Search Results

With economic development and the increasing number of vehicles in cities, urban transport systems have become an important issue in urban development. Efficient traffic signal control is a key part of achieving intelligent transport. Reinforcement learning methods show great potential in solving complex traffic signal control problems with multidimensional states and actions. Most of the existing work has applied reinforcement learning algorithms to intelligently control traffic signals. In this paper, we investigate the agent-based reinforcement learning approach for the intelligent control of ramp entrances and exits of urban arterial roads, and propose the Proximal Policy Optimization (PPO) algorithm for traffic signal control. We compare the method controlled by the improved PPO algorithm with the no-control method.

Self-piercing riveting (SPR) are one of most important joining approaches in lightweight vehicle design for Body-in-white (BIW) manufacturing. Numerical simulation of the riveting process could significantly boost design efficiency by reducing trial-and-error experiments. The traditional Finite Element Method (FEM) with element erosion is hard to capture the large plastic deformation and complex failure behaviors in the SPR process. The smoothed Particle Galerkin Method (SPG) is a genuine meshless method based on Galerkin's weak form, which uses a novel bond-based failure mechanism to keep the conservation of mass and momentum during the material failure process. This study utilizes a combined FEM and SPG approach to join Aluminum sheet 5754 using a full three-dimensional (3D) model in LS-DYNA/explicit.

This paper proposes a detection algorithm based on deep learning for Vulnerable Road Users such as pedestrians and cyclists, which is improved on the basis of YOLOv5 network model. (1) Aiming at the problems of low resolution and insufficient information for small targets, a multi-scale feature fusion method is adopted to integrate shallow features with deep features.

As a crucial part of the intelligent transportation system, traffic signal control will realize the boundary control of the traffic area, it will also lead to delays and excessive fuel consumption when the vehicle is driving at the intersection. To tackle this challenge, this research provides an optimized control framework based on reinforcement learning method and speed guidance strategy for the connected vehicle network. Prior to entering an intersection, vehicles are focused on in a specific speed guidance area, and important factors like uniform speed, acceleration, deceleration, and parking are optimized. Conclusion, derived from deep reinforcement learning algorithm, the summation of the length of the vehicle’s queue in front of the signal light and the sum of the number of brakes are used as the reward function, and the vehicle information at the intersection is collected in real time through the road detector on the road network.

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.

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.

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.

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.

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.

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.

Self-piercing rivets (SPR) are efficient and economical joining methods used in the manufacturing of lightweight automotive bodies. The finite element method (FEM) is a potentially effective way to assess the joining process of SPRs. However, uncertain parameters could lead to significant mismatches between the FEM predictions and physical tests. Thus, a sensitivity study on critical model parameters is important to guide the high-fidelity modeling of the SPR insertion process. In this paper, an axisymmetric FEM model is constructed to simulate the insertion process of the SPR using LS-DYNA/explicit. Then, several surrogate models are evaluated and trained using machine learning methods to represent the relations between selected inputs (e.g., material properties, interfacial frictions, and clamping force) and outputs (cross-section dimensions).

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.

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.

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.

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.

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.

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.