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In partly autonomous driving such as level 2 or level 3 automatic driving from SAE international classification, the switching of the driving right between the human driver and the machine plays an important role in the driving process of vehicle [1]. In this paper, the data collected from vehicle states and the driving behavior of drivers is completed via a simulator and self-report questionnaires. A fuzzy analytic network process (Fuzzy-ANP) model is developed to evaluate the driving capability of the drivers in real time from vehicle states due to its direct inherent link to the change of the driving state of drivers Moreover, in this model, the idea of group decision and multi-index fusion is adopted. The questionnaire is required to identify the experimental results from the simulator. The results show that the proposed Fuzzy-ANP model can evaluate the driving capability of the participants in real time accurately.

The design optimization of vehicle body structure is addressed to reduce interior noise and improve customer satisfaction in this paper. The structural-acoustic model is developed by using finite element method. The frequency response of structural-acoustic system is computed by modal analysis method. The optimization problem is constructed to minimize the sound pressure level in the right ear of the driver. The sensitivity analysis is carried out to find the key panels to be optimized as design variables and improve the efficiency of optimization computation. Response Surface Method (RSM) is utilized to develop the surrogate model and optimize the vehicle Noise Vehicle and Harshness (NVH) behavior. A 9dB reduction of sound pressure level (SPL) in the right era of the driver is obtained through geometric optimization for panels. Furthermore, the topology optimization model is developed to search the optimal layout of constrained layer damping treatments in the front floor.

The design optimization of vehicle body structure is addressed to reduce interior noise and improve customer satisfaction in this paper. The structural-acoustic model is established and the response of sound pressure in frequency domain is obtained by using finite element method. The minimization of sound pressure near the driver's right ear depends on the geometry of vehicle body structure and the layout of damping treatments. The panel participation analysis is performed to find out the key panels as design variables and improve the efficiency of optimization computation. Response Surface Method (RSM) is utilized to optimize the vibro-acoustic properties of vehicle body structure instead of complex structural-acoustic coupling finite element model. Geometric optimization problem of panels is described and solved to minimize the interior noise in vehicle.

In order to measure the noise of auto shock absorbers, a test bench used to detect piston-rod vibration responses of shock absorbers and measuring analyzer named SANTS-I were developed. The vibration response data was detected by bench tests, which shows that there are high-frequency violent peaks on the sine curve of piston-rod oscillating with relative low frequency. In order to explain the interior work dynamic mechanism of shock absorbers, a schematic Micro-process Dynamic Model with 10 steps particularly divided extension and compression stroke in more detail, and dynamic differential equations for each step were presented and discussed. Furthermore, numerical simulation for the inner impacts interaction between piston and damping fluid of hydraulic shock absorber was realized by ADINA software, by the establishment of a gas-liquid two-phase finite element model.

The working principle of dual mass flywheel - radial spring (DMF-RS) type torsional vibration damper was analyzed, and the design method of natural torsional vibration characteristics control of DMF-RS type torsional damper for automotive powertrains was studied herein. Based on the multi-freedom lumped mass - torsional vibration spring analysis model of powertrain, the natural torsional vibration characteristics of the system with DMF-RS type torsional damper were analyzed, and compared with the clutch type torsional vibration damper, the effectiveness of DMF-RS type torsional damper on the torsional vibration control was verified.

This paper presents a road classifier based on the system response with consideration of the tire enveloping. The aim is to provide an easily applicable yet accurate road classification approach for automotive engineers. For this purpose, tire enveloping effect is firstly modeled based on the flexible roller contact (FRC) theory, then transfer functions between road input and commonly used suspension responses i.e. the sprung mass acceleration, unsprung mass acceleration, and rattle space, are calculated for a quarter vehicle model. The influence of parameter variations, vehicle velocity, and measurement noise on transfer functions are comprehensively analyzed to derive the most suitable system response thereafter. In addition, this paper proposes a vehicle speed correction mechanism to further improve the classification accuracy under complex driving conditions.

Due to the design of lightweight, high speed driveline system, the coupled bending and torsional vibration and rotordynamics must be considered to predict vibratory responses more realistically. In the current analysis, a lumped parameter model of the propeller shaft is developed with Timoshenko beam elements, which includes the effect of rotary inertia and shear deformation. The propeller shaft model is then coupled with a hypoid gear pair representation using the component mode synthesis approach. In the proposed formulation, the gyroscopic effect of both the gear and propeller shaft is considered. The simulation results show that the interaction between gear gyroscopic effect and propeller shaft bending flexibility has considerable influence on the gear dynamic mesh responses around bending resonances, whereas the torsional modes still dominate in the overall frequency spectrum.

Brake squeal is an instability issue with many parameters. This study attempts to assess the effect of thermal load on brake squeal behavior through finite element computation. The research can be divided into two parts. The first step is to analyze the thermal conditions of a brake assembly based on ANSYS Fluent. Modeling of transient temperature and thermal-structural analysis are then used in coupled thermal-mechanical analysis using complex eigenvalue methods in ANSYS Mechanical to determine the deformation and the stress established in both the disk and the pad. Thus, the influence of thermal load may be observed when using finite element methods for prediction of brake squeal propensity. A detailed finite element model of a commercial brake disc was developed and verified by experimental modal analysis and structure free-free modal analysis.

The prediction and control of gear vibration and noise has become very important in the design of a quiet, high-quality gearbox systems. The vibratory energy of the gear pair caused by transmission error excitation is transmitted structurally through shaft-bearing-housing assembly and radiates off from exterior housing surface. Most of the previous studies ignore the contribution of components flexibility to the transmission error (TE) and system dynamic responses. In this study, a system level model of axle system with hypoid gear pair is developed, aiming at investigating the effect of the elasticity of the shafts, bearings and housing on TE as well as the contribution of flexible bearings on the dynamic responses. The load distribution results and gear transmission errors are calculated and compared between different assumptions on the boundary conditions.

Noise-vibration-harshness (NVH) design optimization problems have become major concerns in the vehicle product development process. The Body-in-White (BIW) plays an important role in determining the dynamic characteristics of vehicle system during the concept design phase. Finite Element (FE) models are commonly used for vehicle design. However, even though the speed of computers has been increased a lot, the simulation of FE models is still too time-consuming due to the increase in model complexity. For complex systems, like vehicle body structures, the numerous design variables and constraints make the FE simulations based optimization design inefficient. This calls for the development of a systematic and efficient approach that can effectively perform optimization to further improve the NVH performance, while satisfying the stringent design constraints.

Automated driving is considered a key technology for reducing traffic accidents, improving road utilization, and enhancing transportation economy and thus has received extensive attention from academia and industry in recent years. Although recent improvements in artificial intelligence are beginning to be integrated into vehicles, current AD technology is still far from matching or exceeding the level of human driving ability. The key technologies that need to be developed include achieving a deep understanding and cognition of traffic scenarios and highly intelligent decision-making. Automated Vehicles, the Driving Brain, and Artificial Intelligenceaddresses brain-inspired driving and learning from the human brain's cognitive, thinking, reasoning, and memory abilities. This report presents a few unaddressed issues related to brain-inspired driving, including the cognitive mechanism, architecture implementation, scenario cognition, policy learning, testing, and validation.

Significant progress in modeling techniques has greatly enhanced the application of computer simulations in vehicle safety. However, the fine-meshed impact models are usually complex and take lots of computational resources and time to conduct design optimization. Hence, to develop effective methods to simplify the impact models without losing necessary accuracy is of significant meaning in vehicle crashworthiness analysis. Surface deformation is frequently regarded as a critical factor to be measured for validating the accuracy of CAE models. This paper proposes an integrated validation framework to evaluate the inconsistencies between the deformed surfaces of the original model and simplified model. The geometric features and curvature information of the deformed surfaces are firstly obtained from crash simulation. Then, the magnitude and shape discrepancy information are integrated into the validation framework as the surface comparison objects.

In real-world engineering problems, the method of model simplification is usually adopted to increase the simulation efficiency. Nevertheless, the obtained simulation results are commonly with low accuracy. To research the impact from model simplification on simulation results, a feature-based predictive method for simplified CAE model analysis is proposed in this paper. First, the point clouds are used to represent the features of simplified model. Then the features are quantified according to the factors of position for further analysis. A formulated predictive model is then established to evaluate the responses of interest for different models, which are specified by the employed simplification methods. The proposed method is demonstrated through an engineering case. The results suggest that the predictive model can facilitate the analysis procedure to reduce the cost in CAE analysis.