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Wheel Aerodynamics is an important part of vehicle aerodynamics. The wheels can notably influence the total aerodynamic drag, lift and ventilation drag of vehicles. In order to simulate the real on-road condition of driving cars, the moving ground and wheel rotation is of major importance in CFD. However, the wheel rotation condition is difficult to be represented exactly, so this is still a critical topic which needs to be worked on. In this paper, a study, which focuses on two types of cars: a fastback sedan and a notchback DrivAer, is conducted. Comparing three different wheel rotating simulation methods: steady Moving wall, MRF and unsteady Sliding Mesh, the effects of different methods for the numerical simulation of vehicle aerodynamics are revealed. Discrepancies of aerodynamic forces between the methods are discussed as well as the flow field, and the simulation results are also compared with published experimental data for validation.

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.

Multiple actuators equipped in electric vehicles, such as four- wheel steering (4WS) and four-wheel drive (4WD), provide more degrees of freedom for chassis motion control. However, developing independent control strategies for distinct actuator types could result in control conflicts, potentially degrading the vehicle's motion performance. To address this issue, a model predictive control (MPC) based steering-drive cooperated control strategy for enhanced agility and stability of electric vehicles with 4WD and 4WS is proposed in this paper. By designing the control constraints within the MPC framework, the strategy enables single-drive control, single-steering control, and steering-drive cooperative control. In the upper control layer, a linear time-varying MPC (LTV-MPC) is designed to generate optimal additional yaw moment and additional steering angles of front and rear wheels to enhance vehicle agility and lateral stability.

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.

Recently, several datasets have become available for occupant monitoring algorithm development, including real and synthetic datasets. However, real data acquisition is expensive and labeling is complex, while virtual data may not accurately reflect actual human physiology. To address these issues and obtain high-fidelity data for training intelligent driving monitoring systems, we have constructed a hybrid dataset that combines real driving image data with corresponding virtual data generated from 3D driving scenarios. We have also taken into account individual anthropometric measures and driving postures. Our approach not only greatly enriches the dataset by using virtual data to augment the sample size, but it also saves the need for extensive annotation efforts. Besides, we can enhance the authenticity of the virtual data by applying ergonomics techniques based on RAMSIS, which is crucial in dataset construction.

As one of the key technologies in autonomous driving, the lateral planning module guides the lateral movement during the driving process. An integrated lateral planning module should consider the non-holonomic constraints of a vehicle, the optimization of the generated trajectory and the applicability to various scenarios. However, the current lateral planning methods can only meet parts of these requirements. In order to satisfy all the performance requirements above, a novel Potential Field (PF) based lateral planning method is proposed in this paper. Firstly, a PF model is built to describe the potential risk of the traffic entities, including the obstacles, road boundaries and lines. The potential fields of these traffic entities are determined by their properties and the traffic regulations. Secondly, the planning algorithm is presented, which comprises three modules: state prediction, state search and trajectory generation.

During light to moderate braking at high speed, the local high temperature phenomenon can be observed on the brake disc surfaces, known as hot spots. The occurrence of hot spots will lead to negative effects such as brake performance fade, thermal judder and local wear, which seriously affect the performance of vehicle NVH. In this paper, based on the bench test of a ventilated disc brake, the basic characteristics of hot spots is obtained and the evolution process of temperature field and disc deformation is analyzed in detail. In temperature field, hot bands appear first and grow, migrate from inner and outer radius to the middle, with the growing temperature fluctuation and finally hot spots appear in the middle radius of the brake disc. The stable SRO waviness forms much earlier than the temperature fluctuation. In the stop brake studied in this paper, the SRO waviness stabilizes in main 7 order state which is lower than the final hot spot order.

Gesture control has been increasingly applied to automotive industry to reduce the distraction caused by in-vehicle interactions to the primary task of driving. The aim of this study is to find out if gestures can reasonably be used to control in-car devices. Since there exists a big cultural difference of gesture between different countries because of its particularity, a set of gestures which support intuitive human-machine interaction in an automotive environment is searched. The results show a gesture dictionary for a variety of on-board functions, which conforms to Chinese drivers’ driving habits. Furthermore, this paper also describes a driving simulator test to evaluate the usability of gesture from different aspects including the effectiveness, efficiency, satisfaction, memorability and security. Static driving simulator is considered as an excellent environment for the in-car secondary task as its high safety level, repeatability and reliability.

Tire cavity noise of pure electric vehicles is particularly prominent due to the absence of engine noise, which are usually eliminated by adding Helmholtz resonators with arbitrary transversal section to the wheel rims. This paper provides theoretical basis for accurately predicting and effectively improving acoustic performance of wheel resonators. A hybrid finite element method is developed to extract the transversal wavenumbers and eigenvectors, and the mode-matching scheme is employed to determine the transmission loss of the Helmholtz resonator. Based on the accuracy validation of this method, the matching design of the wheel resonators and the optimization method of tire cavity noise are studied. The identification method of the tire cavity resonance frequency is developed through the acoustic modal test. A scientific transmission loss target curve and fitness function are defined according to the noise characteristics.

The backlash between engaging components in a driveline is unavoidable, especially when the gear runs freely and collides with the backlash, the impact torque generated increases the vibration amplitude. The power-split hybrid electric vehicle generates output torque only from the traction motor during the launching process. The nonlinear backlash can greatly influence the driveability of the driveline due to the rapid response of the traction motor and the lack of the traditional clutches and torsional shock absorbers in the powertrain. This paper focuses on the launch vibration of the power-split hybrid electric vehicle, establishes a nonlinear driveline model considering gear backlash, including an engine, two motors, a Ravigneaux planetary gear set, a reducer, a differential, a backlash assembly, half shafts, and wheels.

On the study of reducing the disturbance on driver’s attention induced by low frequency vehicle interior stationary noise, a subjective evaluation is firstly carried out by means of rank rating method which introduces Distraction Level (DL) as evaluation index. A visual-finger response test is developed to help evaluating members better recognize the Distraction Level during the evaluation. A non-linear back propagation artificial neural network (BPANN) is then modeled for the prediction of subjective Distraction Level, in which linear sound pressure RMS amplitudes of five Critical Band Rates (CBRs) from 20 to 500Hz are selected as inputs of the model. These inputs comprise an input vector of BPANN. Furthermore, active noise equalization (ANE) on DL is realized based on Filtered-x Least Mean Square (FxLMS) algorithm that controls the gain coefficients of inputs of trained BPANN.

Lane Keeping Assistance System (LKAS) is a typical lateral driver assistance system with low acceptance. One of the main reasons is that fixed parameters cannot satisfy individual differences. So LKAS adaptive to driver characteristics needs to be designed. Driver Steering Override (DSO) process is an important process of LKAS. It happens when contradiction between driver’s intention and system behavior occurs. As feeling of overriding will affect the overall experience of using LKAS, the design of DSO characteristics is worthy of attention. This research provided an adaptive design scheme aiming at DSO characteristics for LKAS by building Driver Preference Model (DPM) based on simulator test data from preliminary experiments. The DPM was to represent the relationship between driver characteristics indices and driver preferred system characteristics indices. So that new drivers’ preference can be predicted by DPM based on their own daily driving data with LKAS switched off.

The adhesion control is the basic technology of active safety for the four-wheel driven EV. In this paper, a novel adhesion control method based on fuzzy logic control is proposed. The control system can maximize the adhesion force without road condition information and vehicle speed signal. Also, the regulation torque to prevent wheel slip is smooth and the vehicle driving comfort is greatly improved. For implementation, only the rotating speed of the driving wheel and the motor driving torque signals are needed, while the derived information of the wheel acceleration and the skid status are used. The simulation and road test results have shown that the adhesion control method is effective for preventing slip and lock on the slippery road condition.

Electric Power Steering (EPS) is the actuator of several lateral-dynamic-related Advanced Driver Assistance Systems (ADAS). A driving simulator with EPS will be much helpful for the ADAS development. However, if a real EPS is used in the driving simulator, it is quite difficult to realize the road reaction force accurately and responsively. To overcome this weakness, a virtual EPS platform is established. The virtual EPS platform contains two parts: one is the vehicle and EPS model, the other is the force feedback actuator (FFA) of the Steer-by-Wire (SBW) system. The FFA is an interface between the driver and the EPS/vehicle model. The reactive torque of the FFA is obtained based on the models. Meanwhile, the input of the EPS model is the steering angle of the FFA. Comparing to a real EPS, the virtual EPS platform has a problem of instability because of the actuator lag of the FFA. Therefore, a damping control method is applied to make the system stable.

Based on the four-wheel-drive electric vehicle (4WD EV), a variable structure control (VSC) strategy is designed in this paper for the anti-lock braking control. With nonpeak friction coefficient as target, sign judgment method of switch function in this VSC strategy is improved and a new control algorithm is proposed. The improved VSC strategy is made robust to the parameters of the algorithm and verified by the computer simulation as well as the hard-in-loop test. The results show that the slip rate can be controlled to a point in the stable area near the optimal slip ratio and the control strategy can effectively realize the anti-lock braking control.

An integrated-electro-hydraulic brake system (I-EHB) is presented to fulfill the requirements of active safety. Because I-EHB can control the brake pressure accurately and fast. Furthermore I-EHB is a decoupled system, so it could make the maximum regenerative braking while offers the same brake pedal feeling and also good for ADAS and unmanned driving application. Based on the analysis of current electrohydraulic brake systems, regulation requirements and the requirements for brake system, the operating mode requirements of I-EHB are formed. Furthermore, system topological structure and a conceptual design are proposed. After the selection of key components, the parameter design is accomplished by modeling the system. According to the above-mentioned design method, an I-EHB prototype and test rig is made. Through the test rig, characteristics of the system are tested. Results show that this I-EHB system responded rapidly.

Harmony-with-traffic refers to the ability of autonomous vehicles to maximize the driving benefits such as comfort, efficiency, and energy consumption of themselves and the surrounding traffic during interactive driving under traffic rules. In the test of harmony-with-traffic, one or more background vehicles that can respond to the driving behavior of the vehicle under test are required. For this purpose, the functional requirements of car-following model for harmony-with-traffic evaluation are analyzed from the dimensions of test conditions, constraints, steady state and dynamic response. Based on them, an interactive car-following model (ICFM) is developed. In this model, the concept of equivalent distance is proposed to transfer lateral influence to longitudinal. The calculation methods of expected speed are designed according to the different car-following modes divided by interaction object, reaction distance and equivalent distance.

Real-time interaction between a driver and the simulator is problematic. In this study, the racing car driving simulator has been established, which is composed of the following functional components: Motion Controller, Simview, Scenario Editor, Application Programmer Interface (APIs) and Crash Simulation. With TCP/IP protocol, the Motion Controller receives driver's manipulation, road unevenness and crash situation of Simview, then generates motion streams that reflecting the current conditions, and sends them to Simview and to the hydraulic platform. Furthermore, by detecting and analyzing general vehicle two-dimensional impact, a kind of complete and applicable calculation method has been established, and complicated vehicle impacts can be analyzed accurately. This racecar driving simulator places a racing driver in a interactive environment, and provides the driver with high-fidelity motion, visual, auditory, and force feedback cues.

Automatic Lane Change (ALC) function is an important step to promote the currently popular Advanced Driver Assistance Systems (ADAS) within a single lane. The key issue for ALC is accurate steering angle and trajectory tracking during the lane changing process. In this paper, an MPC controller with a receding horizon is designed to track the desired trajectory. During the tracking process, other objectives such as safety and smoothness are considered. Considering of the practical mechanism and parameter uncertainties, an SMC controller is designed to track the target steering angle. For validation, a Hardware-in-the-Loop (HIL) experiment platform is established, and experiments of different control algorithms under different conditions are carried out successively. Comparisons of the experiment results of MPC+SMC and PID+SMC schemes indicate that both the trajectory error and the steering angle error of the former combination are smaller.

Tire forces and moments play an important role in vehicle dynamics and safety. X-by-wire chassis components including active suspension, electronic powered steering, by-wire braking, etc can take the tire forces as inputs to improve vehicle’s dynamic performance. In order to measure the accurate dynamic wheel load, most of the researches focused on the kinematic parameters such as body longitudinal and lateral acceleration, load transfer and etc. In this paper, the authors focus on the suspension system, avoiding the dependence on accurate mass and aerodynamics model of the whole vehicle. The geometry of the suspension is equated by the spatial parallel mechanism model (RSSR model), which improves the calculation speed while ensuring the accuracy. A suspension force observer is created, which contains parameters including spring damper compression length, push rod force, knuckle accelerations, etc., combing the kinematic and dynamic characteristic of the vehicle.