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BBW (Brake-by-wire) can increase the vehicle safety performance due to high control accuracy and fast response speed. As one solution of BBW, the novel Integrated-electro-hydraulic brake system (I-EHB) is proposed, which consists of electro-hydraulic booster and hydraulic pressure control unit. The electro-hydraulic booster is activated by an electric motor that driving linear motion mechanism to directly produce the master cylinder pressure. With electro-hydraulic booster as an actuator, the hydraulic pressure control problem is a key issue. Most literatures deal with the pressure control issue based on the feedback pressure signal measured by pressure sensor. As far as the authors are aware, none of the proposed techniques takes into account the pressure sensor unequipped BBW. In this paper, there is no pressure feedback signal, but there is only position feedback signal measured by position sensor for control law design.

Steering of skid-steered vehicles without steering mechanism is realized by differential drive/brake torque generated from in-wheel motors at left and right sides. Compared to traditional Ackerman-steered vehicles, skid-steered vehicles consume much more energy while steering due to greater steering resistance. Torque allocation is critical to the distributed drive skid-steered vehicles, since it influences not only steering performance, but also energy efficiency. In this paper, the dynamic characteristics of six-wheeled skid-steered vehicles were analyzed, and a 2-DOF vehicle model was established, which is important for both motion tracking control and torque allocation. Furthermore, a hierarchical controller was proposed. Considering tire force characteristics and tire slip, the upper layer calculates the generalized force and desired yaw moment based on anti-windup PI (proportion-integral) control method.

In recent years, intelligent driving technology is being extensively studied. This paper proposes a path planning method for perpendicular parking based on vehicle kinematics model using MPC optimization, which aims to solve the perpendicular parking task. Firstly, in the case of any initial position and orientation of the vehicle, judging whether the vehicle can be parked at one step according to the location of the parking place and the width of the lane, and then calculating the starting position for parking, and use the Bezier curve to connect the initial position and the starting position. Secondly, reference parking path is calculated according to the collision constraints of the parking space. Finally, because the parking path based on the vehicle kinematics model is composed of circle arcs and straight lines, the curvature of the path is discontinuous. The reference parking path is optimized using Model Predictive Control (MPC).

Most centrifugal pendulum vibration absorber (CPVA) research focuses on the horizontal or vertical plane, ignoring the influence of gravity. However, with the wide application of CPVAs in the automobile industry, some gravity-related problems have been encountered in practice. In this study, employing the second kind of Lagrange equation, the differential equation of motion of a CPVA is established, and the first-order approximate analytical solution is solved using the method of multiple scales. The mathematical relations among the excitation torque amplitude and phase, gravity influence, absorber trajectory shape, absorber position, viscous damping coefficient, and mistuning level parameters are provided for study. Specifically, the second-order responses of four absorbers and two absorbers in a gravity field are studied, and the influence of the change in the torque excitation phase on the response of the absorber is thoroughly analyzed.

The research and development of data-driven highly automated driving system components such as trajectory prediction, motion planning, driving test scenario generation, and safety validation all require large amounts of naturalistic vehicle trajectory data. Therefore, a variety of data collection methods have emerged to meet the growing demand. Among these, camera-equipped drones are gaining more and more attention because of their obvious advantages. Specifically, compared to others, drones have a wider field of bird's eye view, which is less likely to be blocked, and they could collect more complete and natural vehicle trajectory data. Besides, they are not easily observed by traffic participants and ensure that the human driver behavior data collected is realistic and natural. In this paper, we present a complete vehicle trajectory data extraction framework based on aerial videos. It consists of three parts: 1) objects detection, 2) data association, and 3) data cleaning.

A tractor-trailer vehicle (TTV) consists of an actuated tractor attached with several full trailers. Because of its nonlinear and noncompleted constraints, it is a challenging task to avoid collisions for path planner. In this paper, we propose an efficient method to plan an optimal trajectory for TTV to reach the destination without any collision. To deal with the complicated constraints, the trajectory planning problem is formulated as an optimal control problem uniformly, which can be solved by the interior point method. A novel incremental optimization solving algorithm (IOSA) is proposed to accelerate the optimization process, which makes the number of trailers and the size of obstacles increase asynchronously. Simulation experiments are carried out in two scenarios with static obstacles. Compared with other methods, the results show that the planning method with IOSA outperforms in the efficiency.

In the hybrid brake system of electric vehicle, due to the limitation of the motor braking force when the motor is at high speed and the failure of the regenerative braking force when the motor is at low speed, there are three transitional conditions in hybrid braking: the hydraulic brake system intervenes the braking, the hydraulic brake system withdraws the braking and the regenerative braking force withdraws the braking. Due to the response speed of the hydraulic system is slower than that of the motor, there is a large braking impact (the derivative of braking deceleration) in the transitional conditions of hybrid braking, which deteriorates the smoothness and comfort in braking. Aiming at the impact caused by the poor cooperation between the hydraulic braking force and the motor braking force, a coordinated strategy of double closed-loop feedback and motor force correction is proposed in this paper.

On the dual-motor electric vehicle, which is driven by two electric motors mounted on the front and rear axles respectively, longitudinal dynamic control and electro-dynamic braking can be achieved by controlling the torque of front and rear axle motors respectively. Suspension displacement is related to the wheel torque, thus the pitch of vehicle body can be influenced by changing the torque distribution ratio. The pitch of the body has a great influence on the vehicle comfort, which occurs mainly during acceleration and braking progress. Traditionally active suspension is adopted to control the pitch of body. Instead, in this paper an ideal torque distribution strategy is developed to limit the pitch during acceleration and braking progress. This paper first explores the relationship between the torque distribution and the body pitch through the real vehicle test, which reveals the feasibility of the vehicle comfort promotion by optimizing the torque distribution coefficient.

With the electrification and intelligentialization of vehicle, requirements on more intelligent and integrated brake system are put forward. A novel integrated-electro-hydraulic brake system (I-EHB) for automotive is presented to fulfill these requirements. I-EHB is consisted of active power source (APS), pedal feel emulator (PFE), electro control unit (ECU) and hydraulic control unit (HCU). The system characteristics of I-EHB are tested through test rig. According to characteristics experiments, friction and non-linear phenomena in hydraulic pressure control are found. In order to overcome these phenomena in control of I-EHB, chatter-compensation is adopted based on experiment analysis. Algorithm are tested and optimized through test rig. As a result, through chatter-compensation the hydraulic pressure is controlled accurately and chatter-compensation is optimized for different working conditions.

In this paper, by analyzing multiple electro-hydraulic brake system schemes in detail, the idea of dual-motor electro-hydraulic brake system is proposed. As a new solution, the dual-motor electro-hydraulic brake system can actively simulate pedal feel, make the most of pedal power (from the driver), and reduce the maximum power output of each active power source remarkably, which is a distinctive innovation compared to most current electro-hydraulic brake systems. Following the proposed concept, a general research thought and method is conceived, and then a dual-motor electro-hydraulic brake system is designed. Finally, the simulation model is set up in AMESim software and its feasibility is simulated and verified.

To research the dynamic response of active power source of electronic hydraulic brake system, the paper proposes a restricted distribution control strategy. Building control strategy model and active power source model to simulation with Matlab/Simulink and AMEsim, and bench test is conducted on different driving cycles, which proves that the dynamic response of active power source is fit and controllable by adjusting PID parameters.

Distributed drive electric vehicle (EV) is driven by four independent hub motors mounted directly in wheels and retains traditional hydraulic brake system. So it can quickly produce driving/braking motor torque and large stable hydraulic braking force. In this paper a new control allocation strategy for distributed drive electric vehicle is proposed to improve vehicle's lateral stability performance. It exploits the quick response of motor torque and controllable hydraulic pressure of the hydraulic brake system. The allocation strategy consists of two sections. The first section uses an optimal allocation controller to calculate the total longitudinal force of each wheel. In the controller, a dynamic efficiency matrix is designed via local linearization to improve lateral stability control performance, as it considers the influence of tire coupling characteristics over yaw moment control in extreme situations.

This paper presents several pressure estimation algorithms (PEAs) for a decoupled electro-hydraulic brake system (EHB), which is driven by an electric motor + reduction gear. Most of the pressure control solutions are based on standard pressure-based feedback control, requiring a pressure signal. Although the pressure sensor can produce the pressure feedback signal, it will increase cost and enlarge installation space. The rotation angle of electric motor is available by the built-in sensor, so the pressure can be estimated by using the rotation angle. Considering the typical nonlinearities (i.e. friction, pressure-position relationship) and uncertainties (i.e. disturbance caused by friction model), the estimation-oriented model is established. The LuGre model is selected to describe the friction, and the pressure-position relationship is fitted by a quadratic polynomial.

Since data processing methods could not completely eliminate the uncertainty of signals, it is a key issue for stable and robust decision-making for uncertainty tolerance of intelligent vehicles. In this paper, a decision-making for an Autonomous Emergency Braking (AEB) case considering the uncertainty of road adhesion coefficient estimation (RACE) is proposed. Firstly, the 3σ criterion is employed to classify the confidence in order to establish the decision-making mechanism considering the signal uncertainty of RACE. Secondly, the model for AEB with the uncertainty of the road adhesion coefficient estimated is designed based on the Seungwuk Moon model. Thirdly, a CCRs and CCRm scenario was designed to verify the feasibility in reference to the European New Car Assessment Programme (Euro NCAP) standard. Finally, the results of 10,000 cycles test illustrate that the proposed method is stable and could significantly improve the safety confidence both in the CCRs and CCRm scenarios.

As one of the essential parts of automatic parking system (APS), the parking motion control module directly affects the system performance and driver experience. Therefore, it is necessary to design a simple, robust and efficient trajectory tracking algorithm which adapt to the various parking conditions. Firstly, considering the predictability and the ability of dealing with various system constraints, the model predictive control (MPC) lateral controller is designed. Then, the second lateral controller based on linear quadratic regulator (LQR) algorithm is designed, which has the excellent capability of balancing the multiple performances of the system. Finally, Stanley lateral controller is designed as the benchmark for horizontal comparison. Parallel and vertical parking simulation environments are proposed to verify the effectiveness of the designed lateral controllers, and the advantages and shortcomings of each control algorithm are horizontally analyzed and summarized.

Recent researches in autonomous driving mainly consider the uncertainty in perception and prediction modules for safety enhancement. However, obstacles which block the field-of-view (FOV) of sensors could generate blind areas and leaves environmental uncertainty a remaining challenge for autonomous vehicles. Current solutions mainly rely on passive obstacles avoidance in path planning instead of active perception to deal with unexplored high-risky areas. In view of the problem, this paper introduces the concept of information entropy, which quantifies uncertain information in the blind area, into the motion planning module of autonomous vehicles. Based on model predictive control (MPC) scheme, the proposed algorithm can plan collision-free trajectories while actively explore unknown areas to minimize environmental uncertainty. Simulation results under various challenging scenarios demonstrate the improvement in safety and comfort with the proposed perception-aware planning scheme.

With the development of brake-by-wire technology, electro-hydraulic composite braking technology came into being. This technology distributes the total braking force demand into motor regenerative braking force and hydraulic braking force, and can achieve a high energy recovery rate. The existing composite braking control belongs to single-channel control, i.e., the four wheel braking pressures are always the same, so the hydraulic braking force distribution relationship of the front and rear wheels does not change. For single-axle-driven electric vehicles, the additional regenerative braking force on the driven wheels will destroy the original braking force distribution relationship, resulting in reduced braking efficiency of the driven wheels, which are much easier to lock under poor road adhesion conditions.

For intelligent vehicles, a fast and accurate estimation of road slope is of great significance for many aspects, including the steering comfort, fuel economy, vehicle stability control, driving decision-making, etc. But the commonly used estimation methods nowadays usually demand additional sensors or complex dynamic models, causing increase in system complexity as well as decrease in accuracy. To solve these problems, this paper puts forward a real-time road slope estimation algorithm leveraging the relationship between pitch angle and road slope, which only requires low sensors cost and computational complexity. Firstly, a GNSS/INS fusion system is established to obtain the pitch angle with respect to the navigation frame, which couples the vehicle’s pitch angle in vehicle frame and road slope angle.