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In order to improve the comprehensive protection for children with variable shapes and sizes, this paper conducted studies on the impact injury for obese children based on a 10-YO finite element model. Some specific geometrics on the body surface were firstly acquired by the combination of pediatric anthropometric database and generator of body (GEBOD). A Radial Basis Function (RBF) based mesh morphing technique was then used to modify the original standard size FE model using the obtained geometrics. The morphed FE model was validated based on the experimental data of frontal sled test and chest-abdomen impact test. The effects of obesity on injury performances were analyzed through simplified high-speed and low-speed crash simulations.

Surrogate model based design optimization has been widely adopted in automotive industry. Hybrid surrogate model with multiple component surrogates is considered to be a better choice when simulating highly non-linear responses in vehicle crashworthiness analysis. Currently, the number of component surrogates has to be decided before-hand when constructing of a hybrid surrogate model. This paper conducts a comparative study on the performances of three popular hybrid modeling methods including heuristic computation strategy, and two kinds of optimal weighted surrogates. The effects of samples size on the number of individual surrogates that should be included into the final hybrid surrogate models for crashworthiness responses are investigated. Different hybrid modeling techniques and multiple validation criteria are evaluated. Some observations and conclusions on the selection of component surrogates in hybrid surrogate modeling are given in the end.

In the field of automobile development, sufficient structure strength is the most basic objective to be accomplished. Typically, method of strength analysis could be divided into static strength and dynamic strength. Analysis of static strength constitutes the major part of the development, but the supplement of dynamic strength is also dispensable to assure structural integrity. This paper presents a methodology about analyzing the impact strength of body structure based on a Multi-body Dynamics (MBD) and Finite Element Analysis (FEA) combined method. Firstly, the full vehicle MBD model consists of Curved Regular Grid (CRG) road model, Flexible Ring Tire (FTire) model and dynamic deflection-force bump stop model was built in Adams/Car. Next, Damage Initiation and Evolution Model (DIEM) failure criteria was adopted to describe material failure behavior.

In this paper, a closed loop path planning and tracking control approach of collision avoidance for autonomous vehicle is proposed. The two-level model predictive control (MPC) is proposed for the path planning and tracking. The upper-level MPC is designed based on the simple vehicle kinematic model to calculate the collision-free trajectory and the potential field method is adopted to evaluate the collision risk and generate the cost function of the optimization problem. The lower-level MPC is the trajectory-tracking controller based on the vehicle dynamics model that calculates the desired control inputs. Finally the control inputs are distributed to steering wheel angle and motor torque via optimal control vectoring algorithm. Test cases are established on the Simulink/CarSim platform to evaluate the performance of the controller.

High density polyethylene (HDPE) is widely used in automotive industry applications. When a specimen made of HDPE tested under cyclic loading, the inelastic deformation causes heat generated within the material, resulting in a temperature rise. The specimen temperature would stabilize if heat transfer from specimen surface can balance with the heat generated. Otherwise, the temperature will continue to rise, leading to a thermo assist failure. It is shown in this study that both frequencies and stress levels contribute to the temperature rise. Under service conditions, most of the automotive components experience low cyclic load frequency much less than 1 Hz. However, the frequency is usually set to a higher constant number for different stress levels in current standard fatigue life tests.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Previous study suggested that female, thin, obese, and older occupants had a higher risk of death and serious injury in motor vehicle crashes. Human body finite element models were a valuable tool in the study of injury biomechanics. The mesh deformation method based on radial basis function(RBF) was an attractive alternative for morphing baseline model to target models. Generally, when a complex model contained many elements and nodes, it was impossible to use all surface nodes as landmarks in RBF interpolation process, due to its prohibitive computational cost. To improve the efficiency, the current technique was to averagely select a set of nodes as landmarks from all surface nodes. In fact, the location and the number of selected landmarks had an important effect on the accuracy of mesh deformation. Hence, how to select important nodes as landmarks was a significant issue. In the paper, an efficient peak point-selection RBF mesh deformation method was used to select landmarks.

The use of lightweight materials is one of the important means to reduce the quality of the vehicle, which involves the connection of dissimilar materials, such as the combination of lightweight materials and traditional steel materials. The riveting quality of self-piercing riveting (SPR) technology will directly affect the safety and durability of automobiles. Therefore, in the initial joint development process, the quality of self-piercing riveting should be inspected and classified to meet safety standards. Based on this, this paper divides the self-piercing riveting quality into riveting appearance quality and riveting section quality. Aiming at the appearance quality of riveting, the generation of cracks on the lower surface of riveting will seriously affect the riveting strength. The existing method of identifying cracks on the lower surface of riveting based on artificial vision has strong subjectivity, low efficiency and cannot be applied on a large scale.

Finite element (FE) models are commonly used for automotive crashworthiness design. However, even with increasing speed of computers, the FE-based simulation is still too time-consuming when simulating the complex dynamic process such as vehicle crashworthiness. To improve the computational efficiency, the response surface model, as the surrogate of FE model, has been widely used for crashworthiness optimization design. Before introducing the surrogate model into the design optimization, the surrogate should satisfy the accuracy requirements. However, the bias of surrogate model is introduced inevitably. Meanwhile, it is also very difficult to decide how many samples are needed when building the high fidelity surrogate model for the system with strong nonlinearity. In order to solve the aforementioned problems, the application of a kind of surrogate optimization method called Efficient Global Optimization (EGO) is proposed to conduct the crashworthiness design optimization.

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.

Accident statistics have shown that older and obese occupants are less adaptable to existing vehicle occupant restraint systems than ordinary middle-aged male occupants, and tend to have higher injury risk in vehicle crashes. However, the current research on injury mechanism of aging and obese occupants in vehicle frontal impacts is scarce. This paper focuses on the optimization design method of occupant restraint system parameters for specific body type characteristics. Three parameters, namely the force limit value of the force limiter in the seat belt, pretensioner preload of the seat belt and the proportionality coefficient of mass flow rate of the inflator were used for optimization. The objective was to minimize the injury risk probability subjected to constraints of occupant injury indicator values for various body regions as specified in US-NCAP frontal impact tests requirements.

Driving safety is an eternal theme of the transportation industry. In recent years, with the rapid growth of car ownership, traffic accidents have become more frequent, and the harm it brings to human society has become increasingly serious. In this context, car safety assisted driving technology has received widespread attention. As an effective means to reduce traffic accidents and reduce accident losses, it has become the research frontier in the field of traffic engineering and represents the trend of future vehicle development. However, there are still many technical problems that need to be solved. With the continuous development of computer vision technology, face detection technology has become more and more mature, and applications have become more and more extensive. This article will use the face detection technology to detect the driver's face, and then analyze the changes in driver's driving focus.

To obtain higher efficiency in analysis process, simplification methods for computer-aided engineering (CAE) models are required in engineering. Current model simplification methods can meet certain precision and efficiency requirement, but these methods mainly concentrate on model features while ignoring model mesh which is also critical to efficiency of the analysis process and preciseness of the results. To address such issues, an integrated mesh simplification and evaluation process is proposed in this paper. The mesh is simplified to fewer features (e.g. faces, edges, and vertices) through edge collapsing based on quadric error metric. Then curvatures and normal vectors which are the objects to be evaluated are extracted from the original and simplified models for comparison. To obtain accurate results, the geometric information of mesh nodes and elements are both considered in this evaluation process. The proposed method is implemented on a vehicle crash test.

Vehicle hood design is a typical multi-disciplinary task. The hood has to meet the demands of different attributes like safety, dynamics, statics, and NVH (Noise, Vibration, Harshness). Multi-disciplinary optimization (MDO) of vehicle hood at early design phase is an efficient way to support right design decision and avoid late-phase design changes. However, due to lacking in CAD models, it is difficult to realize MDO at early design phase. In this research, a new method of design and optimization is proposed to improve the design efficiency. Firstly, an implicit parametric hood model is built to flexibly change shape and size of hood structure, and generate FE models automatically. Secondly, four types of stiffness analysis, one type of modal analysis, together with pedestrian head impact analysis were established to describe multi-disciplinary concern of vehicle hood design.

The dynamic trajectory planning problem for automatic vehicles in complex traffic scenarios is investigated in this paper. A hierarchical motion planning framework is developed to complete the complex planning task. An improved dangerous potential field in the curvilinear coordinate system is constructed to describe the collision risk of automatic vehicles accurately instead of the discrete Gaussian convolution algorithm. At the same time, the driving comfort is also considered in order to generate an optimal, smooth, collision-free and feasible path in dynamics. The optimal path can be mapped into the Cartesian coordinate system simply and conveniently. Furthermore, a velocity profile considering practical vehicle dynamics is also presented to improve the safety and the comfort in driving. The effectiveness of the proposed dynamic trajectory planning is verified by numerical simulation for several typical traffic scenarios.

As pedestrian protection tests and evaluations have been officially incorporated into new C-NCAP, more stringent requirements have been placed on pedestrian protection performance. In this study, in order to reduce the injury of the vehicle front end structure to the pedestrian's lower extremity during the collision, the advanced pedestrian legform impactor (aPLI) model was used in conjunction with the finite element vehicle model for collision simulation based on the new C-NCAP legform test evaluation regulation. This paper selected the key components which have significant influences on the pedestrian's leg protection performance based on the CAE vehicle model, including front bumper, front-cover plate, upper impact pillar, impact beam and lower support plate, to form a simplified model and conducted parametric modeling based on it.

Computer Aided Engineering (CAE) models have proven themselves to be efficient surrogates of real-world systems in automotive industries and academia. To successfully integrate the CAE models into analysis process, model validation is necessarily required to assess the models’ predictive capabilities regarding their intended usage. In the context of model validation, quantitative comparison which considers specific measurements in real-world systems and corresponding simulations serves as a principal step in the assessment process. For applications such as side impact analysis, surface deformation is frequently regarded as a critical factor to be measured for the validation of CAE models. However, recent approaches for such application are commonly based on graphical comparison, while researches on the quantitative metric for surface-surface comparison are rarely found.

To advance vehicle lightweighting, chopped carbon fiber sheet molding compound (SMC) is identified as a promising material to replace metals. However, there are no effective tools and methods to predict the mechanical property of the chopped carbon fiber SMC due to the high complexity in microstructure features and the anisotropic properties. In this paper, a Representative Volume Element (RVE) approach is used to model the SMC microstructure. Two modeling methods, the Voronoi diagram-based method and the chip packing method, are developed to populate the RVE. The elastic moduli of the RVE are calculated and the two methods are compared with experimental tensile test conduct using Digital Image Correlation (DIC). Furthermore, the advantages and shortcomings of these two methods are discussed in terms of the required input information and the convenience of use in the integrated processing-microstructure-property analysis.

Chopped carbon fiber sheet molding compound (SMC) material is a promising material for mass-production lightweight vehicle components. However, the experimental characterization of SMC material property is a challenging task and needs to be further investigated. There now exist two ASTM standards (ASTM D7078/D7078M and ASTM D5379/D5379M) for characterizing the shear properties of composite materials. However, it is still not clear which standard is more suitable for SMC material characterization. In this work, a comparative study is conducted by performing two independent Digital Image Correlation (DIC) shear tests following the two standards, respectively. The results show that ASTM D5379/D5379M is not appropriate for testing SMC materials. Moreover, the failure mode of these samples indicates that the failure is caused by the additional moment raised by the improper design of the fixture.