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In this paper, a Game-based haptic shared control framework is constructed under the condition of driver-automation conflict. The human driver and the automation system are regarded as equal agents to participate in the driving decision making process under this framework. Firstly, a vehicle model integrated with mechanical steering system is established to design automation and driver behavior. Then nash game theory is used to depict the driver-automation interaction relationship when goals are inconsistent. The model predictive control(MPC) method is used to derive the optimal control strategy when reaching nash equilibrium. Finally, joint simulation results show that the Game-based haptic shared control can effectively relieve the strong interference of the automation system compared with the No-game haptic shared control system.

This paper proposes a steady-state drift control strategy based on model predictive control(MPC). A three-degree-of-freedom vehicle model is established to calculate the equilibrium state of steady-state drift. The equation of deviation vehicle dynamics is used to track the equilibrium state. Carsim/Simulink joint simulation is carried out, and the results show that the controller is able to control the steady-state drift of the vehicle accurately. The influence of variation of road adhesion coefficient on vehicle steady-state drift is also analyzed.

This paper proposes a road risk assessment(RRA) algorithm based on lane and obstacle information, including obstacle selection based on lane line equations and risk assessment based on time to collision (TTC). The algorithm can integrate the functions of collision warning and lane change risk assessment, and can be implemented on actual vehicles. RRA can provide collision warning for vehicles in the lane and also give lane-changing suggestions, and can conduct risk assessment of the lane to be changed according to the needs of lane changing. In addition, the alarm logic is designed, that is, when multiple risks appear at the same time, it is determined which HMI signal to prompt the driver. This method has been tested on the AEOLUS D53 vehicle, and the test results show that the system can effectively evaluate the driving risk.

Tractor semi-trailers play an important role in the freight transportation on highways, ports, factories and large logistics centers. A path tracking strategy with excellent performance can not only ensure the tractor semi-trailer safety but also improve the traffic efficiency and profitability of the transportation business. Due to the coupling articulated structure between the tractor and semi-trailer, the semi-trailer trajectory can only be controlled by adjusting the tractor control action, which makes the path tracking of tractor semi-trailer path tracking more difficult than passenger vehicles. Aiming at the problem of semi-trailer path tracking, a novel simplified kinematics and dynamics model of the tractor semi-trailer is established, which can reflect the basic kinematic and dynamic characteristics of tractor semi-trailer. Based on this model, an regional tractor semi-trailer path tracking strategy based on model predictive control(MPC) is presented in this paper, in which only front wheel angle of the tractor is needed to be optimized. Finally, the effectiveness of the controller is verified by the joint simulation of TruckSim and Simulink.

Q-learning usually carries out global path planning in grid environment, which is difficult to satisfy the requirements of vehicle dynamics in practice. In this paper, a local path planning for intelligent vehicle based on Q-learning algorithm is proposed. Firstly, the vehicle-road model is established. The information of lane boundary and lane center line is obtained by interpolation method, and the position relationship between main vehicle and environment vehicle is determined. Secondly, the variables that can reflect the driving state of the vehicle and the relationship with the surrounding vehicle position are determined to describe the vehicle states. According to the actual driving situation and the mechanical structure of the vehicle, the action that the vehicle can take is determined. Then, the reward and punishment function is designed through the optimization objectives: driving in the lane, no collision with the surrounding vehicles and so on. Finally, the vehicle path discrete sequence is obtained after training, it is necessary to smooth the path to ensure that the vehicle can track the planned path well. In this paper, the cubic spline interpolation is used to process the discrete path sequence into smooth continuous path. In order to verify the effectiveness of the planning method, the path tracking simulation is carried out on MATLAB/CarSim software. The results show that the path solved by this method can satisfy the requirements of vehicle dynamics.

Path tracking is an important part of the research of intelligent vehicle driving systems. This paper proposes a path tracking method based on model predictive control (MPC). Firstly, the two-degree-of-freedom vehicle model is established, and the incremental model of the design controller is further obtained. Secondly, taking into account the road safety constraints and actuator constraints associated with the shape of the vehicle, a constrained MPC method is employed; The front wheel angle is obtained by rolling optimization, and vehicle path tracking control is realized. Finally, the effectiveness of the verification scheme is verified by the vehicle dynamics software veDYNA and compared with the non-incremental model. The simulation results show that the incremental model has better anti-interference.

For the problem of vehicle obstacle avoidance control, a steering obstacle avoidance method based on optimization idea is designed. In the research of obstacle avoidance path planning method, a design method with three-segment sinusoidal lateral acceleration is adopted, combining with various practical constraints of vehicle and safe distance, an optimization solution method of obstacle avoidance trajectory is presented, which takes obstacle avoidance time as the optimal objective. Based on the established two-degree-of-freedom vehicle dynamics model, a tracking controller with moving horizon idea is designed. Using the vehicle dynamics simulation software veDYNA, the scene of vehicle lane change and obstacle avoidance is constructed. The effectiveness and control performance of this method are illustrated by simulation.

Brake-by-wire (BBW) system plays an important role in the implementation of active safety technology, auxiliary driving technology and unmanned driving technology. It can not only achieve energy recovery, but also effectively reduce the braking distance and improve the safety of vehicles. In this paper, a new electric booster(E-booster) hydraulic brake system is presented. The booster pedal is completely decoupled and the hydraulic pressure can be controlled linearly. The research contents of this paper are as follows: First, E-booster hydraulic brake system was established in AMESim software with high precision. It mainly includes worm gear and worm transmission mechanism module, brake main cylinder module, brake wheel cylinder module. PMSM model is established in simulink. Second, dual closed -loop PID Controller is designed in simulink to control hydraulic pressure and PMSM adopts vector control. Finally, effectiveness of the controller is verified based on Matlab/Simulink and AMESim software combination and simulation.

The rapid development of active safety control systems has paved the way for the discussion of lateral stability in critical running conditions. To improve the lateral stability of a vehicle in critical running conditions, a nonlinear model predictive control (NMPC) strategy that integrates active front steering and additional yaw moment is proposed in this manuscript. The reference yaw rate in critical running conditions is obtained by employing Lyapunov's second method. In addition, this method adopts a varied sideslip angle to describe a stability region using a phase plane approach, which is used as the vehicle stability constraints. To verify the effectiveness of the presented lateral NMPC stability controller, off-line simulations under various running conditions are carried out using the high-precision vehicle simulation veDYNA software. It is shown that the N-MPC controller presents a feasible performance enhancement in tracking the reference yaw rate and keeping the vehicle stable in critical running conditions.

Vehicle platoon has become a research hotpot due to its potential for improving traffic utility and fuel efficiency, for which reliable tracking performance is critical. This paper proposes a nonlinear moving horizon control strategy that ensures that all vehicles in a truck platoon maintain the consensus speed and the desired spacing distance between the adjacent vehicles. The proposed control strategy guarantees the tracking performance of the following vehicles in platoon by predicting and tracking the state of the adjacent preceding vehicles. To verify the availability of the proposed control strategy, simulations with a platoon of two homogeneous trucks are carried out based on a joint Matlab-TruckSim simulation. The results specify that an acceptable tracking speed and the desired distance between adjacent vehicles can be obtained by the presented nonlinear moving horizon controller.

The driving model switched between an intelligent vehicle and a human driver is a hot discussing issue for intelligent driving system, and it relates to the safety of the intelligent vehicle and traffic efficiency of transportation system. It presents a hazard-evaluation-based driver-automation switched shared steering control approach for intelligent vehicles in this manuscript. The switched operation between human driver and autopilot system is carried out when the hazard situation is tested by the autopilot controller. The driver’s operation and the deviation from the road center line are employed to carry out the hazard evaluation. The autopilot controller is designed using the constrained model predictive control (MPC) approach to keep the intelligent vehicle run in the safe area that is between the road boundary. In order to verify the control performance of the proposed algorithm, simulation verification under hazard situation of the proposed approach are carried out and compared with the non-switching method. The results show that the intelligent vehicle can keep safe in the hazard situation.

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian mixture model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on artificial neural networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios. Accepted version

The accurate information of vehicle states that could not be obtained directly by onboard sensors is virtual important for vehicle active safety systems. This paper presents the nonlinear full-order observer which is used to estimate the longitudinal velocity, lateral velocity and road grade estimation with autonomous driving. Firstly, this paper established a simplified vehicle dynamics model for the Hongqi HQ430 which could characterize the performance of autonomous driving vehicle on highway, and the estimator was designed. Secondly, in order to verify the effectiveness of the proposed nonlinear full-order observer, we do some simulation experiments, simulation experiments were carried out under different running conditions, the simulation results showed that the estimation method have certain validity and accuracy.

A nonlinear reduced-order vehicle velocity observer based on vehicle dynamics is proposed. Firstly, the longitudinal force of the tire is obtained by cascade tire-force estimation method based on the sliding mode observer, and the results are verified. Then, based on the simplified three-degree-of-freedom vehicle model, a nonlinear reduced-order observer with yaw rate as the correction term is designed. A large number of experiments are used to select the parameters and gain of the sliding mode observer and reduced-order velocity observer. Finally, to verify the effectiveness of the observer, the simulation experiments were carried out under various conditions which using the high precision vehicle dynamics software veDYNA. The results show that the estimated vehicle velocity observer based on vehicle dynamics can achieve the expected estimation effect.

As a typical cyber-physical system (CPS), electrified vehicle becomes a hot research topic due to its high efficiency and low emissions. In order to develop advanced electric powertrains, accurate estimations of the unmeasurable hybrid states, including discrete backlash nonlinearity and continuous half-shaft torque, are of great importance. In this paper, a novel estimation algorithm for simultaneously identifying the backlash position and half-shaft torque of an electric powertrain is proposed using a hybrid system approach. System models, including the electric powertrain and vehicle dynamics models, are established considering the drivetrain backlash and flexibility, and also calibrated and validated using vehicle road testing data. Based on the developed system models, the powertrain behavior is represented using hybrid automata according to the piecewise affine property of the backlash dynamics. A hybrid-state observer, which is comprised of a discrete-state observer and a continuous-state observer, is designed for the simultaneous estimation of the backlash position and half-shaft torque. In order to guarantee the stability and reachability, the convergence property of the proposed observer is investigated. The proposed observer are validated under highly dynamical transitions of vehicle states. The validation results demonstrates the feasibility and effectiveness of the proposed hybrid-state observer.

It is an important and essential aspect for autonomous ground vehicles to follow the desired path. In this manuscript, a path tracking controller using model predictive control (MPC) method is proposed. In order to describe the vehicle motion and its dynamics, kinematics and dynamics integrated model is first presented. Then, the control strategy is proposed and MPC is employed to design the path tracking controller. In the following, the stability analysis is carried out and it shows that the system is proven to be asymptotically stable and theoretically it has no static error. In order to validate the effectiveness of the proposed MPC-based path tracking controller, veDYNA-Simulink joint simulations under different velocities and road friction coefficient are carried out and the results illustrate that the path tracking algorithm obtains good tracking performance.

It presents a constrained model predictive control (MPC) scheme to discuss the shared steering control with a driver. It considers the shared control from the following two aspects that are the safety of vehicle and the driving intention of human. For the safety, obstacle avoidance is very important and it is taken as constraints. Furthermore, the vehicle's shape is considered. About the driving intention of human, the vehicle follow the driver's steering command as an objective in the scheme. Finally, simulations are carried out to validate the effectiveness of the constrained MPC approach.

Fe-Ni film with compositions of 45wt.% Ni, which is also denoted as Fe-45Ni, was used as under bump metallization (UBM) to evaluate the interfacial reliability of SnAgCu/Fe-Ni solder joint under electromigration (EM) test. For comparison, commercial Cu UBM was also adopted in the reliability test. The microstructural evolutions of these two UBM solder joints were observed by scanning electron microscope (SEM). For SAC/Cu solder joints, the competition between the consumption of Cu cathode and the solder voiding led the ultimate failure, while the SAC/Fe-45Ni solder joints kept at a perfect condition without any signs of failure after 1000 hours EM test. The characteristic lifetime calculated by Weibull analysis for Fe-45Ni UBM was 2340 hours, which is three times longer than that of Cu UBM (698 hours), demonstrating an outstanding performance for Fe-45Ni UBM in electro-migration resistance. Accordingly, the superior EM resistance can be attributed to the reasons below. Firstly, Fe-45Ni UBM has an excellent barrier effect for Cu diffusion, due to the low diffusivity and high activation energy of interfacial intermetallic compounds (IMCs). Secondly, Fe flux originating from EM effect transports in the opposite direction to the diffusion flux under thermal effect at the cathode, thus it partly counteracted the diffusion flux and slow down the dissolution process of Fe-45Ni cathode.

Lateral stability of an automotive vehicle in a cornering situation is critical to vehicle stability and handling performance and it is effective on reducing single-vehicle accidents. In order to obtain the lateral stability, a nonlinear model predictive control (NMPC) method considering rear steering angle as control variable is presented. It adopts tire sideslip angle to express vehicle lateral stability, and deals with the actuator and security constraints, and nonlinear properties of tire-road force effectively. In order to verify the effectiveness of the presented NMPC method, various simulations are carried out in vehicle conventional and critical running conditions. The simulation results specify that the tire sideslip angle could effectively express the lateral vehicle stability and the proposed NMPC method could obtains better lateral stability.

The accurate information of tire-road forces and vehicle velocities that could not be obtained directly by onboard sensors is virtual important for vehicle active safety systems. In this manuscript, it focuses on modular scheme for vehicle tire-road forces and velocities estimation using sliding mode observer. The longitudinal tire-road force is estimated first. Then, the lateral tire-road force could be estimated considering the longitudinal tire-road force as input. In the following, the integrated of nonlinear and sliding mode observer is proposed for the longitudinal and lateral vehicle velocities estimation. In order to verify the effectiveness of the proposed modular estimation scheme, simulations of various running condition are carried out. Moreover, the estimation results are compared with the experimental data of Hongqi vehicle HQ430. The simulation and experimental results specify that the proposed modular estimation scheme could obtain accurate estimation results of tire-road forces and vehicle velocities.

In order to improve vehicle lateral stability and handling stability, a novel integrated vehicle yaw stability control scheme is proposed in this manuscript which considers front steering wheel angle and additional yaw moment as control variables. The reference yaw rate is firstly determined according to Lyapunov's second law which is determined by keeping sideslip angle within a stable range. In the following, considering mechanical security constrains, nonlinear model predictive control (NMPC) algorithm is utilized to make vehicle yaw rate follow the reference value. Finally, the performance of the proposed integrated controller is evaluated in critical vehicle running conditions, and the simulation results indicate that the proposed method is able to maintain vehicle lateral stability perfectly.

A blowout is a rapid loss of inflation pressure of a pneumatic tire. In order to reduce the fatal damage of tire blowout during vehicle operation, a differential flatness based 3 degrees of freedom (DOF) vehicle dynamics model with a deflated tire is established. The model predictive controller designed based on this method can guarantee that the vehicle after a tire blowout can drive safely until it comes to a complete stop. Due to the heavily online computational burden, it is actually difficult to implement nonlinear model predictive control (NMPC) scheme in the quick-response control system. In this paper, a novel control strategy is proposed to solve this problem and applied to the control of vehicles after a tire blowout. The involved optimization problem of NMPC is solved off-line in the operating points which then are recorded into a control map. Simulation results are provided to verify the effectiveness of the proposed control strategy.

Wheel skidding or spinning is dangerous for driving vehicles, and compared to conventional vehicles, electric vehicles are more easily to slip during driving on slippery road. In this paper, a novel traction control method based on wheel-slip control is proposed to improve the safety and stability of electric vehicles. Considering the high nonlinearity and uncertainties of the traction system, this paper takes use of the feedback linearization methodology to design a nonlinear controller to achieve the tracking control for wheel slip. Meanwhile, to enhance the robustness of the controller, a integral action is introduced. As the controlled variable, the desired value of the wheel slip is required in the traction control system, this paper adopts the so-called Burckhardt's model to make online calculation for it. In order to demonstrate the effectiveness of the proposed method, simulations were conducted in different road conditions, and all the simulation results showed that the wheel-slip controller could produce a fairly good control performance.

Considering the nonlinear characteristics, model uncertainties and the time-varying road conditions of ground vehicles, a novel traction controller for ground vehicles is presented using nonlinear robust modified sliding mode control method to solve the problem of skid braking and spin acceleration. The current wheel slip could be controlled at the optimal value using this method, so that it could improve the safety and stability of ground vehicles in slippery road conditions. Due to the optimal value of the wheel slip depends on road conditions, the so-called Burckhardt's tire model is adopted to make online calculation. In order to validate the effectiveness of the proposed method, simulations in different road conditions are carried out, and a series of simulation results indicate that the wheel slip tracking controller could obtained fairly good performance.

For further enhancing vehicle handling stability and improving the DYC's deficiency of causing undesired longitudinal decelerations, an integrated control of the active front steering and direct yaw moment is proposed. A formulation of the bicycle model which considers the nonlinear tire characteristics is adopted. Considering several security constraints, the model predictive control method is used to design the integrated controller. Finally, some simulations are implemented to verify the effectiveness of the controller. The simulation results show that the proposed integrated control is able to reduce the vehicle longitudinal velocity fluctuations and enhance the yaw stability.

Obstacle avoidance is a critical problem to solve for Unmanned Ground Vehicles (UGVs). The schemes of obstacle avoidance for UGVs are first discussed according to the structured environment road. The braking algorithm is discussed using moving horizon optimization based on differential flatness, where 3 DOF vehicle model is used to follow the planning trajectory for the obstacle avoidance of UGVs. In addition, the cruise control and changing lane control algorithm are given using 2 DOF vehicle model. The optimal problem of avoiding obstacles is formulated in terms of minimizing the bias of the lateral displacement considering the lateral acceleration as constraints. In order to verify the effectiveness of the proposed method, simulation under multi-vehicle environment is carried out.

Simulation-based vehicle system design and development of various active chassis control systems necessitate an enhanced understanding of driver-vehicle systems, in particular improved modeling of driver driving control characteristics. A number of research efforts have been made in developing driver models in the past few decades. However, little effort has been attempted in modeling driver steering control behavior capturing vehicle-road system parameter uncertainties. In this paper, a novel driver steering control model based on stochastic model predictive control (SMPC) is proposed to effectively incorporate the variations in the vehicle-road system parameters. The proposed SMPC-based driver steering control framework consists of three modules, namely perception, decision and execution, where a multi-point driver preview approach is employed. An internal vehicle dynamics model with the parameter uncertainty in road friction coefficient is formulated to represent the driver's knowledge and adaptation about the variations in road conditions. The SMPC method is then used to minimize a cost function that is a weighted combination of lateral path error and ease of driver control. Simulation analysis about the variant parameters and comparison with an MPC-based driver model demonstrate the effectiveness and robustness of the proposed SMPC-based driver steering control model.

Wafer level chip scale packaging (WLCSP) is one of the most promising single chip packaging technologies due to advantages of fewer processing steps, lower cost, and enhanced device performance. Moreover, the redistribution layer (RDL) technology is an effective way to improve I/O density of IC packages. However, the reliability of WLP devices with RDL structure is greatly challenged as the pitch size going down. The RDL structure may affect the thermomechanical performance of the solder joints or silicon chips, thus causing unexpected failure. In this paper, RDL structured WLCSPs with pitch size of 500 μm were fabricated. The components with a size of 6×6 mm2 were flip-chip bonded on FR-4 boards. Board level reliabilities of the WLCSP were evaluated by thermal shock test according to JEDEC standard. Furthermore, failure analysis was carried out to find out the failure mechanism and the failure related to the RDL structure was observed. The effect of RDL structure on the reliability of WLP was analyzed with the finite element simulations.

Chip scale packaging (CSP) samples with Fe-Ni UBM barrier layer were successfully fabricated by actual process. Solid interfacial reactions between SAC105 and three kinds of Fe-Ni UBM barrier layers of different composition had been investigated to clarify the phase evolution of Fe-Ni/SAC solder joints. Microstructural investigation revealed that after reflow only a thin FeSn2 layer was formed on Fe-30Ni and Fe-50Ni substrate, while granular-like CuNiSn IMC was observed on FeSn2 layer for Fe-75Ni solder joint. During solid-state aging, Fe-75Ni films exhibited an obvious IMC growth, while Fe-50Ni and Fe-30Ni films could endure longer time. the outer CuNiSn IMC will transform into (Cu, Ni)6Sn5 compound during thermal aging, which could be accelerated by the prolonging of aging time, the increasing of aging temperature, as well as the decreasing of Fe content in the film.

With the development of radio frequency wireless communication technology, there are strong demands of spiral inductor with high performance and low profile. The inductor is a basic component of IPD (Integrated Passive Devices [1]) including oscillators, filters, mixers and baluns. In this paper, the structure and process of fabricating a high performance wafer level silicon-base spiral inductor was presented and the effects of geometrical parameters on the inductance and quality factor were studied via a commercial 3-D EM [2] (Electromagnetic) simulator software. The performance was compared with the traditional on-chip embedded inductors. The results show that the number of turns and inner radius significantly impact the inductance and the performance of the mentioned spiral inductor is better than the traditional on-chip embedded inductors.

Micro copper pillar bumps (μCPBs) have been an important electrical interconnect method for fine pitch I/O applications such as 2.5D IC integration. The thermal stress induced by the coefficient of thermal expansion (CTE) mismatch between a Cu/low-k silicon die, micro copper pillar bump and through silicon via (TSV) based silicon interposer is a significant reliability issue for 2.5D IC integration. During the whole IC packaging process, the Cu/low-k silicon interconnected with silicon interposer through μCPBs will undergo three times of lead-free reflow process: a. the first level assembly between Cu/low-k silicon die and silicon interposer wafer, b. the second level component assembly between the silicon interposer and organic substrate, c. the third level assembly between the component and printed circuit board (PCB). In order to investigate the stress distribution in the fragile low-k layer of silicon die, parametric finite element analysis (FEA) was carried out respectively for each of the three reflow processes. In finite element models of this paper, the μCPBs were used to realize the first level interconnection between a 5.1mm×5.1mm Cu/low-k die with a 50μm bump pitch of total 10404 I/O and a silicon interposer, and underfilling was carried out after both the first reflow and the second reflow process. The influence of different parameters/factors on the stress distribution is simulated. Results show that relatively thinner die thickness, larger bump diameter, larger PI opening, thinner PI thickness and smaller Al pad diameter may contribute to the low stress in the low-k layer during the reflow processes.

In order to reduce the fatal damage of tire blowout during vehicle operation, a differential flatness based model predictive controller is proposed. A 3 degrees of freedom (DOF) vehicle dynamics model with tire blowout is established, and then the flat outputs are given. The flatness property of the vehicle dynamics is discussed accordingly, and the MPC for the vehicle with a tire blowout stability control is formulated, where the lateral acceleration is considered as safety constraint. Simulations of vehicle with a tire blowout driving on the different road are carried out to verify the effectiveness of the proposed method.

Longitudinal and lateral vehicle velocities are the important information for active vehicle stability control. But for both technical and economical reasons, these vehicle states can not be measured directly in a standard car. In order to provide high precision vehicle velocities information for active stability control system, a mixed EKF is presented to estimate the longitudinal and lateral vehicle velocities, which is suitable for continuous state variables with discrete observation. To offer the necessary tyre-road forces information for estimating vehicle velocities, a random walk model is proposed to model the tyre-road forces. It could avoid the complex tyre-road force model effectively. In order to evaluate the performance of the proposed mixed EKF, the simulation are carried out in the ISO double lane change condition and slalom condition with the parameters of Hongqi vehicle CA7180A3E. Meanwhile, estimation results are compared with the vehicle dynamics software veDYNA. Simulation results show that the introduced mixed EKF method has perfectly high precision and could satisfy the requirement of active vehicle stability control system.