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The relationship between designing for both rigid fixed barrier (RFB) and vehicle-to-vehicle tests is a topical area of research. Specifically, vehicle-to-vehicle compatibility has been a topic of keen interest to many researchers, and the interplay between the two aspects of design is presently addressed. In this paper, the studied vehicles for potential vehicle-to-vehicle impacts included: sport utility vehicles (SUVs), Pickups (PUs), and passenger cars. The SUV/PU-to-Car frontal impact tests were compared to those obtained from vehicle-to-rigid fixed barrier frontal impacts. Acceleration pulses at the B-pillar/rocker as well as dash and cabin intrusions were monitored and compared. Additionally, the energy distributions in SUV/PU-to-Car crash tests were compared to those of single vehicle-to-RFB tests. It was concluded from the analysis that vehicle weight and front-end stiffness were not always the overriding factors dictating performance.

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.

UGV (Unmanned Ground Vehicle) is gaining increasing amounts of attention from both industry and academic communities in recent years. Local trajectory planning is one of the most important parts of designing a UGV. However, there has been little research into local trajectory planning and tracking, and current research has not considered the dynamic of the surrounding environment. Therefore, we propose a dynamic local trajectory planning and tracking method for UGV driving on the highway in this paper. The method proposed in this paper can make the UGV travel from the navigation starting point to the navigation end point without collision on both straight and curve road. The key technology for this method is trajectory planning, trajectory tracking and trajectory update signal generation. Trajectory planning algorithm calculates a reference trajectory satisfying the demands of safety, comfort and traffic efficiency.

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.

Vehicle weight reduction has become one of the essential research areas in the automotive industry. It is important to perform design optimization of Body-in-White (BIW) at the concept design phase so that to reduce the development cost and shorten the time-to-market in later stages. Finite Element (FE) models are commonly used for vehicle design. However, even with increasing speed of computers, the simulation of FE models is still too time-consuming due to the increased complexity of models. This calls for the development of a systematic and efficient approach that can effectively perform vehicle weight reduction, while satisfying the stringent safety regulations and constraints of development time and cost. In this paper, an efficient BIW weight reduction approach is proposed with consideration of complex safety and stiffness performances. A parametric BIW FE model is first constructed, followed by the building of surrogate models for the responses of interest.

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.

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.

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.

The biofidelity of the Ford Motor Company human body finite element (FE) model in side impact simulations was analyzed and evaluated following the procedures outlined in ISO technical report TR9790. This FE model, representing a 50th percentile adult male, was used to simulate the biomechanical impact tests described in ISO-TR9790. These laboratory tests were considered as suitable for assessing the lateral impact biofidelity of the head, neck, shoulder, thorax, abdomen, and pelvis of crash test dummies, subcomponent test devices, and math models that are used to represent a 50th percentile adult male. The simulated impact responses of the head, neck, shoulder, thorax, abdomen, and pelvis of the FE model were compared with the PMHS (Post Mortem Human Subject) data upon which the response requirements for side impact surrogates was based. An overall biofidelity rating of the human body FE model was determined using the ISO-TR9790 rating method.

In recent years, aimed to promote the improvement of China’s new energy vehicle market, a series of incentive policies issued by the Chinese government: including the new energy vehicle subsidy policy, the double credit policy, and the charging pile infrastructure subsidy.Relevant research on new energy vehicle industry is mainly ground on multi-stage game, this paper employs multi-agent games theory, and summarizes the multi-agent decision-making optimization method in differential game based on dynamic programming and reinforcement learning. Then, in the context of new energy vehicles, research and improve the industrial policy of new energy vehicles through this method.A multi-agent differential game decision-making optimization framework is proposed. Complex multi-agent differential game decisions can be solved using the dynamic programming solver or deep reinforcement learning solver in this framework. Case studies and some observations will be given in the end.

Simulation models based design has become the common practice in automotive product development. Before applying these models in practice, model validation needs to be conducted to assess the validity of the models by comparing model predictions with experimental observations. In the validation process, it is vital to develop appropriate validation metrics for intended applications. When dealing with multivariate systems, comparisons between model predictions and test data with multiple responses would lead to conflicting decisions. To address this issue, this paper proposed a Bayesian classifier based validation method. With the consideration of both error rate and confidence in hypothesis testing, Bayesian classifier is developed for decision making. The process of validation is implemented on a real-world vehicle design case. The results show the proposed method’s potential in practical application.

This study applies a detailed finite element model of the human body to simulate occupant knee impacts experienced in vehicular frontal crashes. The human body model includes detailed anatomical features of the head, neck, chest, thoracic and lumbar spine, abdomen, and lower and upper extremities. The material properties used in the model for each anatomic part of the human body were obtained from test data reported in the literature. The total human body model used in the current study has been previously validated in frontal and side impacts. Several cadaver knee impact tests representing occupants in a frontal impact condition were simulated using the previously validated human body model. Model impact responses in terms of force-time and acceleration-time histories were compared with test results. In addition, stress distributions of the patella, femur, and pelvis were reported for the simulated test conditions.

With the development of science and technology, breakthroughs have been made in the fields of intelligent algorithms, environmental perception, chip embedding, scene analysis, and multi-information fusion, which has prompted the wide attention of society, manufacturers and owners of autonomous vehicles. As one of the key issues in the research of autonomous vehicles, the research of vehicle lane change algorithm is of great significance to the safety of vehicle driving. This paper focuses on the conflict of interest between the lane-changing vehicle and the target lane vehicle in the fully autonomous driving environment, and proposes the method of coupling kinematics and game theory, so that when the vehicle is in the process of lane changing game, the lane-changing vehicle and the target lane vehicle can make decisions that are beneficial to the balance of interests of both sides.

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.

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.

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.

Light truck vehicles (LTVs), sport utility vehicles (SUVs), and vans collectively make up a growing segment of the total automotive fleet sales, particularly in the United States. The National Highway Traffic Safety Administration (NHTSA) has identified this trend and has increased the extent of its research in vehicle-to-vehicle compatibility. Additionally, vehicle compatibility concerns have also been emphasized by International Harmonization Research Activity (IHRA). Accordingly, with intention to further enhance road safety, research in the area of crash compatibility between cars and LTVs in different crash configurations is of significant importance. This paper describes a part of ongoing research at Ford Motor Company to further investigate the effect of compatibility in SUV/LTV-to-Car crashes.

Most traffic accidents are caused by the driving behavior of drivers, so the study of driving behavior plays an important role in traffic safety. In this paper, we propose a comprehensive system for evaluating driver driving behavior safety, including classification and prediction of driver driving styles, as well as comprehensive scoring of driving behaviors. The driving behavior dataset needs to be pre-processed first. After that, we extracted driving behaviors and indicators, and performed cluster analysis on the reconstructed data to obtain three styles: conservative, aggressive, and impatient. We introduced a BP neural network to predict driving styles and obtained a relatively high prediction accuracy. Finally, we calculated the combined subjective and objective weights of the five behaviors and combined them with the developed scoring function to evaluate the driving safety of all drivers with a comprehensive score.

Computer Aided Engineering (CAE) has become a vital tool for product development in automotive industry. Increasing computer models are developed to simulate vehicle crashworthiness, dynamic, and fuel efficiency. Before applying these models for product development, model validation needs to be conducted to assess the validity of the models. However, one of the key difficulties for model validation of dynamic systems is that most of the responses are functional responses, such as time history curves. This calls for the development of an objective metric which can evaluate the differences of both the time history and the key features, such as phase shift, magnitude, and slope between test and CAE curves. One of the promising metrics is Error Assessment of Response Time Histories (EARTH), which was recently developed. Three independent error measures that associated with physically meaningful characteristics (phase, magnitude, and slope) were proposed.

This paper describes the development and validation of a finite element model of the SID-IIs beta+-prototype dummy using a nonlinear explicit finite element code. The geometry of the SID-IIs dummy is modeled with shell and solid elements from digital scans. The material properties are derived from dynamic tests and the model validation is conducted on component, subassembly and full assembly levels. Component level validation of the head/neck, arm, ribs, and lumbar spine is presented. The model validation of the thorax and pelvis subassemblies as well as pendulum calibration tests (shoulder, thorax, abdomen, and pelvis) and rigid-wall sled tests of the fully assembled dummy mode is also presented. The model response compares favorably with experimental data and provides a reasonable level of confidence in the model biofidelity.