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Vehicle weight reduction has become one of the most crucial problems in the automotive industry because that increasingly stringent regulatory requirements, such as fuel economy and environmental protection, must be met. The lightweight design needs to consider various vehicle attributes, including crashworthiness and stiffness. Therefore, in essence, the vehicle weight reduction is a typical Multidisciplinary Design Optimization problem. To improve the computational efficiency, meta-models have been widely used as the surrogate of FE model in the multidisciplinary optimization of large structures. However, these surrogate models introduce additional sources of uncertainties, such as model uncertainty, which may lead to the poor accuracy in prediction. In this paper, a method of corrected surrogate model based multidisciplinary design optimization under uncertainty is proposed to incorporate the uncertainties introduced by both meta-models and design variables.

This paper presents a design process with data mining technique and advanced optimization strategy. The proposed design method provides insights in three aspects. First, data mining technique is employed for analysis to identify key factors of design variables. Second, relationship between multiple types of size and shape design variables and performance responses can be analyzed. Last but not least, design preference can be initialized based on data analysis to provide priori guidance for the starting design points of optimization algorithm. An exhaust system design problem which largely contributes to the improvement of vehicular Noise, Vibration and Harshness (NVH) performance is employed for the illustration of the process. Two types of design parameters, structural variable (gauge of component) and layout variable (hanger location), are considered in the studied case.

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.

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.

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.

Collaborative optimization is an important design tool in complex vehicle system engineering. However, there are many problems yet to be resolved when applying the conventional collaborative optimization method in vehicle body weight reduction, such as convergence difficulties and low optimization efficiency. To solve these problems, a maximum incompatibility constrained collaborative optimization method is proposed in the paper. First, the 1-norm equality constraints expression of the system level is used to replace the traditional 2-norm inequality constraints. Then, a maximum incompatibility is selected from modified inequality constraints to improve optimization efficiency. Finally, an overall compatibility constraint is introduced to decrease the influence caused by the initial point. A mathematical example is used to verify the effectiveness and stability of the proposed method.

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.

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.

This paper proposed a two-section trajectory planning algorithm. In this trajectory planning, sigmoid function is adopted to fit two tangent arcs to meet limited parking spaces by reducing the radius of turning. Then the transverse preview model is established and the path tracking errors including distance error and angle error are estimated. The weight coefficient is considered to distribute the impact factor of traverse distance error or traverse angle error in the total error. The fuzzy controller is designed to track the two-section trajectory in autonomous intelligent parking system. The fuzzy controller is developed due to its real-time and robustness in the parking process. Traverse errors and its first-order derivative are selected as input variables and the outer wheel steering angle is selected as the output variable in fuzzy controller. They are also divided into seven fuzzy sets. Finally, forty rules are decided to achieve effective trajectory tracking.

Vehicle weight reduction has become one of the viable solutions to ever-growing energy and environmental crisis. In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affects the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. For the purpose of constructing and correcting the bias in RSMs, scheduling Design of Experiments (DOEs) must be conducted properly. This paper develops a method to arrange DOEs in order to build RSMs with high quality, considering the influence of input uncertainty.

Design optimization methods are widely used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges is to search for the optimal design in an efficient manner. For complex design and optimization problems such as automotive applications, optimization algorithms work better if the initial searching points are within or close to feasible domains. In this paper, the k-means clustering algorithm is exploited to identify sets of reduced feasible domains from the original design space. Within the reduced feasible domains, the optimal design can be obtained efficiently. A mathematical example and a vehicle body structure design problem are used to demonstrate the effectiveness of the proposed method.

This paper proposes a low-cost but indirect method for occupancy detection and occupant counting purpose in current and future automotive systems. It can serve as either a way to determine the number of occupants riding inside a car or a way to complement the other devices in determining the occupancy. The proposed method is useful for various mobility applications including car rental, fleet management, taxi, car sharing, occupancy in autonomous vehicles, etc. It utilizes existing on-board motion sensor measurements, such as those used in the vehicle stability control function, together with door open and closed status. The vehicle’s motion signature in response to an occupant’s boarding and alighting is first extracted from the motion sensors that measure the responses of the vehicle body. Then the weights of the occupants are estimated by fitting the vehicle responses with a transient vehicle dynamics model.

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.

According to driving scenarios, intelligent vehicle is mainly applied on urban driving, highway driving and close zone driving, etc. As one of the most valuable developments, automated highway driving has great progress. This paper focuses on automated highway driving decision, and considering decision efficiency and feasibility, a hierarchical motion planning algorithm based on dynamic programming was proposed, and simultaneously, road coordinate transformation methods were developed to deal with complex road conditions. At first, all transportation user states are transformed into straight road coordinate to simplify modeling and planning, then a set of candidate paths with Bezier form was developed and with the help of obstacles motion prediction, the feasible target paths with collision-free were remains, and via comparing vehicle performance for feasible path, the optimal driving trajectory was generated.

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.

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.

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.

A coupled magnetic-thermal model is established to study the reason for the damage of the starter motor, which belongs to the idling start-stop system of a city bus. A finite element model of the real starter motor is built, and the internal magnetic flux density nephogram and magnetic line distribution chart of the motor are attained by simulation. Then a model in module Transient Thermal of ANSYS is established to calculate the stator and rotor loss, the winding loss and the mechanical loss. Three kinds of losses are coupled to the thermal field as heat sources in two different conditions. The thermal field and the components’ temperature distribution in the starting process are obtained, which are finally compared with the already-burned motor of the city bus in reality to predict the damage. The analysis method proposed is verified to be accurate and reliable through comparing the actual structure with the simulation results.