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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.

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.

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.

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.

Nowadays, the automobile industry is booming and the number of vehicles is proliferating while the road traffic environment is also deteriorating. Therefore, attention should be paid to the protection of vulnerable road users in traffic accidents, such as pedestrians. In order to reduce the pedestrians’ head injury in collision accidents, in this study, the vehicle engine hood which responds significantly to head injuries was taken as the design object, so as to put forward a new optimization design process. The parameters of the hood’s main components, manufacturing materials and structural scheme were considered to carry out simultaneous optimization from various aspects such as pedestrian protection and hood stiffness.

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.

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 together 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 and reinforcement learning based optimization, 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.

Intelligent driving is an important research direction in the field of artificial intelligence. The fourth industrial revolution represented by the Internet of things provides more prospects for the development of intelligent vehicles. Trajectory planning and tracking control is one of the key technologies of intelligent driving vehicle. This paper takes intelligent driving vehicle as the starting point and establishes a research method of intelligent vehicle trajectory planning based on particle swarm optimization, based on the vehicle kinematics and dynamics model, a model predictive control algorithm is built for trajectory tracking control, the simulation scene is built by Prescan, the vehicle dynamics parameters are set in Carsim, and then the joint simulation is carried out with Simulink.

In recent years, game theory and reinforcement learning have become very popular research fields in today's society. As the most strategic analysis and optimization research method, they can be used in the study of subsidy strategy of China's new energy automobile industry to solve the problems caused by the government's subsidy of new energy vehicles. This paper studies the evaluation methods and strategy optimization methods of government subsidy strategies in different situations, and applies them to the subsidy strategies and other strategy optimization problems of new energy vehicles in China. Firstly, based on game theory, this paper studies the evaluation method of government subsidy strategy in the case of “double reciprocity” and “one strong and one weak” by constructing the game process of “double reciprocity” enterprises and “one strong and one weak” enterprises.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

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.