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Vehicle yaw stability control (YSC) can actively adjust the working state of the chassis actuator to generate a certain additional yaw moment for the vehicle, which effectively helps the vehicle maintain good driving quality under strong transient conditions such as high-speed turning and continuous lane change. However, the traditional YSC pursues too much driving stability after activation, ignoring the difference of multi-objective requirements of yaw maneuverability, actuator energy consumption and other requirements in different vehicle stability states, resulting in the decline of vehicle driving quality. Therefore, a vehicle yaw stability model predictive control strategy for dynamic and multi-objective requirements is proposed in this paper. Firstly, the unstable characteristics of vehicle motion are analyzed, and the nonlinear two-degree-of-freedom vehicle dynamics models are established respectively.

Nowadays, the vehicle market puts forward urgent requirement for new kinds of braking booster because the traditional vacuum booster cannot meet the demands of new energy vehicles anymore. However, one problem that all the new plans should face is how to guarantee an ideal pedal feeling. In this paper, a novel mechatronics braking booster is proposed, and servo motor introduced into the booster makes the assist rate can be adjusted under a great degrees of freedom, so the structural parameters and control parameters of the booster should be determined elaborately to get an optimal pedal feeling. The pedal feeling is always represented by the pedal stoke-force curve which is influenced by different parameters.

Based on the research and analysis of the current brake systems, this paper presents a novel electro-hydraulic brake system, which can better meet the functional requirements. The system mainly contains a master cylinder, two brake hydraulic cylinders and drive motors, two transmission mechanisms, thirteen solenoid valves, pedal force simulator, etc. Since the proposed brake system uses a dual motor along with two brake hydraulic cylinders, it has advantages in providing fast pressure response, flexible working modes, high precision and strong fault tolerance. In order to facilitate the study of pressure control algorithm for the proposed brake system, a mathematical model of the brake system is firstly established, then a multiplexed time-division pressure control algorithm is proposed to realize the simultaneous or partially simultaneous pressure control, which ensures the high precision and short response time.

The Electromechanical Brake Booster system (EMBB) integrates active braking and energy recovery and becomes a novel brake-by-wire solution that substitutes the vacuum booster. While the intelligent unmanned vehicle is in unstable state, the EMBB can improve the vehicle yaw stability more quickly and safely. In this paper, a new type of integrated EMBB has been designed, which mainly includes two parts: servo motor unit and hydraulic control unit. Aiming at the dynamic instability problem of intelligent unmanned vehicle, a three-layer vehicle yaw stability control structure including decision layer, distribution layer and execution layer is proposed based on integrated EMBB. Firstly, the decision layer calculates the ideal yaw rate and the side slip angle of the vehicle with the classic 2DOF vehicle dynamics model. The boundary of the stable region is determined by the phase plane method and the additional yaw moment is determined by the feedback PI control algorithm.

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.

The Electro-Mechanical Brake Booster system (EMBB) is a kind of novel braking booster system, which integrates active braking, regenerative braking, and other functions. It usually composes of a servo motor and the transmission mechanism. EMBB can greatly meet the development needs of vehicle intelligentization and electrification. During active braking, EMBB is required to respond quickly to the braking request and track the target pressure accurately. However, due to the highly nonlinearity of the hydraulic system and EMBB, traditional control algorithms especially for PID algorithm do not work well for pressure control. And a large amount of calibration work is required when applying PID algorithms to pressure control in engineering.

The electro-mechanical brake booster (EMBB) and hydraulic control unit (HCU) constitute the electro-mechanical brake system, which can meet the requirements of brake system for intelligent vehicles. It does not need vacuum source, provides active braking function, have high control accuracy and fast response. But it has two electronic control units (ECU), which need coordinated control. When ABS is triggered, the pressure of the master cylinder keeps rising and falling, and the pressure fluctuates greatly. This will lead to noise and reduce the durability of the system. In this paper, a pressure optimization control strategy under ABS condition is proposed. Firstly, the structure and control strategy of EMBB are introduced. Secondly, the braking characteristics without pressure optimization control are analyzed. Thirdly, based on the demand of maximum cylinder pressure, a three-closed-loop pressure optimization control strategy is established.

This paper presents a power assisted braking control based on a novel mechatronic booster system. A brake pedal feel control unit is first discussed which includes a pedal emulator with an angular sensor to detect driver’s pedal travel, a signal processing module with a Kalman filter for sensor signal conditioning, and a driver braking intention detection and behavior recognition module based on the displacement and velocity of the pedal travel. A power assisted braking control is then presented as the core of the system which consists of controls on basic power assist, velocity compensation and friction compensation. The friction is estimated based on a generic algorithm offline. A motor controller is designed to provide the desired torque for the power assist. Finally, a novel mechatronic booster system is designed and built with an experimental platform set up with a widely adopted rapid prototype system using dSPACE products, such as MicroAutoBox, RapidPro, etc.

Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized and green adaptive cruise control for intelligent electric vehicle, which is also known to be MyEco-ACC. MyEco-ACC is based on the optimization of regenerative braking and typical driving styles. Firstly, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed.

In order to improve drivers’ acceptance to advanced driver assistance systems (ADAS) with better adaptation, drivers’ driving behavior should play key role in the design of control strategy. Adaptive cruise control systems (ACC) have many factors that can be influenced by different driving behavior. It is important to recognize drivers’ driving behavior and take human-like parameters to the adaptive cruise control systems to assist different drivers effectively via their driving characteristics. The paper proposed a method to recognize drivers’ behavior and intention based on Gaussian Mixture Model. By means of a fuzzy PID control method, a personalized ACC control strategy was designed for different kinds of drivers to improve the adaptabilities of the systems. Several typical testing scenarios of longitudinal case were created with a host vehicle and a traffic vehicle.

Electric vehicle (EV) has been regarded as not only an effective solution for environmental issues but also a more controllable and responsible device to driving forces with electric motors and precise torque measurement. For electric vehicle equipped with four in-wheel motors, its tire longitudinal forces can be generated independently and individually with fully utilized tire adhesion at each corner. This type of the electric vehicles has a distributed drive system, and often regarded as an over-actuated system since the number of actuators in general exceeds the control variables. Control allocation (CA) is often considered as an effective means for the control of over-actuated systems. The in-vehicle network technology has been one of the major enablers for the distributed drive systems. The vehicle studied in this research has an electrohydraulic brake system (EHB) on front axle, while an electromechanical brake system (EMB) on rear axle.

With the development of modern vehicle chassis control systems, such as Anti-Lock Brake System (ABS), Acceleration Slip Regulation (ASR), Electronic Stability Control (ESC), and Regenerative Braking System (RBS) for EVs, etc., there comes a new requirement for the vehicle brake system that is the precise control of the wheel brake pressure. The Electro-Hydraulic Brake system (EHB), which owns an ability to adjust four wheels’ brake pressure independently, can be a good match with these systems. However, the traditional control logic of EHB is based on the PWM (Pulse-Width Modulation), which has a low control accuracy of linear electromagnetic valves. Therefore, this paper presents a research of the linear electro-magnetic valve characteristic analysis, and proposes a precise pressure control algorithm of the EHB system with a feed forward and a PID control of linear electro-magnetic valves.

Tire modeling plays an important role in the development of an Active Vehicle Safety System. As part of a larger project that aims at developing an integrated chassis control system, this study investigates the performance of a 19” all-season tire on ice for a sport utility vehicle. A design of experiment has been formulated to quantify the effect of operational parameters, specifically: wheel slip, normal load, and inflation pressure on the tire tractive performance. The experimental work was conducted on the Terramechanics Rig in the Advanced Vehicle Dynamics Laboratory at Virginia Tech. The paper investigates an approach for the parameterization of the Dugoff tire model based on the experimental data collected. Compared to other models, this model is attractive in terms of its simplicity, low number of parameters, and easy implementation for real-time applications.

Sloped medians provide a run-off area for errant vehicles so that they can be safely stopped off-road with or without barriers placed in the sloped median. However, in order to optimize the design of sloped medians and the containment barriers, it is essential to accurately model the behavior of vehicles on such sloped terrain surfaces. In this study, models of a vehicle fleet comprising a small sedan and a pickup truck and sloped terrain surface are developed in CarSim™ to simulate errant vehicle behavior on sloped median. Full-scale crash tests were conducted using the vehicle fleet driven across a 9.754 meters wide median with a 6:1 slope at speeds ranging from 30 to 70 km/h. Measured data such as the lateral accelerations of the vehicle as well as chassis rotations (roll and pitch) were synchronized with the vehicle motion obtained from the video data.

This paper presents a unified novel function-based brake control architecture, which is designed based on a top-down approach with functional abstraction and modularity. The proposed control architecture includes a commands interpreter module, including a driver commands interpreter to interpret driver intention, and a command integration to integrate the driver intention with senor-guided active driving command, state observers for estimation of vehicle sideslip, vehicle speed, tire lateral and longitudinal slips, tire-road friction coefficient, etc., a commands integrated control allocation module which aims to generate braking force and yaw moment commands and provide optimal distribution among four wheels without body instability and wheel lock or slip, a low-level control module includes four wheel pressure control modules, each of which regulates wheel pressure by fast and accurate tracking commanded wheel pressure.

Brake-by-wire (BBW) system has drawn a great attention in recent years as driven by rapidly increasing demands on both active brake controls for intelligent vehicles and regenerative braking controls for electric vehicles. However, unlike conversional brake systems, the reliability of the brake-by-wire systems remains to be challenging due to its lack of physical connection in case of system failure. There are various causes for the failure of a BBW system, such as failure of brake controller, loss of sensor signals, failure of communication or even power supply, to name a few. This paper presents a fault-tolerant control under novel control architecture. The proposed control architecture includes a driver command interpreter module, a command integration module, a control allocation module, a fault diagnosis module and state observers. The fault-tolerant control is designed based on a quadratic optimal control method with consideration of actuator constraints.

When automobile is at the threat of collisions, steering usually needs a shorter longitudinal distance than braking to avoid collision, especially at a high speed. In emergency steering evasion, the vehicle may be out of the road or colliding with obstacles ahead when the driver’s steering torque is excessive or insufficient. In view of the above problems, this paper presents an emergency steering evasion torque assistance system based on optimized trajectory. First, a feasible steering evasion area is established which treats the paths of excessive and insufficient steering as boundary conditions in this paper. An optimized trajectory is derived from the lateral acceleration of the vehicle and the time to the adjacent lane as optimization conditions. Second, a two degree of freedom vehicle model is used to represent dynamics of the vehicle.

Driving assistance system is regarded as an effective method to improve driving safety and comfort and is widely used in automobiles. However, due to the different driving styles of different drivers, their acceptance and comfort of driving assistance systems are also different, which greatly affects the driving experience. The key to solving the problem is to let the system understand the driving style and achieve humanization or personalization. This paper focuses on clustering and identification of different driving styles. In this paper, based on the driver's real vehicle experiment, a driving data acquisition platform was built, meanwhile driving conditions were set and drivers were recruited to collect driving information. In order to facilitate the identification of driving style, the correlation analysis of driving features is conducted and the principal component analysis method is used to reduce the dimension of driving features.

To meet the new requirements of braking system for modern electrified and intelligent vehicles, various novel electro-mechanical brake boosters (Eboosters) are emerging. This paper is aimed at a new type of the Ebooster, which is mainly consisted of a permanent magnet synchronous motor (PMSM), a two-stage reduction transmission and a servo mechanism. Among them, the PMSM is a vital actuator to realize the functions of the Ebooster. To get fast response of the Ebooster system, a novel control strategy employing a maximum torque per ampere (MTPA) control with current compensation decoupling and current-adjusting adaptive flux-weakening control is proposed, which requires the PMSM can operate in a large speed range and maintain a certain anti-load interference capability. Firstly, the wide speed control strategy for the Ebooster’s PMSM is designed in MATLAB/Simulink.

As one of advanced driver assistance systems (ADAS), automatic parking system has great market prospect and application value. In this paper, based on an intelligent vehicle platform, an automatic parking system is designed by using multi-point preview theory. The vehicle kinematics model was established, based on Ackermann steering principle. By analyzing working conditions of parallel parking, complex constraint condition of parking trajectory is established and reference trajectory based on sine wave is proposed. In addition, combined with multi-point preview theory, the design of trajectory following controller for automatic parking is completed. The cost function is designed, which consider the trajectory following effect and the degree of easy handling. The optimization of trajectory following control is completed by using the cost function.