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Driving simulators allow the testing of driving functions, vehicle models and acceptance assessment at an early stage. For a real driving experience, it's necessary that all immersions are depicted as realistically as possible. When driving manually, the perceived haptic steering wheel torque plays a key role in conveying a realistic steering feel. To ensure this, complex multi-body systems are used with numerous of parameters that are difficult to identify. Therefore, this study shows a method how to generate a realistic steering feel with a nonlinear open-loop model which only contains significant parameters, particularly the friction of the steering gear. This is suitable for the steering feel in the most driving on-center area. Measurements from test benches and real test drives with an Electric Power Steering (EPS) were used for the Identification and Validation of the model.

In pursuit of safety validation of automated driving functions, efforts are being made to accompany real world test drives by test drives in virtual environments. To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing. The signal propagation is modeled by a ray tracing approach simulating the interaction of light waves with the environment.

To investigate the rollover phenomena experienced by all-terrain vehicles (ATVs) during their motion caused by input from the road surface, a combined simulation using CarSim and Simulink has been employed to validate an active anti-rollover control strategy based on differential braking for ATVs, followed by vehicle testing. In the research process, a nonlinear three-degrees-of-freedom vehicle model has been developed. By utilizing a zero-moment point index as a rollover warning indicator, this approach could accurately detect the rollover status of the vehicle, particularly in scenarios involving low road adhesion on unpaved surfaces, which are characteristic of ATV operation. The differential braking, generating a roll moment by adjusting the amount of lateral force each braked tire can generate, was proved as an effective method to enhance rolling stability.

A road test on semi-trailers is carried out, and accelerations of some characteristic points on the braking system,axles,and truck body is measured,also brake pressure and noise around the support frame is acquired.The measured data was analyzed to determine the causes of the brake noise, and the mechanism of the noise of the drum brake of semi-trailers during low-speed braking was investigated. The following conclusions are obtained: (1) Brake noise of the drum brake of the semi-trailer at low-frequency is generated from vibrations of the brake shoes, axle, and body, and the vibration frequency is close to 2nd natural frequency of the axle. (2) Brake noise is generated from stick-slip motion between the brake shoes and the brake drum, where the relative motion between the brake drum and the brake shoes is changed alternately with sliding and sticking, resulting in sudden changes in acceleration and shock vibration.

A fully instrumented Tesla Model 3 was used to collect thousands of hours of real-world automated driving data, encompassing both Autopilot and Full Self-Driving modes. This comprehensive dataset included vehicle operational parameters from the data busses, capturing details such as powertrain performance, energy consumption, and the control of advanced driver assistance systems (ADAS). Additionally, interactions with the surrounding traffic were recorded using a perception kit developed in-house equipped with LIDAR and a 360-degree camera system. We collected the data as part of a larger program to assess energy-efficient driving behavior of production connected and automated vehicles. One important aspect of characterizing the test vehicle is predicting its car-following behavior. Using both uncontrolled on-road tests and dedicated tests with a lead car performing set speed maneuvers, we tuned conventional adaptive cruise control (ACC) equations to fit the vehicle’s behavior.

Autonomous Vehicles are being widely tested under diverse conditions with expectations that they will soon be a regular feature on roads. The development of Autonomous Vehicles has become an important policy in countries around the world, and the technologies developed by countries and car manufacturers are different, and at the same time to adapt to the road environment and traffic management facilities of different countries, so some countries have built self-driving test sites, and the test content is also different, so it is impossible to compare its relative difficulty. This study surveyed experts and scholars to develop a means of weighting the respective difficulty of various autonomous vehicle testing conditions based on the analytic hierarchy process and fuzzy analytic hierarchy process, applied to a sample of 33 sets of testing conditions based on road type, management actions and operational capabilities.

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.

Brake judder affects vehicle safety and comfort, making it a key area of research in brake NVH. Transfer path analysis is effective for analyzing and reducing brake judder. However, current studies mainly focus on passenger cars, with limited investigation into commercial vehicles. The complex chassis structures of commercial vehicles involve multiple transfer paths, resulting in extensive data and testing challenges. This hinders the analysis and suppression of brake judder using transfer path analysis. In this study, we propose a simulation-based method to investigate brake judder transfer paths in commercial vehicles. Firstly, road tests were conducted to investigate the brake judder of commercial vehicles. Time-domain analysis, order characteristics analysis, and transfer function analysis between components were performed.

This paper presents a torque distribution strategy for four-wheel independent drive electric vehicles (4WIDEVs) to achieve both handling stability and energy efficiency. The strategy is based on the dynamic adjustment of two optimization objectives. Firstly, a 2DOF vehicle model is employed to define the stability control objective for Direct Yaw moment Control (DYC). The upper-layer controller, designed using Linear Quadratic Regulator (LQR), is responsible for tracking the target yaw rate and target sideslip angle. Secondly, the lower-layer torque distribution strategy is established by optimizing the tire load rate and motor energy consumption for dynamic adjustment. To regulate the weights of the optimization targets, stability and energy efficiency allocation coefficient is introduced. Simulation results of double lane change and split μ road conditions are used to demonstrate the effectiveness of the proposed DYC controller.

By installing an automated mechanical transmission (AMT) on heavy-duty vehicles and developing a reasonable shift strategy, it can reduce driver fatigue and eliminate technical differences among drivers, improving vehicle performance. However, after detaching from the experience of good drivers, the current shifting strategy is limited to the vehicle state at the current moment, and cannot make predictive judgment of the road environment ahead, and problems such as cyclic shifting will occur due to insufficient power when driving on the ramp. To improve the adaptability of heavy-duty truck shift strategy to dynamic driving environments, this paper first analyzes the shortcomings of existing traditional heavy-duty truck shift strategies on slopes, and develops a comprehensive performance shift strategy incorporating slope factors. Based on this, forward-looking information is introduced to propose a predictive intelligent shift strategy that balances power and economy.

The pursuit of maintaining a zero-sideslip angle has long driven the development of four-wheel-steering (4WS) technology, enhancing vehicle directional performance, as supported by extensive studies. However, strict adherence to this principle often leads to excessive understeer characteristics before tire saturation limits are reached, resulting in counter-intuitive and uncomfortable steering maneuvers during turns with variable speeds. This research delves into the phenomenon encountered when a 4WS-equipped vehicle enters a curved path while simultaneously decelerating, necessitating a reduction in steering input to adapt to the increasing road curvature. To address this challenge, this paper presents a novel method for dynamically regulating the steady-state yaw rate of 4WS vehicles. This regulation aims to decrease the vehicle's sideslip angle and provide controlled understeer within predetermined limits.

The tire cornering stiffness plays a vital role in the functionality of vehicle dynamics control systems, particularly when it comes to stability and path tracking controllers. This parameter relies on various external variables such as the tire/ambient temperature, tire wear condition, the road surface state, etc. Ensuring a reliable estimation of the cornering stiffness value is crucial for control systems. This ensures that these systems can accurately compute actuator requests in a wide range of driving conditions. In this paper, a novel estimation method is introduced that relies solely on standard vehicle sensor data, including data such as steering wheel angles, longitudinal acceleration, lateral acceleration, yaw rate, and vehicle speed, among others. Initially, the vehicle's handling characteristics are deduced by estimating the understeer gradient.

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.

This paper proposes a thorough investigation of steady-state cornering equilibria for cars. Besides equilibria corresponding to normal driving behaviour - herein denoted as stable-normal turn, drifting is attracting increasing attention. When discussing drifting, it is typically assumed that yaw rate and steering angle have opposite signs, i.e. the driver is countersteering, and the rear axle is saturated. Interestingly, another unstable equilibrium is possible, herein referred to as unstable-normal turn. In this work, an attempt to give a comprehensive definition of drift is made. An inverse model is proposed to compute the driver inputs needed to perform a steady-state turn for a given radius and sideslip angle. The mathematical meaning of all equilibria is explored by linearizing the system and analyzing eigenvalues and eigenvectors of the resulting state matrices.

Complex chassis systems operate in various environments such as low-mu surfaces and highly dynamic maneuvers. The existing metrics for lateral motion hazard by Neukum [13] and Amberkar [17] have been developed and correlated to driver behavior against disturbances on straight line driving on a dry surface, but do not cover low-mu surfaces and dynamic driving scenarios which include both linear and nonlinear region of vehicle operation. As a result, an improved methodology for evaluating vehicle yaw dynamics is needed for safety analysis. Vehicle yaw dynamics safety analysis is a methodical evaluation of the overall vehicle controllability with respect to its yaw motion and change of handling characteristic.

The experimental control findings of increasing the handling performance so that the yaw motion of the vehicle is nimble and stable utilizing the upgraded rear wheel steering system equipped with dual-link actuators are shown in this work. In most automobiles, the steering axis is well defined in front suspension. However, unless the vehicle's rear suspension is a sort of double wishbone, the steering axis is not clearly defined in regular multi-link rear suspensions. As a result, most current automobiles have a suspension geometry feature in which the camber and toe angles change at the same time when the assist link is changed to steer the back wheels. To create lateral force from the rear tire while preserving maximum tire grip, the dual-link actuators control for modifying the strokes of suspension links must keep the camber angle constant and adjust only the toe angle.

Emissions and fuel economy certification testing for vehicles is carried out on a chassis dynamometer using standard test procedures. The vehicle coastdown method (SAE J2263) used to experimentally measure the road load of a vehicle for certification testing is a time-consuming procedure considering the high number of distinct variants of a vehicle family produced by an automaker today. Moreover, test-to-test repeatability is compromised by environmental conditions: wind, pressure, temperature, track surface condition, etc., while vehicle shape, driveline type, transmission type, etc. are some factors that lead to vehicle-to-vehicle variation. Controlled lab tests are employed to determine individual road load components: tire rolling resistance (SAE J2452), aerodynamic drag (wind tunnels), and driveline parasitic loss (dynamometer in a driveline friction measurement lab). These individual components are added to obtain a road load model to be applied on a chassis dynamometer.

The emergence of new electric vehicle (EV) corner concepts with in-wheel motors offers numerous opportunities to improve handling, comfort, and stability. This study investigates the potential of controlling the vehicle's corner positioning by changing wheel toe and camber angles. A high-fidelity simulation environment was used to evaluate the proposed solution. The effects of the placement of the corresponding actuators and the actuation point on the force required during cornering were investigated. The results demonstrate that the toe angle, compared to the camber angle, offers more effect for improving the vehicle dynamics. The developed direct yaw rate control with four toe actuators improves stability, has a positive effect on comfort, and contributes to the development of new active corner architectures for electric and automated vehicles.

In order to improve the trajectory tracking accuracy and yaw stability of vehicles under extreme conditions such as high speed and low adhesion, a coordinated control method of trajectory tracking and yaw stability is proposed based on four-wheel-independent-driving vehicles with four-wheel-steering. The hierarchical structure includes the trajectory tracking control layer, the lateral stability control decision layer, and the four-wheel angle and torque distribution layer. Firstly, the upper layer establishes a three-degree-of-freedom vehicle dynamics model as the controller prediction model, the front wheel steering controller is designed to realize the lateral path tracking based on adaptive model predictive control algorithm and the longitudinal speed controller is designed to realize the longitudinal speed tracking based on PID control algorithm.

In vehicle development, reducing noise is a major concern to ensure passenger comfort. As electric vehicles become more common and engine and vibration noises improve, the aerodynamic noise generated around the vehicle becomes relatively more noticeable. In particular, the fluctuating wind noise, which is affected by turbulence in the atmosphere, gusts of wind, and wake caused by the vehicle in front, can make passengers feel uncomfortable. However, the cause of the fluctuating wind noise has not been fully understood, and a solution has not yet been found. The reason for this is that fluctuating wind noise cannot be quantitatively evaluated using common noise evaluation methods such as FFT and STFT. In addition, previous studies have relied on road tests, which do not provide reproducible conditions due to changing atmospheric conditions. To address this issue, automobile manufacturers are developing devices to generate turbulence in wind tunnels.