Explicit–Implicit Co-Simulation Techniques for Dynamic Responses of a Passenger Car on Arbitrary Road Surfaces

To study the durability of a passenger car, this work investigates numerical simulation techniques. The investigations are based on an explicit implicit approach in which substructure techniques are used to reduce the simulation time, allowing full vehicle dynamic analyses to be performed on a timescale that is dif cult or impossible with the conventional nite element model (FEM). The model used here includes all necessary nonlinearities in order to maintain accuracy. All key components of the car structure are modeled with deformable materials. Tire road interactions are modeled in the explicit package with contact-impact interfaces with arbitrary frictional and geometric properties. Key parameters of the responses of the car driven on six different kinds of test road surfaces are examined and compared with experimental values. It can be concluded that the explicit implicit co-simulation techniques used here are ef cient and accurate enough for engineering purposes. This paper also discusses the limitations of the proposed method and outlines possible improvements for future work.

Hierarchical Cooperative Control of Connected Vehicles:From Heterogeneous Parameters to Heterogeneous Structures

As one of the typical applications of connected vehicles(CVs),the vehicle platoon control technique has been proven to have the advantages of reducing emissions,improving traffic throughout and driving safety.In this paper,a unified hierarchical framework is designed for cooperative control of CVs with both heterogeneous model parameters and structures.By separating neighboring information interaction from local dynamics control,the proposed framework is designed to contain an upper-level observing layer and a lower-level tracking control layer,which helps address the heterogeneity in vehicle parameters and structures.Within the proposed framework,an observer is designed for following vehicles to observe the leading vehicle's states using neighboring communication,while a tracking controller is designed to track the observed leading vehicle using local feedback control.Closed-loop stability in the absence and presence of communication time delay is analyzed,and the observer is further extended to a finite time convergent one to address string stability under general communication topology.Numerical simulation and field experiment verify the effectiveness of the proposed method.

让我们共同面对挑战

纵观人类历史,工程科学技术推动了人类经济社会和人类文明的快速发展。21世纪,为了维护安全,提高生活质量,建设更美好的、可持续发展的未来,人类必须应对和解决粮食不足、资源短缺、能源紧张、环境污染、气候异常、人口膨胀、区域贫困、疾病流行和经济危机等一系列重大挑战。

Facing Challenges Together

From a wide view of human history,it is clear that engineering has continuously advanced the development of human civilization and society.At different developmental stages,humans are confronted with different problems that must be addressed with different key technologies.In the 21st century,humans have been confronted with critical problems such as starvation,resource scarcity,energy shortages,environmental pollution。

Letter from Editors-in-Chief

Engineering comes to you at last.As the Editors-in-Chief of Engineering,first of all,we wish to thank all the scholars at home and abroad for their eager responses and full support,and all those who have provided encouragement and contributions toward the emergence of this journal.

A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving

In mixed and dynamic traffic environments,accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles to accomplish reasonable behavioral decisions and guarantee driving safety.In this paper,we propose an integrated probabilistic architecture for long-term vehicle trajectory prediction,which consists of a driving inference model(DIM)and a trajectory prediction model(TPM).The DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesian network.The proposed DIM incorporates the basic traffic rules and multivariate vehicle motion information.To further improve the prediction accuracy and realize uncertainty estimation,we develop a Gaussian process-based TPM,considering both the short-term prediction results of the vehicle model and the driving motion characteristics.Afterward,the effectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving dataset under lane-changing scenarios.The superior performance on the task of long-term trajectory prediction is presented and verified by comparing with other advanced methods.

Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning

Ensuring the safety of pedestrians is essential and challenging when autonomous vehicles are involved.Classical pedestrian avoidance strategies cannot handle uncertainty,and learning-based methods lack performance guarantees.In this paper we propose a hybrid reinforcement learning(HRL)approach for autonomous vehicles to safely interact with pedestrians behaving uncertainly.The method integrates the rule-based strategy and reinforcement learning strategy.The confidence of both strategies is evaluated using the data recorded in the training process.Then we design an activation function to select the final policy with higher confidence.In this way,we can guarantee that the final policy performance is not worse than that of the rule-based policy.To demonstrate the effectiveness of the proposed method,we validate it in simulation using an accelerated testing technique to generate stochastic pedestrians.The results indicate that it increases the success rate for pedestrian avoidance to 98.8%,compared with 94.4%of the baseline method.

In-situ carbonizing of coal pitch on the surface of silica sphere as quasi-graphitized carbon stationary phase for liquid chromatography

A novel chromatography stationary phase with a quasi-graphitized carbon modified shell has been developed. Coal pitch was directly carbonized on the surface of porous silica with in-situ carbonization. The carbonized coal pitch coating exhibits some degree of graphitization with a 78 nm-thick layer on the surface of silica and a 0.5 nm-thick layer on the inner surface of the mesopores. Based on the special structure of the graphitized carbon coating, the novel stationary phase can provide multiple interactions such as hydrophobic interaction, π-π interaction and dipole-dipole interaction. The novel composite material exhibited unique separation selectivity and excellent separation efficiency for polar compounds, including imidazoles, nucleosides and pesticides. Besides, the packed column also exhibited great repeatability with the RSDs of the retention time of nucleosides between 0.07%-0.50%(n = 5). Finally, satisfied result was achieved in the separation of fullerenes on the new column, suggesting the great potential in the industrial-scale purification of fullerenes.

State of the Art on Deep Learning-enhanced Rendering Methods

Photorealistic rendering of the virtual world is an important and classic problem in the field of computer graphics.With the development of GPU hardware and continuous research on computer graphics,representing and rendering virtual scenes has become easier and more efficient.However,there are still unresolved challenges in efficiently rendering global illumination effects.At the same time,machine learning and computer vision provide real-world image analysis and synthesis methods,which can be exploited by computer graphics rendering pipelines.Deep learning-enhanced rendering combines techniques from deep learning and computer vision into the traditional graphics rendering pipeline to enhance existing rasterization or Monte Carlo integration renderers.This state-of-the-art report summarizes recent studies of deep learning-enhanced rendering in the computer graphics community.Specifically,we focus on works of renderers represented using neural networks,whether the scene is represented by neural networks or traditional scene files.These works are either for general scenes or specific scenes,which are differentiated by the need to retrain the network for new scenes.

AN IMPROVED MODAL ANALYSIS FOR THREE-DIMENSIONAL PROBLEMS USING FACE-BASED SMOOTHED FINITE ELEMENT METHOD

In this work, we further extended the face-based smoothed finite element method （FS-FEM） for modal analysis of three-dimensional solids using four-node tetrahedron elements. The FS-FEM is formulated based on the smoothed Calerkin weak form which employs smoothed strains obtained using the gradient smoothing operation on face-based smoothing domains. This strain smoothing operation can provide softening effect to the system stiffness and make the FSFEM provide more accurate eigenfrequency prediction than the FEM does. Numerical studies have verified this attractive property of FS-FEM as well as its ability and effectiveness on providing reliable eigenfrequency and eigenmode prediction in practical engineering application.

Design and experimental validation of event-triggered multi-vehicle cooperation in conflicting scenarios

Platoon control is widely studied for coordinating connected and automated vehicles(CAVs)on highways due to its potential for improving traffic throughput and road safety.Inspired by platoon control,the cooperation of multiple CAVs in conflicting scenarios can be greatly simplified by virtual platooning.Vehicle-to-vehicle communication is an essential ingredient in virtual platoon systems.Massive data transmission with limited communication resources incurs inevitable imperfections such as transmission delay and dropped packets.As a result,unnecessary transmission needs to be avoided to establish a reliable wireless network.To this end,an event-triggered robust control method is developed to reduce the use of communication resources while ensuring the stability of the virtual platoon system with time-varying uncertainty.The uniform boundedness,uniform ultimate boundedness,and string stability of the closed-loop system are analytically proved.As for the triggering condition,the uncertainty of the boundary information is considered,so that the threshold can be estimated more reasonably.Simulation and experimental results verify that the proposed method can greatly reduce data transmission while creating multi-vehicle cooperation.The threshold affects the tracking ability and communication burden,and hence an optimization framework for choosing the threshold is worth exploring in future research.

Vehicle roll stability control with active roll-resistant electro-hydraulic suspension

This study examines roll stability control for vehicles with an active roll-resistant electro-hydraulic suspension(RREHS)subsystem under steering maneuvers.First,we derive a vehicle model with four degrees of freedom and incorporates yaw and roll motions.Second,an optimal linear quadratic regulator controller is obtained in consideration of dynamic vehicle performance.Third,an RREHS subsystem with an electric servo-valve actuator is proposed,and the corresponding dynamic equations are obtained.Fourth,field experiments are conducted to validate the performance of the vehicle model under sine-wave and double-lane-change steering maneuvers.Finally,the effectiveness of the active RREHS is determined by examining vehicle responses under sine-wave and double-lane-change maneuvers.The enhancement in vehicle roll stability through the RREHS subsystem is also verified.

Preface for Robust and Certifiable Perception System for Intelligent Vehicle

The perception system is one of the most important parts of intelligent vehicles and has developed rapidly in recent years.Accuracy,efficiency,and robustness of perception algorithms like object detection and scene parsing have been greatly improved.However,developing a robust and certifiable perception system is still challenging.Most perception systems can only work in relatively simple traffic scenarios,such as highways or closed environments,and would possibly fail in extreme conditions with the presence of extreme amounts of outliers due to false detection or sensor malfunction.