effective way to improve flexibility of vehicle. The front and rear wheels rotate in opposite direction to reduce the turning radius to improve the servo agility operation at the low speed while those rotate in same direction to reduce the probability of the slip accident to improve the stability at the high speed. Hence, this paper established Four-Wheel-Steering(4WS) vehicle dynamic model and quasi real lane-changing scenes to analyze the motion constraints of the vehicles.

This paper proposes a dynamic obstacle avoidance system to help autonomous vehicles drive on high-speed structured roads. The system is mainly composed of trajectory planning and tracking controllers. The potential field (PF) model is introduced to establish a three-dimensional potential field for structured roads and obstacle vehicles. The trajectory planning problem that considers the vehicle’s and tires’ dynamics constraints is transformed into an optimization problem with muti-constraints by combining the model predictive control (MPC) algorithms. The trajectory tracking controller used in this paper is based on the 7 degrees of freedom (DOF) vehicle model and the UniTire tire model, which was discussed in detail in previous work [25, 26]. The controller maintains good trajectory tracking performance even under extreme driving conditions, such as roads with poor adhesion conditions, where the car’s tires enter the nonlinear region easily.

Steer-by-wire System is a new conception for steering system, which eliminates those mechanical linkages between hand steering wheel and front wheels, and communicates among the driver and wheels by signals and controllers. All these facilities improve the safety and conformability of the vehicle system and get rid of the mechanical constricts. This paper proposed three vehicle stability control strategies, including front wheel control, yaw rate feedback control and yaw rate& acceleration feedback control. We compared these three control methods by simulation and simulator tests. We also studied the integrated control algorithm of Steer-by-Wire System and 4WS, and compared with 2WS for SBW and the classical 4WS.

On-center handling has drawn lots of attention from consumers and car manufactures for its extraordinarily large effect on vehicle security at high speed. So far, there are a large number of objective evaluation indices for on-center handling, but it is not clear which ones are the key points on evaluation indices. In this paper, the authors propose a simplified on-center handling objective evaluation index system. Firstly, a basic on-center handling objective evaluation index system is summarized based on the ones of ISO, GM, MIRA, TRC and Hyundai, and then dynamics analysis on each index is conducted so as to primarily eliminate the redundant indices. Secondly, the repetitive indices are cut out again by the correlation analysis among indices in objective tests for eight types of vehicles. Thirdly, the importance factor of each subjective evaluation index is gained on the basis of subjective evaluation tests for eight types of vehicles by the weighed principal component analysis.

The steer-by-wire system has been widely studied due to many advantages such as good controllability. In the system, the steering column is cancelled and the driver can't feel the feedback torque (also called steering feeling) coming from the ground. Therefore a steering feeling feedback system is needed. In this paper, we propose a simple method to calculate desired feedback torque based on a nonlinear 2DOF vehicle model. The vehicle model contains the nonlinearity of tire. So that the proposed method is also appropriate for big acceleration conditions. Besides that, the properties of steering system such as friction and stiffness are also taken into consideration. As for conventional steering system, driver can only feel part of the feedback torque due to the power assist system. In order to provide steering feeling similar to conventional steering system, a weighting function is proposed to compensate the influence of power assist system.

Reliable and safe Redundant Steering System (RSS) equipped with Dual-Winding Permanent Magnet Synchronous Motor (DW-PMSM) is considered an ideal actuator for future autonomous vehicle chassis. The built-in DW-PMSM of the RSS is required to identify various winding’s faults such as disconnection, open circuit, and grounding. When achieving redundant control through winding switching, it is necessary to suppress speed fluctuations during the process of winding switching to ensure angle control precision. In this paper, a steering angle safety control for RSS considering motor winding’s faults is proposed. First, we analyze working principle of RSS. Corresponding steering system model and fault model of DW-PMSM have been established. Next, we design the fault diagnosis and fault tolerance strategy of RSS.

In the field of autonomous driving trajectory planning, it’s virtual to ensure real-time planning while guaranteeing feasibility and robustness. Current widely adopted approaches include decoupling path planning and velocity planning based on optimization method, which can’t always yield optimal solutions, especially in complex dynamic scenarios. Furthermore, search-based and sampling-based solutions encounter limitations due to their low resolution and high computational costs. This paper presents a novel spatio-temporal trajectory planning approach that integrates both search-based planning and optimization-based planning method. This approach retains the advantages of search-based method, allowing for the identification of a global optimal solution through search. To address the challenge posed by the non-convex nature of the original solution space, we introduce a spatio-temporal semantic corridor structure, which constructs a convex feasible set for the problem.

Electric Power-Assisted Steering (EPAS) is a new power steering technology that will define the future of vehicle steering. The assist of EPAS is the function of the steering wheel torque and vehicle velocity. The assist characteristic of EPAS is set by control software, which is one of the key issues of EPAS. The straight-line type assist characteristic has been used in some current EPAS products, but its influence on the steering maneuverability and road feel hasn't been explicitly studied in theory. In this paper, the straight-line type assist characteristic is analyzed theoretically. Then a whole vehicle dynamic model used to study the straight-line type assist characteristic is built with ADAMS/Car and validated with DCF (Driver Control Files) mode of ADAMS/Car. Based on the whole vehicle dynamic model, the straight-line type assist characteristic's influence on the steering maneuverability and road feel is investigated.

Magneto-rhelological(MR) dampers are devices that use rheological fluids to modify the mechanical properties of fluid absorber. The mechanical simplicity, high dynamic range, large force capacity, lower power requirements, robustness and safe manner of operation have made MR dampers attractive devices for semi-active real-time control in civil, aerospace and automotive applications. Landing gear is one of the most essential components of the aircraft, which plays an extreme important role in preventing the airframe from vibration and excessive impact forces, improving passenger comfortable characteristics and increasing aircraft flight safety. In this paper, the semi-active system used in landing gear damping controller design, simulation, and the vibration test-bed are discussed and researched. The MR dampers employed in landing gear system were designed, manufactured and characterized as available semi-active actuators.

The steer-by-wire (SBW) system, an integral component of the drive-by-wire chassis responsible for controlling the lateral motion of a vehicle, plays a pivotal role in enhancing vehicle safety. However, it poses a unique challenge concerning steering wheel return control, primarily due to its fundamental characteristic of severing the mechanical connection between the steering wheel and the turning wheel. This disconnect results in the inability to directly transmit the self-aligning torque to the steering wheel, giving rise to complications in ensuring a seamless return process. In order to realize precise control of steering wheel return, solving the problem of insufficient low-speed return and high-speed return overshoot of the steering wheel of the SBW system, this paper proposes a steering wheel active return control strategy for SBW system based on the backstepping control method.

With rapid development of the automobile industry and the growing maturity of the automotive electronic technologies, the distributed-traction electric vehicle with four-wheel-distributed steering/braking/traction systems is regarded as an important development direction. With its unique chassis structure, it is the ideal benchmark platform used to evaluate active safety systems. The distributed-traction electric vehicle with four-wheel-distributed steering system is essentially full drive-by-wire vehicle. With its flexible chassis layout and high control degrees-of-freedom, the full drive-by-wire electric vehicle acted as a kind of redundant system is an ideal platform for the research of integrated control. In this treatise, the longitudinal dynamics of the electric vehicle as well as its lateral and yaw motions are controlled simultaneously.

The performance of suspension system has a direct impact on the riding comfort and smoothness. For the traditional suspension can not effectively alleviate the impact of road surface and the poor anti-vibration performance, The dynamics model of vehicle suspension system is established, and the control model of vehicle four-degree-of-freedom active suspension is designed with fuzzy control strategy. On this basis, a comprehensive simulation model of the control model of vehicle active suspension coupled with road excitation is established. and the ride comfort of vehicles under different types of suspension are tested through Simulink. The simulation results show that compared with the passive suspension, the reduction of vehicle acceleration and dynamic deformation of the active suspension controlled by fuzzy PID can reach 33.76% and 22.45%. and the reduction of pitch Angle speed and dynamic load of the active suspension controlled by fuzzy PID can reach 16.18% and 10.72%.

With the popularity of electrification and driver assistance systems on vehicle dynamics and controls, the steering performance of the vehicle put forward higher requirements. Thus, the steer-by-wire technology is becoming particularly important. Through specific control algorithm, the steer-by-wire system electronic control unit can receive signals from other sensors on the vehicle, realize the personalized vehicle dynamics control on the basis of understanding the driver’s intention, and grasp the vehicle movement state. At the same time, to make these driver assistance systems better cooperate with human drivers, reduce system frequent false warning, full consideration of mutual adaptation for the systems and the driver’s characteristics is critical. This paper focuses on the steering performance of steer-by-wire vehicle. Feature parameters are obtained from the virtual turning experiment designed on the driving simulator experimental platform.

For the roll vibration problem of a Truck, a 4-DOF roll vibration model of its front suspension system was built. According to dynamics theory, the complex modal vibration modes of the model were all obtained. At the same time, the frequency response functions of frame roll angle acceleration, the relative dynamic load of wheel and the suspension dynamic deflection were respectively presented. Then their characteristics were respectively researched. In the process of characteristic analysis, a new system parameter was proposed, which is the space ratio of the space between suspensions of left and right sides and the wheel track of the front axle (space ratio in short). At last, the influence of system parameters on the vibration transmission property was also reserached, which included the natural frequency of the frame, the damping ratio, the stiffness ratio, the mass ratio, the rotational inertia ratio and the space ratio.

Autonomous driving in real-world urban traffic must cope with dynamic environments. This presents a challenging decision-making problem, e.g. deciding when to perform an overtaking maneuver or how to safely merge into traffic. The traditional autonomous driving algorithm framework decouples prediction and decision-making, which means that the decision-making and planning tasks will be carried out after the prediction task is over. The disadvantage of this approach is that it does not consider the possible impact of ego vehicle decisions on the future states of other agents. In this article, a decision-making and planning method which considers longitudinal interaction is represented. The method’s architecture is mainly composed of the following parts: trajectory sampling, forward simulation, trajectory scoring and trajectory selection. For trajectory sampling, a lattice planner is used to sample three-dimensionally in both the time horizon and the space horizon.

Active steering technology can improve the operability of the driver by the involvement to the steering system. Driver is the major controller of the vehicle Therefore, the involvement of advanced technologies including the active steering technology shouldn’t interfere with the intention of the driver, and the driver should still have great control of the vehicle. The aim of this paper is to solve the problem of the driver’s control when the active steering system works to improve the flexibility of the low speed and the stability of the high speed, and the active steering model based on the driver’s steering intention is established. Through the CarSim simulation software, this paper adopts 9 parameters related to the vehicle steering of the DLC (Double Line Change). And PCA (Principal Component Analysis) algorithm, a tool of statistical analysis, is applied to select 4 parameters which can stand for the DLC from the 9 parameters, which makes the data processing easier.

Roundabouts are one of the most complex traffic environments in urban roads, and a key challenge for intelligent driving decision-making. Deep reinforcement learning, as an emerging solution for intelligent driving decisions, has the advantage of avoiding complex algorithm design and sustainable iteration. For the decision difficulty in roundabout scenarios, this paper proposes an artificial potential field based Soft Actor-Critic (APF-SAC) algorithm. Firstly, based on the Carla simulator and Gym framework, a reinforcement learning simulation system for roundabout driving is built. Secondly, to reduce reinforcement learning exploration difficulty, global path planning and path smoothing algorithms are designed to generate and optimize the path to guide the agent.

Artificial intelligence systems are highly accepted as a technology to offer an alternative way to tackle complex and non-linear problems. They can learn from data, and they are able to handle noisy and incomplete data. Once trained, they can perform prediction and generalization at high speed. The aim of the present study is to propose a novel approach utilizing the adaptive neuro-fuzzy inference system (ANFIS) and the fuzzy clustering method for automotive ride performance estimation. This study investigated the relationship between the automotive ride performance and relative parameters including speed, spring stiffness, damper coefficients, ratios of sprung and unsprung mass. A Takagi-Sugeno fuzzy inference system associated with artificial neuro network was employed. The C-mean fuzzy clustering method was used for grouping the data and identifying membership functions.

In this paper, a shock absorber physical model is developed. Firstly, a rebound valve model which is based on its structure parameters is built through using the large deflection theory. The von Karman equations are introduced to discover the physical relationships between the load and the deflection of valve discs. An analytical solution of the von Karman equations is then deducted via perturbation method. Secondly, the flow equations and the pressure equations of the shock absorber operating are investigated. The relationship between fluid flow rate and pressure drop of rebound valve is analyzed based on the analytical solution of valve discs deflection. Thirdly, an inter-iterative process of flow rate and pressure drop is employed in order to adequately consider the influence of fluid flow on damping force. Finally, the physical model is validated by comparing the experimental data with the simulation output.

To reduce the interference and conflict of human-machine cooperative control, lighten the operation workload of drivers, and improve the friendliness and acceptability of intelligent vehicles, a personalized human-machine cooperative lane-change trajectory tracking control method was proposed. First, a lane-changing driving data acquisition test was carried out to collect different driving behaviors of different drivers and form the data pool for the machine learning method. Two typical driving behaviors from an aggressive driver and a moderate driver are selected to be studied. Then, a control structure combined by feedforward and feedback control based on Long Short Term Memory (LSTM) and model-based optimum control was introduced. LSTM is a machine learning method that has the ability of memory. It is used to capture the lane-changing behaviors of each driver to achieve personalization. For each driver, a specific personalized controller is trained using his driving data.