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This paper proposed a novel fault-tolerant control method based on control allocation via dynamic constrained optimization for electric vehicles with XBW systems. The total vehicle control command is first derived based on interpretation on driver's intention as a set of desired vehicle body forces, which is further dynamically distributed to the control command of each actuator among vehicle four corners. A dynamic constrained optimization method is proposed with the cost function set to be a linear combination of multiple control objectives, such that the control allocation problem is transformed into a linear programming formulation. An analytical yet explicit solution is then derived, which not only provides a systematic approach in handling the actuation faults, but also is efficient and real-time feasible for in-vehicle implementation. The simulation results show that the proposed method is valid and effective in maintaining vehicle operation as expected even with faults.

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.

In order to solve such problems that the millimeter-wave radar is of large computation, poor robustness and low precision of the target tracking algorithm, this paper presents an algorithmic framework for millimeter-wave radar tracking of target-vehicles. The target measurement information outside the millimeter- wave radar detection range is eliminated by the data plausibility judgment method based on the millimeter-wave radar detection parameters. Target clustering is made using Manhattan distance, to eliminate clutter interference and cluster multiple target measurements into one. The data association is made by use of nearest neighbor to determine the correspondence between information received measured by the radar and the real target. The vehicle is the key detection target of the vehicle millimeter-wave radar during road driving.

With the increasing number of urban cars, parking has become the primary problem that people face in daily life. Therefore, many scholars have studied the automatic parking system. In the existing research, most of the path planning methods use the combined path of arc and straight line. In this method, the path curvature is not continuous, which indirectly leads to the low accuracy of path tracking. The parking path designed using the fifth-order polynomial is continuous, but its curvature is too large to meet the steering constraints in some cases. In this paper, a continuous-curvature parking path is proposed. The parking path tracker based on Model Predictive Control (MPC) algorithm is designed under the constraints of the control accuracy and vehicle steering. Firstly, in order to make the curvature of the parking path continuous, this paper superimposes the fifth-order polynomial with the sigmoid function, and the curve obtained has the continuous and relatively small curvature.

As a new brake-by-wire solution, the electro-booster (Ebooster) brake system can work with the electronic stability program (ESP) equipped in the real vehicle to realize various excellent functions such as basic force boosting (BFB), active braking and energy recovery, which is promoting the development of smart vehicles. Among them, the BFB is the function of Ebooster's servo force to assist the driver's brake pedal force establishing high-intensity braking pressure. After the BFB function failure of the Ebooster, it was not possible to provide sufficient brake pressure for the driver's normal braking, and eventually led to traffic accidents. In this paper, a compensation redundancy control strategy based on ESP is proposed for the BFB failure of the self-designed Ebooster.

Highway traffic safety has been the most serious problem in current society, statistics show that about 70% to 90% of accidents are caused by driver operational errors. The autonomous emergency braking (AEB) is one of important vehicle intelligent safety technologies to avoid or mitigate collision. The AEB system applies the vehicle brakes when a collision is eminent in spite of any reaction by the driver. In some technologies, the system forewarns the driver with an acoustic signal when a collision is still avoidable, but subsequently applies the brakes automatically if the driver fails to respond. This paper presents the development and implementation of a rear-end collision avoidance system based on hierarchical control framework which consists of threat assessment layer, wheel slip ratio control layer and integrated-electro-hydraulic brake (IEHB) actuator control layer.

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 brake-by-wire system (BBW) is better match the new energy vehicle in the future direction of development. The electro-mechanical brake (EMB) is lack of the brake failure backup and need a high 42 V voltage for the power supply. This paper presents a new brake-by-wire hybrid brake system (HBS) with the electro-hydraulic brake (EHB) equipped on the front wheels and the EMB equipped on the rear wheels. The combination of these two brake-by-wire systems has advantages of both the EHB and EMB system. The EMB on the rear wheels totally removing the rear pipes and can be simply mounted. In addition, since the need of brake torque on the rear axle is relatively small, the power supply of EMB can be reduced to 12 V. Meanwhile, the EHB on the front wheels has the failure backup function through the hydraulic line. The HBS can quickly and accurately regulate four wheels brake force of vehicles which can well meet the requirement of antilock brake system (ABS).

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.

Lane detection and tracking play a key role in autonomous driving, not only in the LKA System but help estimate the pose of the vehicle. While there has been significant development in recent years, traditional outdoor SLAM algorithms still struggle to provide reliable information in challenging dynamic environments such as lack of roadside landscape or surrounding vehicles at almost the same speed or on the road in the woods. On the structured road, lane markings as static semantic features may provide a stable landmark assist in robust localization. As most of the current lane detection work mainly on separated images ignoring the relationship between adjacent frames, we propose a pixel-level lane tracking method for autonomous vehicles. In this paper, we introduce a deep network to detect and track lane features. The network has two parallel branches. One branch detects the lane position, while the other extracts the point description on a pixel level.

Autonomous driving technology, as the product of the fifth stage of the information technology revolution, is of great significance for improving urban traffic and environmentally friendly sustainable development. Autonomous driving can be divided into three main modules. The input of the decision module is the perception information from the perception module and the output of the control strategy to the control module. The deep reinforcement learning method proposes an end-to-end decision-making system design scheme. This paper adopts the Deep Deterministic Policy Gradient Algorithm (DDPG) that incorporates the Priority Experience Playback (PER) method. The framework of the algorithm is based on the actor-critic network structure model. The model takes the continuously acquired perception information as input and the continuous control of the vehicle as output.

Subframe is an important part of automobile chassis, which is connected with body, suspension control arm, powertrain mount, etc. The dynamic stiffness value of the connection point is an important performance index of the subframe, which affects the vibration of the vehicle body. This paper introduces the basic concept and related theory of dynamic stiffness, derives the theoretical formula of dynamic stiffness, and analyzes the frequency response of the key points of the subframe. In view of the fact that the dynamic stiffness of the subframe of a certain vehicle model is not up to the standard at some connection points, the dynamic stiffness CAE simulation analysis is carried out to determine the frequency range of insufficient dynamic stiffness and the connection points that need to be optimized.

Most of the Advanced Driver Assistance System (ADAS) applications are aiming at improving both driving safety and comfort. Understanding human drivers' driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system performance, in particular, the acceptance and adaption of ADAS to human drivers. The research presented in this paper focuses on the classification and identification for personalized driving styles. To motivate and reflect the information of different driving styles at the most extent, two sets, which consist of six kinds of stimuli with stochastic disturbance for the leading vehicles are created on a real-time Driver-In-the-Loop Intelligent Simulation Platform (DILISP) with PanoSim-RT®, dSPACE® and DEWETRON® and field test with both RT3000 family and RT-Range respectively.

The limitation of drivers' attention and perception may bring collision dangers, Autonomous Emergency Braking (AEB) can help drivers to avoid the potential collisions through active braking. Since the positive effect of it, motor corporations have begun to equip their vehicles with the system, and regulatory agencies in various countries have introduced test standards. At this stage, the actuator of AEB usually adopts Electronic Stability Program (ESP), but it poor performance of continuous working period and active pressure built-up for all wheels limits its implements. Electromechanical brake booster can realize power assisted brake without relying on the vacuum source and a variety of specific power curves. Moreover it can achieve the active braking with a rapid response, which make it can fulfill requirements of automotive electric and intelligent development.

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.

The Electronic Stability Program (ESP), as a key actuator of traditional automobile braking system, plays an important role in the development of intelligent vehicles by accurately controlling the pressure of wheels. However, the ESP is a highly nonlinear controlled object due to the changing of the working temperature, humidity, and hydraulic load. In this paper, an accurate pressure control strategy of single wheel during active braking of ESP is proposed, which doesn’t rely on the specific parameters of the hydraulic system and ESP. First, the structure and working principle of ESP have been introduced. Then, we discuss the possibility of Pulse Width Modulation (PWM) control based on the mathematical model of the high-speed switching valve. Subsequently, the pressure building characteristics of the inlet and outlet valves are analyzed by the hardware in the Loop (HiL) experimental platform.

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.

This paper first presents an algorithm to detect tire blowout based on wheel speed sensor signals, which either reduces the cost for a TPMS or provides a backup in case it fails, and a tire blowout model considering different tire pressure is also built based on the UniTire model. The vehicle dynamic model uses commercial software CarSim. After detecting tire blowout, the active braking control, based on a 2DOF reference model, determines an optimal correcting yaw moment and the braking forces that slow down and stop the vehicle, based on a linear quadratic regulator. Then the braking force commands are further translated into target pressure command for each wheel cylinder to ensure the target braking forces are generated. Some simulations are conducted to verify the active control strategy.

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.

Effectively detecting road boundaries in real time is critical to the applications of autonomous vehicles, such as vehicle localization, path planning and environmental understanding. To precisely extract the road boundaries from the 3D-LiDAR data, a dedicated algorithm consisting of four steps is proposed in this paper. The steps are as follows: Firstly, the 3D-LiDAR data is pre-processed by employing the RANSAC method, the ground points are quickly separated from the original 3D-LiDAR point cloud to reduce the disturbance from the obstacles on the road, this greatly decreases the size of the point cloud to be processed. Secondly, based on the principle of 3D-LiDAR scanning, the ground points are divided into scan layers. And the road boundary points of each scan layer are detected by using three spatial features based on sliding window.