3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

One-step hydrothermal synthesis of Cu-SAPO-34/cordierite and its catalytic performance on NO_x removal from diesel vehicles

Cu-SAPO-34/cordierite catalysts were prepared via one-step hydrothermal synthesis method and their performances to remove NO x from the diesel vehicle exhaust were evaluated. The morphology, structure, Cu content and valence state were characterized by SEM, XRD, ICP and XPS, respectively. The experimental results show the active component Cu of the catalysts via in situ synthesis could significantly improve the selective catalytic reduction （SCR） activities of NOx and the optimal Cu content is in the range of 0.30%-0.40%（mass fraction）. No N 2 O is detected by gas chromatograph （GC） during the evaluation process, which implies that NOx is almost entirely converted to N2 over Cu-SAPO-34/cordierite catalyst. The conversion rate of NOx to N2 by NH3 over catalyst could almost be up to 100%in the temperature range of 300-670 ℃with a space velocity of 12000 h-1 and it is still more than 60% at 300-620 ℃ under 36000 h-1. The catalysts also show the good hydrothermal and chemical stability at the atmosphere with H 2 O.

CeO_2-ZrO_2-La_2O_3-Al_2O_3 composite oxide and its supported palladium catalyst for the treatment of exhaust of natural gas engined vehicles

Composite supports CeO2-ZrO2-Al2O3（CZA） and CeO2-ZrO2-Al2O3-La2O3（CZALa） were prepared by co-precipitation method. Palladium catalysts were prepared by impregnation and their purification ability for CH4, CO and NOx in the mixture gas simulated the exhaust from natural gas vehicles （NGVs） operated under stoichiometric condition was investigated. The effect of La2O3 on the physicochemical properties of supports and catalysts was characterized by various techniques. The characterizations with X-ray diffraction （XRD） and Raman spectroscopy revealed that the doping of La2O3 restrained effectively the sintering of crystallite particles, maintained the crystallite particles in nanoscale and stabilized the crystal phase after calcination at 1000 ℃. The results of N2-adsorption, H2-temperatnre-programmed reduction （H2-TPR） and oxygen storage capacity （OSC） measurements indicated that La2O3 improved the textural properties, reducibility and OSC of composite supports. Activity testing results showed that the catalysts exhibit excellent activities for the simultaneous removal of methane, CO and NOx in the simulated exhaust gas. The catalysts supported on CZALa showed remarkable thermal stability and catalytic activity for the three pollutants, especially for NOx. The prepared palladium catalysts have high ability to remove NOx, CH4 and CO, and they can be used as excellent catalysts for the purification of exhaust from NGVs operated under stoichiometric condition. The catalysts reported in this work also have significant potential in industrial application because of their high performance and low cost.

Development of vehicle-recognition method on water surfaces using LiDAR data:SPD^(2)(spherically stratified point projection with diameter and distance)

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.

Intelligent Layout Method of the Powerhouse for Tank & Armored Vehicles Based on 3-Dimensional Rectangular Packing Theory

Probes into a new and effective method in arranging the powerhouses of tank & armored vehicles. Theory and method of 3-dimensional rectangular packing are adapted to arrange effectively almost all the systems and components in the powerhouse of the vehicle, thus the study can be regarded as an attempt for the theory's engineering applications in the field of tank & armored vehicle design. It is proved that most parts of the solutions attained are reasonable, and some of the solutions are innovative.

3D modeling of Unmanned Aerial Vehicles Tilt Photogrammetry

Unmanned Aerial Vehicles(UAV)tilt photogrammetry technology can quickly acquire image data in a short time.This technology has been widely used in all walks of life with the rapid development in recent years especially in the rapid acquisition of high-resolution remote sensing images,because of its advantages of high efficiency,reliability,low cost and high precision.Fully using the UAV tilt photogrammetry technology,the construction image progress can be observed by stages,and the construction site can be reasonably and optimally arranged through three-dimensional modeling to create a civilized,safe and tidy construction environment.

Blast disruption using 3D grids/perforated plates for vehicle protection

In this work, blast disruption and mitigation using 3D grids/perforated plates were tested for underbelly and side protection of vehicles. Two vehicle simulants were used: a small-scale one for side vehicle protection assessment and a true-to-scale simulant for underbelly protection testing. The deformation of target plates was assessed. These were either unprotected or protected by three different types of disruptors. The first disruptor was made of a sandwich structure of two perforated plates filled with a thin aluminum structure allowing the air to pass through. The two other disruptors were made of pieces of cast metallic foam. Two different kinds of foams were used: one with large cells and the second one with small cells. Beforehand, the mitigation efficiency of the disruptors was evaluated using an explosivedriven shock tube(EDST). The experiments showed that blast disruption/mitigation by 3D grid/perforated plate structures was not suitable for vehicle side protection. However, 3D grids/perforated structures proved to be relatively effective for underbelly protection compared to an equivalent mass of steel.

3D Vehicle Detection Algorithm Based onMultimodal Decision-Level Fusion

3D vehicle detection based on LiDAR-camera fusion is becoming an emerging research topic in autonomous driving.The algorithm based on the Camera-LiDAR object candidate fusion method(CLOCs)is currently considered to be a more effective decision-level fusion algorithm,but it does not fully utilize the extracted features of 3D and 2D.Therefore,we proposed a 3D vehicle detection algorithm based onmultimodal decision-level fusion.First,project the anchor point of the 3D detection bounding box into the 2D image,calculate the distance between 2D and 3D anchor points,and use this distance as a new fusion feature to enhance the feature redundancy of the network.Subsequently,add an attention module:squeeze-and-excitation networks,weight each feature channel to enhance the important features of the network,and suppress useless features.The experimental results show that the mean average precision of the algorithm in the KITTI dataset is 82.96%,which outperforms previous state-ofthe-art multimodal fusion-based methods,and the average accuracy in the Easy,Moderate and Hard evaluation indicators reaches 88.96%,82.60%,and 77.31%,respectively,which are higher compared to the original CLOCs model by 1.02%,2.29%,and 0.41%,respectively.Compared with the original CLOCs algorithm,our algorithm has higher accuracy and better performance in 3D vehicle detection.

Review and Prospect of LM-3A Series Launch Vehicle As Space Express for BDS

The LM-3A series launch vehicle was used for all launch missions for the BeiDou Navigation Satellite System(BDS)project,including BDS-1,BDS-2,and BDS-3.So it is known as Space Express for BDS.During the 26 years’development period for the BDS project,a series of key technological breakthroughs with the LM-3 A series of launch vehicles were made,improving the launch capability of different payloads into GTO,IGTO and MTO,from sending one satellites into transfer orbit to sending two satellites into transfer orbit,to sending two satellites into target orbit directly.A total of 59 satellites in 44 launches were launched using the LM-3 A series launch vehicle for the BDS project,achieving 100%success.

V2I Based Environment Perception for Autonomous Vehicles at Intersections

In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges.V2I(Vehicle-to-Infrastructure)communication is a potential solution to enable cooperative intelligence of vehicles and roads.In this paper,the RGB-PVRCNN,an environment perception framework,is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology.This framework integrates vision feature based on PVRCNN.The normal distributions transform(NDT)point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection.The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.

Attacks and defences on intelligent connected vehicles:a survey

Intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity,which opens up new possibilities for different cyber-attacks,including in-vehicle attacks(e.g.,hijacking attacks)and vehicle-to-everything communicationattacks(e.g.,data theft).These problems are becoming increasingly serious with the development of 4G LTE and 5G communication technologies.Although many efforts are made to improve the resilience to cyber attacks,there are still many unsolved challenges.This paper first identifies some major security attacks on intelligent connected vehicles.Then,we investigate and summarize the available defences against these attacks and classify them into four categories:cryptography,network security,software vulnerability detection,and malware detection.Remaining challenges and future directions for preventing attacks on intelligent vehicle systems have been discussed as well.

Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm

To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast.

基于3m波长地铁钢轨焊缝接头不平顺的轮轨响应分析

相对于1 m弦测值的焊缝接头动力学评估与管理,基于3 m波长范围的焊缝接头不平顺轮轨响应分析对其服役状态评估与管理具有重要意义。首先,基于车辆-轨道垂向耦合动力学理论建立车辆-轨道垂向耦合模型;然后,借助基于一弦N点弦测法研制的钢轨短波不平顺测量小车,对地铁线路焊缝接头不平顺进行精确测量,并根据传统弦测理论得到1 m弦测值;接着,将实测3 m波长焊缝接头不平顺和1 m弦测值作为车辆-轨道垂向耦合模型的不平顺激励输入,对比3 m波长焊缝接头不平顺和1 m弦测值激扰下的轮轨响应;最后,研究了车速和焊缝位置对轮轨动力响应的影响。结果表明:相比1 m弦测值激扰下的轮轨动力响应结果,3 m波长焊缝接头不平顺激扰下的轮轨力幅值变化不明显,轮重减载率幅值增大11.52%,钢轨加速度幅值增大54.05%,轮对振动加速度幅值增大37.67%;车速提高会导致轮轨力和轮重减载率幅值增大;焊缝中心与轨枕间的距离增加,轮轨力幅值先增加后减小,当焊缝中心距离轨枕0.1 m时,轮轨力幅值最大。

基于无人机观测的不同污染天气O_(3)垂直分布特征

为了解不同污染天气下近地面O_(3)垂直分布及演变特征,利用无人机及机载环境气象设备于2022年8~9月在山西省大同(清洁天)、太原(污染天)、临汾(污染天转清洁天)进行了33次外场观测,获得了近地面500m内每日06:00、10:00、15:00和19:00O_(3)、总挥发性有机物(TVOCs)、二氧化氮(NO2)和气温、湿度垂直分布.结果表明:O_(3)浓度随高度增加而上升,但15:00主要表现为下降(太原、临汾)或均匀分布(大同);NO2和TVOCs浓度则均为随高度增加而下降.大同O_(3)垂直分布日变化为随高度垂直递增—均匀分布(或递增减弱)—垂直递增,太原、临汾主要为垂直递增—垂直递减—垂直递增.受不同高度区域输送影响,光化学反应期间(10:00~15:00)垂直方向O_(3)日变幅形成2种类型分别是高空—接地层O_(3)浓度同步增高型和接地层增高—高空基本稳定型.白天垂直扩散条件越好,午后污染峰值期间O_(3)垂直递减越显著.受夜间逆温层影响,边界层中上层储存的O_(3)会持续影响第二日O_(3)垂直分布及地面浓度.各观测城市近地面均为VOCs控制区,随着高度的上升逐渐转为共同控制区及NO_(x)控制区,观测期间O_(3)浓度越高VOCs控制区的高度也越高.

工程车辆3-RPS座椅悬架半主动滑模控制

工程车辆处于恶劣的工作环境中,垂向单维度的被动减振无法满足实际需求。为解决该问题,依据可实现三维减振的3-RPS并联机构座椅悬架,提出一种新型三维控制策略。基于“座椅-人体”三维振动数学模型,设计一种新的三维天棚控制模型,作为滑模控制的参考对象。设计滑模控制器,并根据Lyapunov稳定判据,对控制系统的稳定性进行分析判断。利用Simulink软件建立座椅-人体受振模型,对处于E、F、G级3种路面环境中,分别采用滑模控制以及被动控制模式的座椅的减振效果进行仿真对比分析。结果表明,相较于被动控制的座椅悬架,采用三维滑模控制的座椅悬架在垂直方向上的加速度下降率在38.88%至57.06%之间;俯仰方向上的角加速度的下降率在36.00%至54.89%之间;侧倾方向上的角加速度的下降率在49.58%至59.97%之间。且可有效降低垂直方向上的加速度峰值,对于冲击载荷减振性能良好。研究成果可为半主动多维减振座椅悬架控制策略的研究提供一定参考。

Development of A Three-Dimensional Guidance System for Long-Range Maneuvering of A Miniature Autonomous Underwater Vehicle

The present paper introduces a three-dimensional guidance system developed for a miniature Autonomous Underwater Vehicle（AUV）. The guidance system determines the best trajectory for the vehicle based on target behavior and vehicle capabilities. The dynamic model of this novel AUV is derived based on its special characteristics such as the horizontal posture and the independent diving mechanism. To design the guidance strategy, the main idea is to select the desired depth, presumed proportional to the horizontal distance of the AUV and the target. By connecting the two with a straight line, this strategy helps the AUV move in a trajectory sufficiently close to this line. The adjacency of the trajectory to the line leads to reasonably short travelling distances and avoids unsafe areas. Autopilots are designed using sliding mode controller. Two different engagement geometries are considered to evaluate the strategy＇s performance： stationary target and moving target. The simulation results show that the strategy can provide sufficiently fast and smooth trajectories in both target situations.

A Light-weight Deep Neural Network for Vehicle Detection in Complex Tunnel Environments

With the rapid development of social economy,transportation has become faster and more efficient.As an important part of goods transportation,the safe maintenance of tunnel highways has become particularly important.The maintenance of tunnel roads has become more difficult due to problems such as sealing,narrowness and lack of light.Currently,target detection methods are advantageous in detecting tunnel vehicles in a timely manner through monitoring.Therefore,in order to prevent vehicle misdetection and missed detection in this complex environment,we propose aYOLOv5-Vehicle model based on the YOLOv5 network.This model is improved in three ways.Firstly,The backbone network of YOLOv5 is replaced by the lightweight MobileNetV3 network to extract features,which reduces the number of model parameters;Next,all convolutions in the neck module are improved to the depth-wise separable convolutions to further reduce the number of model parameters and computation,and improve the detection speed of the model;Finally,to ensure the accuracy of the model,the CBAM attention mechanism is introduced to improve the detection accuracy and precision of the model.Experiments results demonstrate that the YOLOv5-Vehicle model can improve the accuracy.

基于Internet和3G的汽车远程诊断数据采集技术的研究

为解决传统汽车诊断资源共享受地理位置限制的问题,构建了汽车远程诊断系统,提出了一种基于Internet和3G的汽车远程诊断数据采集方法。根据数据传输流程,结合网络传输特性,设计了各个阶段的数据传输策略,实现了汽车远程诊断数据采集。试验结果表明:所提出的汽车远程诊断数据采集方法实时性高、数据传输准确,可为远程诊断提供有效的数据支持。

遗传算法和3-opt结合求解带有能力约束的VRP

描述了带有能力约束的车辆路径问题（ＶＲＰ），在预先不固定车辆数的情况下，把聚类和排序有机地结合起来，并用遗传算法和３ｏｐｔ算法相结合的混合算法对问题进行求解，实验结果表明算法获得的最好解、平均负荷率和计算成本都比较令人满意·

深圳地铁3号线横岗双层车辆段经济分析

研究目的:深圳地铁3号线横岗双层车辆段是国内第一个采用线路双层布置,并进行上盖物业综合开发的车辆段,最大限度地实现了城市土地资源的集约化利用。通过对横岗双层车辆段的建造成本和经济效益进行分析研究,论证地铁车辆段双层布置技术在经济上的合理性,为其它地铁车辆段建设及综合物业开发提供一种新思路。研究结论:(1)深圳地铁3号线横岗车辆段采用双层设计后,车辆段用地面积减少了35%,节约用地效益明显。(2)双层方案由于增加了白地开发的面积,提高了物业开发项目的整体容积率,本次分析其商业开发经济效益为平面车辆段上盖物业开发方案的2倍,经济效益明显。(3)横岗双层地铁车辆段的建设和物业开发理念具有较高的推广价值。